THE CONSTRUCTION OF OPTIMAL PORTFOLIOS OF TRADITIONAL INVESTMENT AND ALTERNATIVE INVESTMENTS PDF Free Download
1 / 237/237
100%
THE CONSTRUCTION OF OPTIMAL PORTFOLIOS OF TRADITIONAL
INVESTMENT AND ALTERNATIVE INVESTMENTS
THE CONSTRUCTION OF OPTIMAL PORTFOLIOS OF TRADITIONAL INVESTMENT
AND ALTERNATIVE INVESTMENTS
A Thesis Presented to
The Graduate School of Bangkok University
In Partial Fulfillment
Of the Requirements for the Degree
Master of Business Administration
By
Thu Ta Naing
2023
This thesis has been approved by
the Graduate School
Bangkok University
Title : The Construction of Optimal Portfolios of Traditional Investment and
Alternative Investments
Author : Thu Ta Naing
Thesis Committee:
Chairman Dr. Nathee Naktnasukanjn
(External Representative)
Committee Assoc. Prof. Dr. Supachet Chansarn
(Thesis Advisor)
Committee Dr. Rapeesorn Fuangkasem
(Thesis Co-advisor)
Committee Assoc. Prof. Dr. Suthinan Pomsuwan
(Program Faculty Members)
iii
Thu Ta Naing, Master of Business Administration, April 2024,
Graduate School, Bangkok University,
The Construction of Optimal Portfolios of Traditional Investment and Alternative
Investments ( 218 pp. )
Advisor: Assoc. Prof. Supachet Chansarn, Ph.D.
ABSTRACT
This study investigates the factors of portfolio assets composition, weights of
assets allocation, and investment period factors on constructing the optimal portfolios.
Through the utilization of Markowitz’s Modern Portfolio Theory, the optimal
portfolios are discovered through the usage of official past secondary data sources, as
well as computer-aided linear problem-solving techniques. 5 assets, namely, SET
stocks, bonds, gold, real-estate and bitcoin, are considered in a variety of asset
combinations to construct the Efficient Frontier curve and optimal portfolio
combinations. According to the findings, the best optimum portfolio so far, is the 5-
assets combination of portfolio over the 10-year investment timeframe, with the
Sharpe ratio of 6.245, with assets weight allocations of 20.6% stocks, 24.4% bonds,
53.7% real-estate shares, and 1.3% bitcoin. Moreover, the two investment timeframes
(5-year period and 10-year period) are both considered and compared with each other
to give insights on best possible timeframe to consider. The result is a longer 10-year
timeframe, as the returns are more smoothed as well as exhibiting less risk and higher
expected return.
iv
Keywords: Modern Portfolio Theory, Portfolio Management, Efficient Frontier, Stock
Exchange of Thailand, Alternative Investments, Thailand Capital Markets, Bitcoin
v
ACKNOWLEDGEMENT
This thesis study will not be completed without the excellent suggestions,
support and advice from my Assoc. Prof. Supachet Chansarn, Ph.D., who is my main
advisor. In fact, I would also like to give thanks to my Assoc. Prof. Rapeesorn
Fuangkasem, my another advisor, who validates and checks the quality of this thesis
manuscript.
Furthermore, I also would like to show respect and gratitude to my parents;
Mr. Win Khaing Moe and Mrs. Ni Lar Aung, who encouraged me in all of my
pursuits.
In fact, I am also thankful to all the Instructors from MBA Program classes at
Bangkok University, who teach us practical insights in pursuing this MBA Degree, as
well as their cleverness on each of their respective subject matters.
Indeed, I am also very grateful to the respective organizations including Thai
Bond Markets Association, the Stock Exchange of Thailand (SET), and Bank of
Thailand (BOT) for their transparency and helpful gathering of our required data
sources for this thesis study. Without their support and publications, I would not be
able to continue this thesis study.
Moreover, I would like to thank my online mentor, Mr. Ryan O’Connell,
CFA, who demonstrates the construction of Efficient Frontier and Optimal Portfolios
Construction related to this thesis study.
In addition, I would also like to thank my MBA program director, Assoc. Prof.
Dr. Suthinan Pomsuwan, who guides me everything throughout the MBA Program as
well.
vi
Last but not the least, I would like to especially say a great thank you to my
customers, whom of them are essential to my training business (Artifica Lab Co., Ltd)
during the hard times in Myanmar. Without my customers’ support, I would not have
been here in Bangkok University, studying this MBA master’s degree.
vii
TABLE OF CONTENTS
ABSTRACT iii
ACKNOWLEDGEMENT v
LIST OF TABLES x
LIST OF FIGURES xii
CHAPTER 1: INTRODUCTION 1
1.1 Background and significance of the problem 1
1.2 Objectives of the study 3
1.3 Research problems of the study 4
1.4 Method of the study 4
1.5 Scope of the study 6
1.6 Independent and Dependent Variables 7
1.7 Benefits of the Research 8
1.8 Definition of terms 10
CHAPTER 2: LITERATURE REVIEW 18
2.1 Common Stocks 18
2.2 Fixed-Income Securities 26
2.3 Alternative Assets: Gold 33
2.4 Alternative Assets: Real-Estate 36
2.5 Alternative Assets: Cryptocurrency 38
2.6 Other Alternative Assets 42
2.7 Portfolio Management 43
Page
viii
TABLE OF CONTENTS (Continued)
CHAPTER 2: LITERATURE REVIEW (Continued)
2.8 Markowitz’s Modern Portfolio Theory 44
2.9 Capital Asset Pricing Model (CAPM) 54
2.10 Risk-Adjusted Rate of Return 56
2.11 Related Research 58
2.12 Research Gaps 67
2.13 Conceptual Framework of the Study 67
CHAPTER 3: METHODOLOGY 70
3.1 Data and sources 70
3.2 Analytical Methods 76
3.3 Research Hypothesis of this thesis study 94
3.4 Limitations of this thesis study 94
CHAPTER 4: EMPIRICAL RESULTS 96
4.1 Descriptive statistics 97
4.2 Correlation analysis 121
4.3 Efficient frontier and optimal portfolio: traditional investment
portfolio 126
4.4 Efficient frontier and optimal portfolio: alternative investment
portfolio with high-risk assets 133
4.5 Comparison of the Optimal Portfolio 176
4.6 Hypothesis conclusions 184
Page
ix
TABLE OF CONTENTS (Continued)
CHAPTER 5: DISCUSSION 192
5.1 Significant Findings 192
5.2 Research Discussion 201
5.3 Recommendations 205
5.4 Limitations of the Study 208
BIBLIOGRAPHY 209
BIODATA 218
Page
x
LIST OF TABLES
Table 2.1: SET50 Index vs SET Index performance as of 31st Oct 2023 24
Table 2.2: Fixed-income securities rating standards 28
Table 3.1: Data and sources of this study 71
Table 3.2: Optimal portfolios construction on 16 possible scenarios 84
Table 3.3: Sample of the efficient portfolios of 5-asset portfolios 87
Table 4.1: Descriptive Statistics of monthly return during 5-year period 97
Table 4.2: Descriptive Statistics of monthly return during 10-year period 98
Table 4.3: Monthly Rate of Return for 5-year duration 111
Table 4.4: Monthly Rate of Return for 10-year duration 115
Table 4.5: Correlation table for the selected total assets for 5-year duration 122
Table 4.6: Covariance table for the selected total assets for 5-year duration 122
Table 4.7: Correlation table for the selected total assets for 10-year duration 124
Table 4.8: Covariance table for the selected total assets for 10-year duration 124
Table 4.9: Performance results of optimal portfolios 177
Table 4.10: Portfolio types of this study 178
Table 4.11: Best optimum portfolio of all 16 total portfolios 180
Table 4.12: Best optimum portfolio of all 5-assets portfolios 180
Table 4.13: Best optimum portfolio of all 4-assets portfolios 181
Table 4.14: Best optimum portfolio of all 3-assets portfolios 182
Table 4.15: Best optimum portfolio of all 2-assets portfolios 182
Table 4.16: Best optimum portfolio within 5-year period 183
Page
xi
LIST OF TABLES (Continued)
Table 4.17: Best optimum portfolio within 10-year period 184
Table 4.18: Sharpe ratio results of 5-year period portfolios 186
Table 4.19: Sharpe ratio results of 5-year period portfolios 187
Table 4.20: Sharpe ratio results of 5-year period portfolios 188
Table 4.21: Sharpe ratio results of 10-year period portfolios 189
Table 5.1: Traditional portfolios’ weight allocation observations 193
Table 5.2: Alternative portfolio type-1 weight allocation observations 194
Table 5.3: Alternative portfolio type-2 weight allocation observations 195
Table 5.4: Alternative portfolio type-3 weight allocation observations 196
Table 5.5: Alternative portfolio type-4 weight allocation observations 197
Table 5.6: Alternative portfolio type-5 weight allocation observations 198
Table 5.7: Alternative portfolio type-6 weight allocation observations 198
Table 5.8: Alternative portfolio type-7 weight allocation observations 200
Page
xii
LIST OF FIGURES
Figure 2.1: Industry Groups of Thai SET Market 23
Figure 2.2: SET50 Index Performance over different time periods 24
Figure 2.3: Top 10 stocks included in SET50 index, as of October 2023 25
Figure 2.4: Degree of risk on major asset classes 27
Figure 2.5: Systematic risk and unsystematic risk 46
Figure 2.6: Efficient frontier parametric plot of a portfolio 49
Figure 2.7: the Capital Allocation Line and Efficient Frontier 51
Figure 2.8: the Capital Market Line (CML) and Efficient Frontier 54
Figure 2.9: Analysis of Top US Endowment Funds with Net Asset Value > USD 1
billion in 2016 64
Figure 2.10: Top US Endowment Funds in comparison with Traditional Portfolios 65
Figure 2.11: Percentage allocation on Alternatives, for 20-year annualized returns 65
Figure 2.12: Conceptual Framework Diagram of the thesis study 68
Figure 3.1: Construction of the Efficient Frontier curve using Excel Linear Solver
Package 86
Figure 3.2: A sample of Efficient Frontier curve constructed on 5-assets portfolio 88
Figure 3.3: Solution of the optimum portfolio with Excel Linear-Solver package 91
Figure 3.4: Allocation of the optimal portfolio on the Efficient Frontier 92
Figure 4.1: Monthly rate of return from SET50 index during 5-year period 99
Figure 4.2: Monthly price data from SET50 index during 5-year period 100
Figure 4.3: Monthly rate of return from SET50 index during 10-year period 100
Page
xiii
LIST OF FIGURES (Continued)
Figure 4.4: Monthly price data from SET50 index during 10-year period 101
Figure 4.5: Monthly rate of return from rating A- corporate bonds during 5-year
period 101
Figure 4.6: Monthly price data from rating A- corporate bonds during 5-year
period 102
Figure 4.7: Monthly rate of return data from rating A- corporate bonds during
10-year period 102
Figure 4.8: Monthly price data from rating A- corporate bonds during 10-year
period 103
Figure 4.9: Monthly rate of return from physical real-estate during 5-year
period 103
Figure 4.10: Monthly price data from physical real-estate during 5-year period 104
Figure 4.11: Monthly rate of return from physical real-estate during 10-year
period 104
Figure 4.12: Monthly price data from physical real-estate during 10-year period 105
Figure 4.13: Monthly rate of return from 96.5% gold during 5-year period 105
Figure 4.14: Monthly price data from 96.5% Gold during 5-year period 106
Figure 4.15: Monthly rate of return from 96.5% Gold during 10-year period 106
Figure 4.16: Monthly price data from 96.5% Gold during 10-year period 107
Figure 4.17: Monthly rate of return from real-estate funds (PF&REIT)
during 5-year period 107
Page
xiv
LIST OF FIGURES (Continued)
Figure 4.18: Monthly price data from real-estate funds (PF&REIT)
during 5-year period 108
Figure 4.19: Monthly rate of return from real-estate funds during
10-year period 108
Figure 4.20: Monthly price data from real-estate funds (PF&REIT) during
10-year period 109
Figure 4.21: Monthly rate of return from Bitcoin during 5-year period 109
Figure 4.22: Monthly price data from Bitcoin during 5-year period 110
Figure 4.23: Monthly rate of return from Bitcoin during 10-year period 110
Figure 4.24: Monthly price data from Bitcoin during 10-year period 111
Figure 4.25: Efficient frontier and highest expected return of traditional
portfolio 1 for 5-year duration 127
Figure 4.26: Efficient frontier and highest expected return of traditional
portfolio 1 for 10-year duration 128
Figure 4.27: Efficient frontier and lowest risk of traditional portfolio 2 for
5-year duration 129
Figure 4.28: Efficient frontier and lowest risk of traditional portfolio 2 for
10-year duration 130
Figure 4.29: Efficient frontier and highest risk-adjusted return of
traditional portfolio 3 for 5-year duration 131
Figure 4.30: Efficient frontier and highest risk-adjusted return of
traditional portfolio 3 for 10-year duration 132
Page
xv
LIST OF FIGURES (Continued)
Figure 4.31: Efficient frontier and highest return of 3-assets portfolio type-3
on 5-year duration 134
Figure 4.32: Efficient frontier and highest return of 3-assets portfolio type-3
on 10-year duration 135
Figure 4.33: Efficient frontier and lowest risk of 3-assets portfolio type-3
on 5-year duration 136
Figure 4.34: Efficient frontier and lowest risk of 3-assets portfolio type-3
on 10-year duration 137
Figure 4.35: Efficient frontier and highest risk-adjusted return of
3-assets portfolio type-3 on 5-year duration 138
Figure 4.36: Efficient frontier and highest risk-adjusted return of
3-assets portfolio type-3 on 10-year duration 139
Figure 4.37: Efficient frontier and highest return of 3-assets portfolio type-2
on 5-year duration 140
Figure 4.38: Efficient frontier and highest return of 3-assets portfolio type-2 on
10-year duration 141
Figure 4.39: Efficient frontier and lowest risk of 3-assets portfolio type-2
on 5-year duration 142
Figure 4.40: Efficient frontier and lowest risk of 3-assets portfolio type-2
on 10-year duration 143
Figure 4.41: Efficient frontier and highest risk-adjusted return of 3-assets portfolio
type-2 on 5-year duration 144
Page
xvi
LIST OF FIGURES (Continued)
Figure 4.42: Efficient frontier and highest risk-adjusted return of 3-assets portfolio
type-2 on 10-year duration 145
Figure 4.43: Efficient frontier and highest return of 3-assets portfolio type-1
on 5-year duration 146
Figure 4.44: Efficient frontier and highest return of 3-assets portfolio type-1
on 10-year duration 147
Figure 4.45: Efficient frontier and lowest risk of 3-assets portfolio type-1
on 5-year duration 148
Figure 4.46: Efficient frontier and lowest risk of 3-assets portfolio type-1
on 10-year duration 149
Figure 4.47: Efficient frontier and highest risk-adjusted return of
3-assets portfolio type-1 on 5-year duration 150
Figure 4.48: Efficient frontier and highest risk-adjusted return of
3-assets portfolio type-1 on 10-year duration 151
Figure 4.49: Efficient frontier and highest return of 4-assets portfolio
type-5 on 5-year duration 152
Figure 4.50: Efficient frontier and highest return of 4-assets portfolio
type-5 on 10-year duration 153
Figure 4.51: Efficient frontier and lowest risk of 4-assets portfolio
type-5 on 5-year duration 154
Figure 4.52: Efficient frontier and lowest risk of 4-assets portfolio
type-5 on 10-year duration 155
Page
xvii
LIST OF FIGURES (Continued)
Figure 4.53: Efficient frontier and highest risk-adjusted return of
4-assets portfolio type-5 on 5-year duration 156
Figure 4.54: Efficient frontier and highest risk-adjusted return of
4-assets portfolio type-5 on 10-year duration 157
Figure 4.55: Efficient frontier and highest return of 4-assets portfolio
type-4 on 5-year duration 158
Figure 4.56: Efficient frontier and highest return of 4-assets portfolio
type-4 on 10-year duration 159
Figure 4.57: Efficient frontier and lowest risk of 4-assets portfolio type-4
on 5-year duration 160
Figure 4.58: Efficient frontier and lowest risk of 4-assets portfolio type-4
on 5-year duration 161
Figure 4.59: Efficient frontier and highest risk-adjusted return of
4-assets portfolio type-4 on 5-year duration 162
Figure 4.60: Efficient frontier and highest risk-adjusted return of
4-assets portfolio type-4 on 10-year duration 163
Figure 4.61: Efficient frontier and highest return of 5-assets portfolio
type-7 on 5-year duration 164
Figure 4.62: Efficient frontier and highest return of 5-assets portfolio
type-7 on 10-year duration 165
Figure 4.63: Efficient frontier and lowest risk of 5-assets portfolio
type-7 on 5-year duration 166
Page
xviii
LIST OF FIGURES (Continued)
Figure 4.64: Efficient frontier and lowest risk of 5-assets portfolio
type-7 on 10-year duration 167
Figure 4.65: Efficient frontier and highest risk-adjusted return of
5-assets portfolio type-7 on 5-year duration 168
Figure 4.66: Efficient frontier and highest risk-adjusted return of
5-assets portfolio type-7 on 10-year duration 169
Figure 4.67: Efficient frontier and highest return of 5-assets portfolio
type-6 on 5-year duration 170
Figure 4.68: Efficient frontier and highest return of 5-assets portfolio
type-6 on 10-year duration 171
Figure 4.69: Efficient frontier and lowest risk of 5-assets portfolio
type-6 on 5-year duration 172
Figure 4.70: Efficient frontier and lowest risk of 5-assets portfolio
type-6 on 10-year duration 173
Figure 4.71: Efficient frontier and highest risk-adjusted return of
5-assets portfolio type-6 on 5-year duration 174
Figure 4.72: Efficient frontier and highest risk-adjusted return of
5-assets portfolio type-6 on 10-year duration 175
Page
1
CHAPTER 1
INTRODUCTION
1.1 Background and significance of the problem
Investment portfolios now play an important role in every society. Regarding
wealth accumulation, people cannot typically accumulate, increase, and maintain
wealth based on earned income only. No matter who we are or where we are from,
we, individuals as well as institutions in this society, conducted investment planning
since previous times before. In fact, the earned income, such as salary, business
profits, and other incomes need to be invested to achieve the long-term goals of
investors. Due to the low rate of return from investments in Money markets, such as
bank deposits, which typically offers only up to 1-2 percent of interest per annum.
Thus, the alternative solution and the need to invest in capital markets became a huge
concern for investors who are willing to secure their financial objectives in future.
However, investing in capital markets came along with risk and return. Unlike
the money markets, which are regarded as low risk, investing in capital markets
requires the need to accept the risks of each asset class. Capital markets include
everything from stocks, bonds, real-estate to complex financial instruments such as
derivatives, options, futures, and so on.
Currently, in this 21st century, together with the development of technology,
integrated capital markets, and the availability of real-time information, investors can
now easily analyze the capital markets, and invest accordingly according to their risk
tolerances and rewards. However, with the development of ever new financial
instruments such as derivatives, high-risk & volatile cryptocurrencies, and even NFTs
2
(Non-Fungible Tokens), the decision on whether to invest or not becomes a huge
concern for 21st century investors. In this thesis study, the author will conduct
extensive research findings on Thailand’s traditional investment assets (SET stocks,
government bonds & corporate bonds) as well as considering alternative investment
assets such as real-estate, gold, and cryptocurrencies.
According to (SET, 2024), SET, known as the “Stock Exchange of Thailand”
is the only Stock Exchange in Thailand, as well as the 2nd largest Stock Exchange in
ASEAN after Singapore. Thus, the author will target investor types of both local and
foreign on how they can construct an optimal investment portfolio, regarding their
preferred risk tolerances and expected returns.
Thus, another factor to consider is the importance of diversification, to
minimize risks for every investor they participate in investment decisions. Therefore,
the modern portfolio theory will be introduced, and applied in this thesis study to
minimize risk, while achieving the highest risk-adjusted expected returns. This theory
called “Modern Portfolio Approach”, is developed by Harry Markowitz in 1952. This
aims to ensure that investors could utilize our proposed investment portfolios that
could maximize their investment returns without taking unacceptable amounts of risk.
Contrary to traditional portfolios, in which during previous times, traditional
assets (i.e., stocks and bonds) are only invested, the new theory allows the investors to
consider risky alternative investments (i.e., real-estate, commodities, mutual funds,
and digital assets), most of which offer better investment returns than the traditional
ones.
Therefore, in this thesis study, the author will utilize both traditional and
alternative assets to construct the investment portfolios that would combine them and
3
provide the risk-adjusted returns for every risk limit they take.
Not only that, in fact, utilizing the modern portfolio approach will ensure that
all our investment portfolios are located on the efficient frontier. This will be
considered as the optimal portfolio.
In addition, this thesis study on optimal portfolios construction would give
valuable insights for foreign investors, who are interested on investing in Thailand
capital markets. This in turn, will increase the Thailand’s Foreign Direct Investment
while boosting the Thai economy.
Therefore, this research thesis study would be the most essential investment
insights and information for the ultimate benefits of Thailand as well as for the
investors who utilize this thesis research in good faith and ultimate benefit of our
society.
1.2 Objectives of the study
Regarding this thesis research study, there are 3 major objectives as follows:
1. To examine the optimal combinations of various assets, including common
stock, corporate bonds, real estate, gold, and cryptocurrencies, in various
investment portfolios which yield the highest risk-adjusted rate of return.
2. To examine the optimal weight allocation of each asset in different investment
portfolios which yield the highest risk-adjusted rate of return.
3. To compare the risk-adjusted rate of return from different investment
portfolios which comprise different combinations of assets, including common
4
stock, corporate bonds, real estate, gold, and cryptocurrencies.
1.3 Research problems of the study
Therefore, the research problems will be stated as follows:
1. What is the highest possible risk-adjusted rate of return from each investment
portfolio which is composed of different sets of assets, including common
stock, corporate bonds, real estate, gold, and cryptocurrencies.
2. What is the optimal weight allocation of each asset in different investment
portfolios which yield the highest risk-adjusted rate of return and what is the
highest possible risk-adjusted rate of return from each portfolio.
3. Which investment portfolio yields the highest risk-adjusted rate of return.
1.4 Method of the study
This thesis research focuses on optimal portfolio construction, which requires
extensive mathematical calculation & secondary data sources using the modern
portfolio approach.
Step 1: Five types of investment categories will be included in our portfolio: Thai
SET stocks, corporate bonds, real-estate, gold, and bitcoin. All data sources will be
secondary data. In addition, two possible types of real-estate assets will be considered
including: physical property index (Bank of Thailand) and real-estate investment
trusts funds (Stock Exchange of Thailand) indices.
5
Step 2: In fact, the modern portfolio theory, developed by Harry Markowitz, will be
utilized throughout this thesis research to develop, and construct the optimal
investment portfolios.
Step 3: Mathematical models of Efficient Frontier will be constructed using the
standard deviation and covariance.
Step 4: Therefore, assets will be placed on the efficient frontier according to the
expected return and expected risk taken, while the author will select and construct the
optimal portfolio, where the Capital Allocation Line (CAL) intersects.
Step 5: Portfolios’ Performance will be revealed based on 3 specific criteria: (1)
highest return, (2) lowest risk, and (3) highest risk-adjusted return. The findings will
be taken.
Step 6: The optimal portfolios (highest risk-adjusted return) will be measured by the
highest value of Sharpe ratio. The results will then be compared with other portfolio
types and the results of total findings will be taken with a Table chart.
Moreover, regarding the modern portfolio theory approach, this thesis
research study will reduce unsystematic risks as much as possible since it is the
investment risks that could be eliminated by diversification. However, the systematic
risks will not be reduced in this research study since it is caused by macroeconomic
factors and will not be possible for individual investors.
6
1.5 Scope of the study
The scope of the study will be defined as follows:
1. This study relies on monthly asset price data, retrieved from secondary
sources.
2. This study covers the period of a total of 10 years from 2013 to 2022, resulting
in a total of 120 months.
3. This study shall investigate five types of assets including common stocks,
corporate bonds, real-estate, gold, and cryptocurrencies.
4. In this study, common stocks and corporate bonds are considered as traditional
assets while real estate, gold and cryptocurrencies are considered as alternative
assets.
5. In this study, common stocks are represented by SET50 index, corporate
bonds are represented by Thai BMA corporate bond index, real estate is
represented by real estate price index and property fund and REIT index, gold
is represented by 96.5% gold 1 Baht price and cryptocurrency is represented
by Bitcoin price.
6. This study measures risk adjusted rate of return from portfolio by Sharpe ratio.
7. In this study, 10-Year Thai BMA government bond yield is considered as the
7
risk-free rate of return.
8. This study does not consider liquidity and rebalancing issues in constructing
investment portfolios.
9. The study does not consider macroeconomic or other external factors such as
political uncertainty, natural disaster, or geographical risk in constructing
investment portfolios.
10. This study employs Harry Markowitz’s Modern Portfolio Theory to construct
efficient frontiers and calculate the risk-adjusted rate of return on those
investment portfolios.
11. The optimal weight allocations findings using Excel linear solver tool will
constraint on those weight allocations be non-negative.
1.6 Independent and Dependent Variables
Independent Variables:
1.6.1. Portfolio Composition
Portfolio 1 = stock and bond
Portfolio 2 = stock and bond and physical real-estate
Portfolio 3 = stock and bond and real-estate funds
Portfolio 4 = stock and bond and gold
Portfolio 5 = stock and bond and gold and physical real-estate
8
Portfolio 6 = stock and bond and gold and real-estate funds
Portfolio 7 = stock and bond and gold and physical real-estate and
Bitcoin
Portfolio 8 = stock and bond and gold and real-estate funds and Bitcoin
1.6.2. Assets Allocation
Proportion of each asset type in the portfolios
1.6.3. Investment Periods
5-year period of investment
10-year period of investment
Dependent Variable:
1.6.4. Optimal Portfolios
Rate of return
Risk (Standard deviation)
Risk-adjusted rate of return (based on Sharpe ratio)
1.7 Benefits of the Research
The principal aim of this thesis research is to provide untapped investment
insights for investors, who want to invest in Thailand capital markets, with the
combination of inflation-hedged assets such as real-estate and other new digital assets,
example, Bitcoin. This would result in offering investors the maximum expected
return, while protecting against downside risks.
9
1. Investors: Individual investors will benefit from this study by taking this thesis
study insights as the basement and deciding whether to invest in certain assets of the
portfolio, and ideally, percent amount of optimal allocation to invest.
2. Mutual fund companies and investment advisors: Firms will benefit from this thesis
insights by developing various types of investment profiles and portfolios allocation
for certain types of investors.
3. Policy makers, such as SET: Policy makers should use this information on how
well the market efficiency is, and whether relying on capital markets will generate
long term benefits for investors, according to Markowitz’s theory. If not, policy
makers should adjust and modify the index constituents that will benefit in positive
upside returns.
Moreover, according to Bank of Thailand. (n.d.), the previous 25 years of
Tom-yum-kung 1997 financial crisis haunts very Thai investor, whether another
financial crisis could likely to happen after Covid-19 pandemic. Therefore, to hedge
those hyperinflation crises, alternative assets are considered to protect portfolio
capital value, as well as hedging the unexpected inflation rate.
Moreover, according to (SET, 2024), from 01 Jan 2023 – 05 Sept 2023, local
Thai investors involve 68.5 percent, while foreigners involve 31.5 percent of total
investments in Thailand SET markets. This indicates that Thailand SET market is still
a quite new untouched field for foreign investors, in which this thesis research study
will reveal how foreign investors could easily invest according to the research’s
findings, with the least risk and maximum expected return.
10
Therefore, based on this thesis research’s findings, investors (both local and
foreigners) can realize how much return & risk to expect in Thailand capital markets
& Thailand’s alternative investments, while improving liquidity in Thai SET markets,
which in turn results in improving Thailand economy.
Last, but not least, this thesis research will benefit to financial planners,
investment consultants, future research students, as the investment firms in Thailand,
who want to examine the optimal portfolios and risk-adjusted rate of return based on
the stated investment assets.
1.8 Definition of terms
1. Common stocks
Common stocks are stocks that represent a partial ownership in a particular
company and are the most popular option that investors buy. In fact, voting rights,
dividends benefits, and capital gains are highly benefited by investors on this type of
share.
2. Stock Exchange of Thailand (SET)
The Stock Exchange of Thailand, also known as the SET, is the only official
stock exchange of Thailand. It was founded on 30th April 19675, with the goal of
making the capital market “work” for everyone, from institutional firms to
individuals. Currently, the exchange serves in 2 markets: (1) SET and (2) mai (Market
for Alternative Investment), both of which are for public listed firms. However, the
SET market includes large enterprises, while mai includes small and medium
enterprises.
11
3. SET Index
The SET Index is a kind of composite index, which represents the overall
price movement for all common stocks trading on the SET Exchange. The
methodology is mainly based on market capitalization-weighted of the prices of all
common stocks listed on the SET market. However, the index doesn’t include stocks
that have not actively traded for more than three months.
4. SET50 Index
The SET50 Index is also similar to SET Index as stated before. However, the
SET50 Index only represents and reflects the overall price movement of the top 50
firms that are selected in terms of market capitalization, liquidity, as well as both the
trading value and turnover ratio.
5. Fixed income security
Fixed income security is essentially a debt instrument mainly issued by
governments, highly qualified corporations, and other high-ranking organizations, that
are designed to finance their short and long-term operations. In return, the issues offer
a form of fixed periodic payments as well as the principal return when the security
expires at maturity date.
6. Government bonds
Government bonds are also fixed-income securities, in which the issuer is the
national government. Due to its sovereignty and taxation power from the people,
12
government bonds typically have never been defaulted on, and thus regarded as the
safest form of fixed-income securities. However, the yields and returns are quite low
compared to other forms of debt securities. Governments typically issue these
securities to finance various infrastructure spendings and projects to stimulate
economic growth of their countries.
7. Corporate bonds
Corporate bonds are also another type of fixed-income securities, mainly
issued by large and highly qualified corporations such as banks and major firms. The
maturity term can be ranging from 1 to 30 years. Although they carry higher risk than
government bonds, corporate bonds typically offer a higher yield than government
bonds. Moreover, these types of bonds (corporate bonds) are typically sold over the
trustee, usually a third-party firm. To prevent the risk of default, covenants are usually
set between the investors and the bonds’ issuer so that the issues keep in line with the
stated bond contract details.
8. Thai BMA
The Thai Bond Market Association, also known as Thai BMA, is one of the
regulatory organizations, which is established under the Thailand Securities and
Exchange Act. In fact, the main objective is to develop the Thai bond market sectors
including both public and private. The organization offers numerous types of bond
indices including the rating A- up corporate bonds, which will be used as the risk-free
rate of return in this study.
13
9. Real-estate investment
Real-estate investments are one of the major investment assets that is often
included in many investment portfolios. Because of the distinct characteristics of real-
estate assets, investors have allocated significantly on the real-estate assets compared
with other asset classes. Two types of investment types can be observed: directly
through real-estate physical properties and indirectly, through the real-estate property
funds & real-estate investment trusts.
10. Property funds
Property funds are typically managed by asset management firms, which take
a fee structure by pooling the clients’ investments and invest in commercial properties
on behalf of their clients. The main types of commercial properties invested in include
factories, shopping retail malls, warehouses, office areas etc. The income of those
property funds is generated by the rental income of those commercial properties, as
well as the capital gains of those properties rising in the future.
11. Real estate investment trusts (REIT)
Real estate investment trusts are funds that are indirectly invested in income-
generating real-estate assets. Investors of REITs usually receive a regular interval of
rental income as dividends, through the trust funds, that professionally manage the
typical assets they invested. In fact, REITs are also traded like stocks, such that they
are typically traded in the current SET market, which makes them highly liquid. Most
of the REITs invest in commercial assets such as hotels, retail malls, warehouses,
apartments, cell towers, data centers, medical facilities, etc.
14
12. Gold
Gold is one of the rarest earth’s materials and is one of the investment popular
assets in Asia. In fact, Thailand currently holds the third-largest gold market, just
behind India and China, among the Asia continent. The standard purity of physical
gold used in Thailand is 96.5 percent purity, which is also known as the 23 karats. In
fact, the standard gold unit in Thailand is Baht. In this study, 96.5 percent purity 1
Baht gold price is considered.
13. Cryptocurrency
Cryptocurrency is a form of currency that only exists in digitalized form, and
especially utilizes the power of cryptography to secure the transactions. One of the
prominent characteristics of cryptocurrencies is that there is no central authority,
while a decentralized system, also known as the blockchain system is used to record
and regulate transactions globally. In fact, all the transactions are transparent such that
all records are done on a distributed public ledger, also known as the blockchain.
14. Bitcoin
Bitcoin is the first cryptocurrency ever to exist in this 21st century. Founded in
2009, by Satoshi Nakamoto, Bitcoin is currently regarded as the digital version of
gold and is currently used as the electronic peer-to-peer currency without the need for
centralized management. Low-trading fees, ease of use, & anonymity are such
benefits of Bitcoin.
15
15. Portfolio investment
Portfolio investment refers to ownership of various investment assets, with the
purpose of achieving a typical objective, whether it be short-term or long-term. Two
main types of investments can be greatly observed: (1) strategic investment, and (2)
tactical approach. Strategic investment is allocating funds to various assets for long-
term potential while the tactical approach involves active portfolio investing,
including buying, and selling for profit on short-term gains.
16. Portfolio management
Portfolio management is the process of decision making on investment assets
selection. This involves the use of modern portfolio theory approaches as well as
adjusting the strategy based on clients’ requirements. Active portfolio management
includes investing decisions to outperform the target benchmark. However, passive
portfolio management involves buying and holding for the investment horizon,
typically to beat the broad stock market index over time. Active management could
typically involve taking short-term positions (long & short), futures positions which
cost a substantial trading fee. However, passive management does not.
17. Diversification
Diversification refers to investing the total capital in different assets with
different proportions of capital. The idea is that portfolios with different assets will
perform better eventually over time than the portfolio with a single asset. However,
diversification is only achieved if those selected assets exhibit a low correlation with
each other, thereby limiting the downside risk of the total portfolio.
16
18. Efficient frontier
The Markowitz’s Efficient Frontier is a mathematical concept such that the
optimal combinations of those constructed portfolios will produce the highest return
for the selected amount of risk taken.
19. Rate of return
The rate of return is the final investment gain or loss over the investment
period, based on the initial investment amount expressed in terms of percentage value.
In the modern portfolio theory, the rate of return is considered to evaluate the total
portfolio’s return, when invested over the specific investment period.
20. Investment risk
The investment risk refers to the likelihood of losses occurrence relative to the
stated investment capital. In this study, Markowitz’s modern portfolio theory is
utilized, thus investment risk shall be measured in terms of standard deviation ( σ ). In
terms of portfolio investment, it tells investors how their portfolio returns values
deviate from the expected return mean. That is, the more standard deviation of a
particular asset, means the more volatile and riskier of the particular asset.
21. Risk-adjusted rate of return
Risk-adjusted rate of return refers to the measurement of potential returns on
the portfolio investment relative to the amount of risk taken. For example, if two
portfolios delivered the same return over the given time interval, the portfolio with the
17
lowest risk (standard deviation) will offer the better risk-adjusted rate of return. In this
study, Sharpe ratio is used to evaluate the risk-adjusted rate of return for the
portfolios. The ratio measures the portfolio’s returns that exceeds the risk-free rate per
unit of each risk taken (standard deviation). A higher Sharpe ratio indicates the better
investment portfolio.
22. Expected rate of return
The expected rate of return refers to the profit amount or loss that investors
could expect from the selected portfolio investment. However, it is still not a
guarantee of the total outcome, since the rate of return is just a forecasting concept
based on financial concepts and theories. In this study, the expected rate of return is
used to measure the portfolios’ future rate of return based on the evaluation of
previous past returns.
18
CHAPTER 2
LITERATURE REVIEW
This chapter will summarize and describe the relevant theories and literature
that will aid this thesis research study on achieving the thesis goals. Therefore,
regarding this thesis on constructing optimal investment portfolios, the following
factors “capital market investments”, “alternative investments”, “investment return
and risk (single asset vs multi-assets portfolio)”, “Capital Asset Pricing Model”,
“Markowitz’s modern portfolio theory”, “concepts of efficient frontier”, “risk
adjusted rate of return: Sharpe ratio” have been researched and stated. Further
literature review analysis will be stated in this section.
2.1 Common Stocks
2.1.1 Overview of Common Stocks
Common stocks are mostly issued by public firms to raise money for financing
their business operations as well as business expansion. In fact, common stocks are
also called ordinary stocks, with the voting polices of one vote per share owned
usually. In addition, owners of the common stocks are usually entitled to receive
dividends, share gains as well as the additional rights to votes within the company’s
decisions.
2.1.2 Returns and Risk from Common Stock Investment
Investing in common stocks usually benefits investors on long-term gains
since it provides a higher potential for long-term gains. In fact, they usually
19
outperform better than bonds and preferred shares. In fact, investing in common
stocks usually means representing the ownership of the firm invested. Moreover,
investors can still benefit from the gains of the share price appreciation as well as
dividends paid.
However, there are some specific risks of common shares. Shares’ value can
be decreased due to the firm’s mismanagement while there is no guarantee on
payments of dividends. If the firm doesn’t perform well, dividends shall not be paid to
investors as well. However, unlike common shares, preferred shares have regular
fixed schedules of dividends payments as well as having more priority to be paid first
in case of liquidation.
Moreover, investing in common stocks expose to the market risks (i.e., Thai
economy) while exposing to also specific risk (i.e., risk due to specific firms involved
in the portfolio selection. In addition, if there is too much positive correlation between
the selected stocks, it will reduce exposure to diversification.
In addition, liquidity / solvency risks also need to be considered such that the
firms involved will no longer be able to meet short-term financial debts: such as
interest payments, principal repayments, while including long-term obligations.
Failure to meet those obligations could lead to firms’ bankruptcy and could impact on
portfolios’ performance.
Moreover, operational risks are also still possible that involve typical mistakes
(i.e. human error, corruption, wrong expenditure, fraud) caused by employees of the
firm. This could lead to financial disasters and operational loss of the firms involved.
Another risk factor also is the possibility of downside risks. Due to exogenous
shocks, the falling stock prices could lead to a loss of value for the capital invested in
20
portfolios.
Other risk factors still possible include reputation risks due to bad
management, defective items, litigation cases, disputes with customers or suppliers
etc., that could impact on the firms’ image.
2.1.3 Stock Exchange of Thailand
According to (SET, 2024), the Stock Exchange of Thailand, is the official
stock market exchange of Thailand. Indeed, Thailand stock market exchange was
started since 1962, from the starting point of the Bangkok stock exchange. According
to its history, unfortunately, the Bangkok stock exchange failed at that time due to
limited attention and understanding of the equity markets by the Thai people.
However, starting from 1974, with the establishment and enactment of “The
Securities Exchange of Thailand Act”, to promote the savings to capital markets while
regulate the stock market trading. Nowadays according to (SET, 2024), there is an
average trading value of around THB 76.77 billion per day, which is considered
highest in ASEAN countries. in fact, the initial public offerings (IPOs) value reached
up to THB 127.84 billion, just in year 2022. This indicates that Thailand capital
markets are becoming an attractive source for newly investors from foreign
jurisdictions as well as to new local comers. Therefore, this research will offer
fundamental insights on how to invest on Thai equities as part of the optimal
portfolios’ construction.
2.1.4 SET Index
According to (S&P 500®, 2023), S&P 500 index is considered one of the most
21
popular indices to track the US stocks as well as the US economy. Indeed, it measures
the stock’s performance of the top 500 US largest firms.
Similarly, regarding on Thai equities, the numerous types of SET Index
represent the performance of the selected Thai public firms.
1. SET Index
The SET Index is a composite index that refers to the price movement of all
the common stocks available and trading on the SET exchange market. In fact, the
methodology is based on market capitalization-weighted price index.
2. SET50 Index
The SET50 Index refers to the top 50 listed public firms on SET, in terms of
market capitalization, liquidity and regulatory compliance. In fact, SET50 is also a
composite index, which includes a combination of different industrial business groups
in Thailand. Investing in the SET50 index covers the inclusion of the top 50 listed
firms and thus could be considered a representative of the major Thai economy.
However, a rebalancing is conducted every 6 months, for the selected equities in
SET50 Index. (SET, 2023)
3. SET100 Index
SET100 Index includes a broader range and includes the top 100 listed firms
on the Stock Exchange of Thailand. SET100 is also the composite index.
4. sSET Index
The sSET Index tracks the price volatilities of common stocks, that are not
22
included in the above SET50 and SET100. This is very useful for investors, to track
the minorities of stock price movements in the SET market. Indeed, sSET index could
be used as the benchmark index for stocks, excluding the top 100 listed public firms
(SET, 2023).
5. SETCLMV Index
The SETCLMV Index is designed to reflect the Thai companies that generate
revenue from other ASEAN Countries such as Laos, Myanmar, Cambodia, Vietnam
etc. Thus, it is only useful to track the revenue performance from other countries
(SET, 2024).
6. SETHD Index
The SET High Dividend 30 Index (SETHD) is designed for investors who
wish to invest in Thai securities that distributes high dividend yields. However, the
dividend yield for the selected securities is capped at 15 percent. Thus, it is only
suitable for investors who are interested in high-dividend yield stocks.
7. SETESG Index
This index is designed for positive ESG factors including Environmental,
Social and Corporate Governance. Investors who want to get good exposure to
positive ESG themes, and sustainable investment, should consider this index.
8. SETWEB Index
The “SET Well-Being Index” (SETWEB) is a market capitalization-weighted
23
price index, focusing on seven sectors of the Thai economy. The objective is to raise
and measure the living standards of local Thai population.
Therefore, among all other SET indices, the SET50 index would be chosen as
the price indicator for common stocks in this study as part of the thesis scope. Indeed,
being the composite index, as well as representative of the top 50 public firms, it will
serve as a major price indicator of common stocks of Thailand equities.
2.1.5 Industry Group Classification in SET
In this section, the industry group and sector classifications are stated as follows
(SET, 2024).
Figure 2.1: Industry Groups of Thai SET Market
24
2.1.6 MAI (Market for Alternative Investments)
The Market of Alternative Investments (MAI) is the stock market, specific for
small and medium enterprises. Indeed, the MAI index reflects the price movement of
all common stocks trading on the MAI market. Thus, it reflects the stocks of SMEs
and high risk, high-growth start-ups since the index can be considered riskier than
SET Markets.
2.1.7 SET Index and SET50 Index Comparison
According to table 2.1, SET50 index statistics are comparable to the SET
index. However, the SET50 covers 54 percent of top 10 holdings, than the SET index.
Table 2.1: SET50 vs SET performance as of 31 Oct 2023
SET50 Index
SET Index
Dividend Yield percent
2.96
3.38
P/E Ratio
19.58
19.21
P/BV Ratio
1.59
1.35
Total Market Cap (trillion THB)
11.66
16.97
Top 10 Holdings (percent Market Cap.)
54
37.14
According to (SET, 2023), the SET50 October 2023 report indicates that the
index performance over Year-by-Year graph as below.
Figure 2.2: SET50 Index Performance over different time periods
25
Therefore, the SET50 index could be an acceptable choice if considered for
longer term durations, especially the 10-year interval. However, when investing in
short-term intervals, the volatility is quite high, in which a 1-year interval resulted in
up to -12.55 percent total returns.
However, another possible factor is that negative total returns could be due to
macroeconomic events and external shocks (covid-19 pandemic, political situations),
in which Thai economy has just recovered since Year 2022.
Moreover, the SET50 index would give exposure to major economic sectors as
stated in the figure 2.3 below. Thus, the SET50 total return’s future performance
would highly depend on those sectors, unless major rebalancing is performed.
Figure 2.3: Top 10 Stocks included in SET50 Index, as of October 2023
26
2.2 Fixed-Income Securities
2.2.1 Overview of Fixed-Income Securities
Fixed-income securities are investment securities that typically offer a pre-
negotiated rate of return. Indeed, they are usually less risky than common stocks,
however, yields lower returns than the stocks. Thus, to increase diversification in a
typical investment portfolio, fixed-income securities are usually considered and
included as a key asset.
The most common types of fixed-income securities include bonds, treasury
bills, and commercial papers.
In fact, bonds offer a fixed-scheduled interest rate, mostly issued by
governments and highly qualified corporations. The maturity term can last from
months to several years.
However, treasury bills are usually issued by the governments, with an
objective of short-term only, typically one year or less. Similarly, commercial papers
are also issued with short-term maturity, despite being issued by the corporations.
2.2.2 Return and Risk from Fixed-Income Investment
Fixed-income securities are mainly raised by the governments, highly
qualified corporations, and other supranational organizations such as World Bank,
IMF, to raise their funds and finance their entities (RBC Wealth Management, 2023).
Therefore, the benefits are that fixed-income securities have a broader context
than common stocks. Indeed, government and corporate bonds, especially provide a
much lower correlation with other major assets including common stocks.
Moreover, according to figure 2.4 below, fixed-income securities such as
27
money market funds, fiduciary deposits, government bonds are considered as the
lowest risk category while stocks and commodities are considered as high-risk
category (Snopek, 2012).
Figure 2.4: Degree of risk on major asset classes
2.2.3 Fixed-Income Securities Rating
According to (ThaiBMA, 2023), the bond ratings of ThaiBMA are based on
standard ratings of S&P and Fitch. Thus, the ratings of fixed-income securities of
Thailand can be categorized as follows.
28
Table 2.2: Fixed-income securities ratings standards
Fitch S&P Moody’s Rating grade description
(Moody’s)
AAA AAA Aaa
Investment grade
Minimal credit risk
AA+ AA+ Aa1
Very low credit risk AA AA Aa2
AA- AA- Aa3
A+ A+ A1
Low credit risk A A A2
A- A- A3
BBB+ BBB+ Baa1
Moderate credit risk BBB BBB Baa2
BBB- BBB- Baa3
BB+ BB+ Ba1
Speculative grade
Substantial credit risk BB BB Ba2
BB- BB- Ba3
B+ B+ B1
High credit risk B B B2
B- B- B3
CCC+ CCC+ Caa1
Very high credit risk CCC CCC Caa2
CCC- CCC- Caa3
CC CC Ca
In or near default, with
possibility of recovery
C C
DDD SD C
In default, with little
chance of recovery
DD D
D
29
2.2.4 Thai BMA
The ThaiBMA also known as the Thai Bond Market Association, is a
securities business-related official association under the Securities and Exchange
Commission Act of Thailand. Being a self-regulatory organization, the main purpose
of the organization is to conduct a fair justice, and efficient operations of bond
markets of Thailand, as well as establishing market conventions, and bond pricing
operations.
2.2.5 Bond Price Index in ThaiBMA
Generally, the debt-securities (Thai bonds) are issued by one of the 3 issuers
as follows:
1. The Public Agency: which issues Treasury Bills and Government Bonds,
State-Owned,
2. Corporations: which issue Debentures, and Bill of Exchanges,
3. Foreign Institutions: which issue the Foreign Bonds.
Therefore, different types of indices are available to be used as price indicators
for bonds as follows (Thai BMA, 2023).
1. Thai BMA Bond Index
According to (Thai BMA, 2023), this index reflects all the Thai government
bonds, starting with the Symbol Name “LB”. This includes “1-to-3-year Maturities”,
“3-to-7-year Maturities”, “7-to-10-year Maturities and “above 10-year Maturities”.
Three types of prices are available: clean price, gross price, and total return
price, in which all can be used to track them.
30
However, the Government Bond yield for 3 year is 2.550336 percent, while 5
year is 2.630513 percent, and 10 year is 3.020763 percent.
2. Composite Bond Index
The Composite Bond Index is calculated from different weightings of
Government Bonds, State Enterprise Bond indices and Corporate Bond indices.
3. Zero Coupon Rate Return Index
The Zero Coupon Rate Return Index reflects the total returns from ThaiBDC’s
zero coupon bonds. In fact, since zero coupon bonds never pay interest. Therefore,
investors who prefer interest payments would not be a suitable choice for this
instrument. The returns are similar to Government Bond Yield.
4. T-Bill Index
The T-Bill Index reflects the total return of treasury bill in Thai capital
markets.
5. Commercial Paper Index
The Commercial Paper Index reflects the Short-Term Commercial Papers,
with 3 groups of Ratings: (AA- up, A- up, and BBB- up).
6. Short-term Government Bond Index
The Short-term Government Bond Index reflects the short-term debt
instruments issued by the Bank of Thailand (BOT) and the Ministry of Finance.
31
7. MTM (Mark to Market) Government Bond Index
The MTM Government Bond Index reflects only Government Loan Bonds
(LBs). 7 Groups are further classified to track, depending on the maturity terms.
8. MTM Corporate Bond Index
According to (Thai BMA, 2023), the MTM Corporate Bond Index return is
calculated from Mark-to-Market data of Corporate Bonds.
In fact, the corporate bonds are distinguished into 4 different corporate bond
indices based on their credit ratings:
1. (Rating A- and above),
2. (Rating BBB+ and above),
3. (Rating BBB and above), and
4. Rating (BBB- and above).
Moreover, based on different Time Maturities of Investment, 5 Groups are
further then classified on each bond rating group as follow:
1. Group 1 (Time-to-maturity is between 1 year and 3 year)
2. Group 2 (Time-to-maturity is between 3 year and 7 year)
3. Group 3 (Time-to-maturity is between 7 year and 10 year)
4. Group 4 (Time-to-maturity is less than 10 year)
5. Group 5 (Time-to-maturity is less than 1 year)
32
9. Fixed-Term Corporate Bond Index
The Fixed-Term Corporate Bond Index is calculated from corporate bonds,
which distribute fixed interest rates over the investment horizon. According to (Thai
BMA, 2023), the methodology is based on market weighting capitalization method,
and using the reference price from MTM Corporate Bond Index.
10. ESG Bond Index
According to (Thai BMA, 2023), the ESG Bond Index is the latest bond index,
constructed by the Thai BMA organization. Since investors nowadays are concerned
towards Environmental, Social and Corporate Governance factors, the need to
consider ESG factors is essential in fixed-income securities.
2.2.6 Clean Price, Gross Price, and Total Return Indices
Three types of Return Indices are available to be used from the MTM
Corporate Bond Index as the price indicator for this study. This includes:
1. Clean Price Index
The Clean Price Index does not consider accrued interest in the data values. In
fact, only the price of a coupon bond is considered in this index. Indeed, it is usually
the quoted price. Therefore, the Clean Price Index will ignore the accrued interest
pricing if selected as the price indicator of MTM Corporate bond.
2. Gross Price Index
Unlike Clean Price Index, choosing this Gross Price Index will now include
the effects of fundamental price, as well as the accrued interest. However,
33
withholding tax rates’ effects will not be included in this index type.
3. Total Return Index
The Total Return Index includes the withholding Tax rates of 15percent set by
the Government of Thailand as per updated. In fact, it will include both capital gains
as well as any dividends (if possible) or accrued interest from the corporate bonds.
Therefore, among three types of returns available, Total Return Index will be
used on the MTM Corporate Bonds.
2.2.7 Overview of Corporate Bond Index
Therefore, the MTM Corporate Bond Index (A- and above) will be chosen for
constructing optimal portfolios. This is because Corporate Bonds achieved higher
yields than the Government Bond yields.
Among three types of returns: Clean Price, Gross Return and Total Return; the
Total Return Price will be used to construct optimal portfolios. This is due to
consideration of capital gains, accrued interests, and withholding tax (15 percent)
inclusion.
2.3 Alternative Assets: Gold
2.3.1 Gold Price
Regarding commodities to be included in the portfolios, Gold will be
considered in this thesis study, due to the above stated thesis scope. Indeed, due to the
scope of this study, Thai 96.5% purity gold price will be chosen as a price indicator
for representing the performance of gold investment.
34
Indeed, according to (Bank of Thailand, 2024), Gold had been considered as
the safe-haven asset for the International Reserves. As per updated on 10th Nov 2023,
USD 15,205.31 million amount of Gold has been under International Reserves, by the
Bank of Thailand. In addition, physical gold assets are also considered safe-haven
assets in Asia, with Thailand being ranked as third-largest gold market, only after
India and China. Moreover, all imports and exports of Gold are considered VAT
(Value-Added Tax) exempt according to (Thailand Revenue Department, 2023).
Moreover, based on public beliefs and opinion, Thai investors still prefer to only
invest in Gold, as a safe-haven asset (Ajanapanya, 2022). Finally, gold has been
considered as God’s money, since years of centuries before, and thus will remain as a
safe-haven asset.
In fact, there are some significant differences between the global gold price
and the gold price in Thailand. In Thailand, 96.5 percent purity of gold is mainly
used, with the standard 1Baht equivalent unit for measuring the weight of the gold. In
fact, the labor price and operating costs are much lower than with the global price,
thus Thai gold is considered as cheaper than the global gold price. However, the
supply and demand of Thai gold is based on the economic factors since local people
tends to stock up physical gold often in times of uncertainties. This causes the Thai
gold price to appreciate in the short term.
2.3.2 Determination of Gold Price
Two types of price indicators are available for Thai gold: 96.5 percent purity
and 99.99% purity. However, most gold sold in Thailand is based on 96.5 percent
purity material and the daily selling price and buying price can be officially gathered
35
from the Thai Gold Traders Association.
Indeed, physical gold bars with purity of 96.5 percent are also readily
available and have high liquidity in the market. Despite the risk of theft, fire, natural
disasters, and loss of the precious metal, gold in Thailand remain the 3rd highest
demand in Southeast Asia (Ajanapanya, 2023).
In fact, for the gold price determination, only the selling price will be used
based on the reports of Gold Traders Association of Thailand, since investors will be
the buyers from gold shops in Thailand.
2.3.3 Return and Risk from Gold Investment
The inclusion of gold brings a significant return for the related portfolios of
this thesis study. Since gold is typically low correlated with traditional assets such as
stocks and bonds, the inclusion of gold will achieve a diversified portfolio.
Meanwhile, gold is considered as a safe-haven asset in times of global conflicts,
financial distress, and inflation-proof investment assets over the long-term horizon.
Also, the returns of gold are typically higher than the corporate bonds, while they
don’t exhibit significant risks than the stocks.
Regarding commodities such as gold, the cost of carry needs to be considered.
Convenience yield and storage costs become one of the risk concerns for gold.
Moreover, gold ETFs are also available which are exchange-traded funds, that offer
investors similar exposure to Gold, without owning the physical gold or having to
purchase directly. However, regarding this study, only Thai physical gold 96.5%
purity price will be used as the price indicator for this study.
36
2.4 Alternative Assets: Real-Estate
2.4.1 Overview of Real-Estate as Investment Assets
Real-estate assets are now becoming part of the major asset allocations in
modern portfolios (Krulický & Horák, 2019). Indeed, real-estate can yield high
returns, while being useful as a diversification tool and as a hedge against inflation.
However, investing in physical real-estate property requires highly specialized
knowledge and due diligence such that wrongful investment decisions could decrease
the portfolio investment value. However, the portfolio of pension and mutual funds
currently now include real-estate assets with target allocations ranging from 9 percent
to 10 percent (Kanoria & Muzaffar, 2017).
2.4.2 Direct Investment in Real Estate
Real-estate can be directly invested through ownership of physical properties.
According to (Snopek, 2012), investors could expect both capital gains, as well as
rental income from real-estate.
However, direct investing typically requires a substantial amount of
investment capital (typically, starting from a few million baht), with a long investment
horizon, as well as understanding the local property knowledge on the specific area
he/she invests.
The investment assets through direct investing could include:
1. Commercial places (i.e., shopping malls & office areas),
2. Residential places (i.e., condos, town halls etc.)
However, the right to invest/buy will depend on the local regulations set by the
Thai government. Also, low liquidity typically exhibits in real-estate.
37
In this study, to represent the price indicator of physical property, The Bank of
Thailand Property Index will be used to represent the overall price indicator for
physical property investment.
2.4.3 Indirect Investment in Real-Estate
Moreover, Real-estate can be indirectly invested through ownership of real-
estate fund shares such as property funds and real-estate investment trusts. With
indirect investing, it is now possible to invest from a few thousand Baht to unlimited,
that investors want to allocate in his/her portfolios. In addition, liquidity is typically
higher than physical properties, since property funds & real estate investment trust
funds are traded in the SET markets.
In fact, real-estate and stocks generally have weak correlation between each
other, providing diversification for the portfolios (Snopek, 2012). Research suggests
that regarding direct investing in real-estate, optimum share should be considered
between 15 - 20 percent of total portfolio, while about 5 percent should be allocated
for indirect investing in optimum portfolios (Snopek, 2012).
Moreover, in Thailand’s real-estate investment trusts, investors can enjoy
returns from a variety of real-estate assets including office building, retail centers,
warehouses, residential areas, and other commercial areas, if the businesses conducted
on the property are ethical and legally under the jurisdiction.
2.4.4 Return and Risk from Real-Estate Investment
Considering real-estate as investment assets brings substantial returns to
investors. Indeed, investing in real-estate assets actually hedge against inflation
38
(Rodnikorn, 2021).
Moreover, regarding the Thailand’s inflation, according to (Rodnikorn, 2021)
stated the main reasons for the inflation rate increased currently. They examined that
inflation is caused by the increased price of supply commodities such as food, oil and
gas. This is due to exogeneous shocks such as Covid-19 as well as constant natural
disasters & bad weather, occurring throughout the country. Another factor is that the
government increased cash supply as stimulus policies during Covid-19 pandemic, led
to price increase, i.e., inflation. Thus, real-estate assets will be considered as part of
inflation-hedged assets in our upcoming portfolios construction.
However, there are certain risks that real-estate investment exhibits as follows
to (Snopek, 2012). Since real-estate are physical assets (i.e., land, buildings, and
physical infrastructure), they possess physical risks such as physical damage, Act of
God (i.e., unexpected disasters), as well as highly maintenance costs, and unregulated
market. Moreover, unless indirect investing is involved, investing in physical
properties carries a liquidity risk. In fact, due to supply & demand factors, changing
governmental regulations and tax incentives, there is no exact guarantee of real estate
price rigidity. The value of invested property could fluctuate depending on changing
market conditions. Finally, investors who invest in property with mortgages will have
to face possible capital gains / losses due to interest rate movements.
2.5 Alternative Assets: Cryptocurrency
2.5.1 Overview of Cryptocurrency
Cryptocurrency is a form of currency in a digitalized form, in which their
mechanisms are based on cryptography. Due to its decentralized nature,
39
cryptocurrencies have a distinct form of currency, which are very different from the
traditional fiat currencies such as USD, British Pound, Swiss Francs etc.
In fact, cryptocurrency utilizes a distributed ledger called the blockchain, in
which every transaction of cryptocurrency can be explored publicly. Meanwhile, the
ledger transactions cannot be altered or modified by others since approvals of every
transaction need to be confirmed by a majority of blockchain holders. Thus, they
could now provide as a storage of value, offer peer-to-peer transactions as well as
hard to fake, which all possess as the characteristics of a typical currency.
2.5.2 Determination of Cryptocurrency Price
According to (Horstmeyer, 2022), cryptocurrencies are the type of assets
popular in the 21st century, that can be electronically created, modified, stored, and
transferred.
One of the reasons to consider cryptocurrencies in future portfolio investments
is the emerging factor of decentralization technology in banking and finance sector
nowadays. This could provide a future-proof investment strategy for the optimal
portfolios.
However, unlike other assets, there is no specific fundamental valuation on
determination of cryptocurrency price. In fact, the prices are mainly determined by the
supply and demand, its current utility and expected future utility, as well as how well
the competition with other types of cryptocurrencies, its availability and popularity
among the investors’ community.
Indeed, the supply of cryptocurrencies are often limited in nature, while some
cryptocurrencies often conduct a strategy called “burning” to reduce the supplies of
40
those cryptocurrencies. Thus, these are the catalysts to drive the price up, giving
substantial returns to the investors. Moreover, the utility of cryptocurrency is an
important fact such that most of the cryptocurrencies possess a road-map plan with
real-world issues that their teams are trying to solve.
Moreover, the nature of cryptocurrencies is highly different than the
traditional financial assets, in a way that traditional analysis methods could not
applied on Bitcoin. This is because, since the value of cryptocurrencies are not backed
by other underlying assets, or generate cashflows (e.g., dividends, interest payments),
they could be considered as digital commodities to some extent. In fact, mostly, the
value of digital assets is mainly based on its technological development, scarcity as
well as the ability to transfer value, without intermediaries, in the future.
2.5.3 Major Cryptocurrencies
There are numerous types of cryptocurrencies currently circulating in the
cryptocurrency market exchange. One of the first cryptocurrencies is Bitcoin,
developed by Satoshi Nakamoto in 2009. According to (Nakamoto, 2009), bitcoin is a
pure P2P (Peer-to-Peer) electronic cash that could transfer directly from one person to
another person, without the need for intermediaries, typically financial institutions
(i.e., banks). Therefore, since 2009, contrary to other financial transaction systems,
Bitcoin proved as the new innovative and disruptive way in financial transactions.
Besides Bitcoin, there are also other alternative cryptocurrencies which are
often termed as Alternative Coins, also known as Altcoins. These include Ethereum,
Cardano, Ripple, Solana, Polkadot etc., and so on. In fact, while Bitcoin is regarded as
a store of value, Ethereum is regarded as a smart contract blockchain, in which
41
decentralized applications can be built on it.
However, in this study, only bitcoin will be used as price indicator of
cryptocurrency. Moreover, Bitcoin proved to be the top 1 highest return since the
inception of 2009. In fact, the distinctive part is the nominations of supply and
demand. Unlike fiat cash, in which the M1, M2, M3 Money supply could be increased
or decreased according to the government’s monetary policies, the supply of Bitcoin
is fixed at 19,543,818 Bitcoin; approximately 19.54 million Bitcoin and cannot be
increased by any means. This proved, as the digital version of Gold, for Investors,
who want to get exposure to newly disruptive financial technologies like Bitcoin.
According to (Sriram, 2021), Bitcoin has been achieved as the best performing
asset of the previous 10 years, even surpassing 10x (10 times) than the Nasdaq 100
index. As per statistics, the ROI (Return of Investment) on Bitcoin would be exceeded
20,000,000 percent, if per invested since 2011.
This proves that the need to consider digital assets in the diversified portfolios
is essential that it will provide higher expected returns than any other assets would.
Nevertheless, they are considered highly risky and highly volatile, and caution should
be taken.
Unlike other types of assets available, the allocation of Bitcoin could be
considered as a highly volatile asset, which could result in losing 100 percent of
investors’ capital on Bitcoin’s investment. Also, investors are also prone to attack by
cyber-attacks, scams, fraud, and operational errors from crypto investing. Therefore,
investors must consider carefully and be aware of the risks & take necessary
precautions on safety of the Bitcoin assets in the investment portfolios.
42
2.6 Other Alternative Assets
2.6.1 Commodities
There are also other types of commodities that can be allocated in portfolios.
Indeed, other commodities include energy and metals which are considered as hard
commodities as well as livestock, and agriculture, which are considered as soft
commodities.
2.6.2 Private equities
Moreover, private equities are also alternative investment assets that focus on
investing in private companies, as well as providing additional expertise, and other
assistance support. The invested firms include venture capitals (i.e., startup companies
& seed-stage firms), as well as investing in private firms of maturity stage, and
conducting buyouts (i.e., acquisitions from other firms).
2.6.3 Collectibles and Antiques
According to (De Souza, 2023), rare collectibles have investment values that
appreciate over time. These types of collectives usually include rare stamps, vintage
wines, classic cars, fine jewelry, and limited-edition watches etc. that appreciate value
over time.
However, due to complexity of the asset valuation nature, as well as due to its
uncertain nature, highly illiquid, and typically sold over Auctions, this type of asset is
not considered in this thesis research study.
43
2.6.4 Hedge Funds
Hedge Funds typically utilize active management strategies, and sometimes
even risky strategies to beat the targeted benchmark returns. In fact, typical strategies
of hedge funds include leverage, the use of derivatives, shorting assets, as well as
other strategies prescribed by those investment firms.
2.7 Portfolio Management
2.7.1 Definition of Portfolio Investment
Portfolio investment refers to the investment and ownership of stocks, bonds,
and other assets with a total expectation that the total return of those assets will
generate a risk-adjusted return and grow in capital value over time. Indeed, it refers
mainly to passive ownership, without active management role on investment of assets.
Portfolio investment can include two types: strategic investment, which refers
to investing in financial assets for long-term gains, as well as for income generation.
However, the tactical approach uses active strategies on allocation of investment
assets, as well as using different types of strategies within a short-term interval to
generate capital gains.
2.7.2 Definition of Portfolio Management
Portfolio management refers to managing the current assets included in the
portfolios with the objective of minimizing risk and generating higher returns. This
includes diversifying the investment assets, preserving the investment capital,
aligning the investment portfolios with the clients’ goals, managing the liquidity of
the investments, as well as constant monitoring of the assets included based on the
44
constantly changing market situations.
2.7.3 Active and Passive Portfolio Management
Two types of portfolio management can be greatly observed: active and
passive. Indeed, active portfolio management refers to professional investment firms
and firm managers actively managing the portfolio to outperform the specific
benchmark index such as S&P 500 index, SET index, SET50 index etc. Thus,
frequent buying and selling of shares and investment assets are involved in this
management. Therefore, trading fees and portfolio management fees also need to be
considered in this type of portfolio.
However, for passive portfolio management, this refers to strategically
allocating the investment assets with the purpose of replicating the target specific
benchmark to match its performance. Thus, frequent buying and selling are not
involved in this type. Passive portfolio management style is used in this thesis study.
2.8 Markowitz’s Modern Portfolio Theory
2.8.1 What is Modern Portfolio Theory
The Modern Portfolio Theory is based on the concept that it is possible to
develop an ideal portfolio that will offer investors the maximized returns just by
accepting the optimal amount of risk. Indeed, it is based on the mathematical
framework developed by Harry Markowitz in 1952. According to (Markowitz, 1952),
a diverse, efficient, and profitable investment portfolio can be achieved by combining
multiple asset classes, that will maximize investors’ returns without taking
unacceptable levels of risk. In other words, it stated that risky assets can now be
45
invested along with other assets, which in turn, a combination of all reduces the total
risk of the Portfolio, while achieving the highest expected return.
Before the development of modern portfolio theory, traditional portfolio
theory was usually applied in investment strategies. According to (Leković, 2021),
they stated that only lowest risk securities should be selected in traditional ways.
Therefore, the traditional theory only selects the investment assets with the risk and
return constraints.
However, in modern portfolio theory, the risky assets and traditional assets
can be assembled as a portfolio of assets, which in turn reduces the total risk of the
portfolio, while achieving diversification.
2.8.2 Diversification
The concept of diversification is introduced well in the modern portfolio
theory. In fact, the key idea is that the risk and return of an investment asset should
not be considered by itself. Instead, it should be considered on how it contributes to
the portfolio’s overall risk and return. Indeed, diversification is achieved by investing
in different kinds of investment assets, which are less correlated with each other.
Thus, the total portfolio’ risk is reduced and less risky rather than investing in only
one type of asset.
However, according to (Kierkegaard et al., 2006), the modern portfolio theory
only reduced the unsystematic risks of the total portfolio. In fact, unsystematic risks
refer to risks that affect only a particular asset. This unsystematic risk is inherent in a
particular firm or industry. However, this can be diversified, it is also called a
diversifiable risk.
46
In fact, systematic risks cannot be diversified since they affect the whole
market and are typically caused by macroeconomic events. External broad factors
such as stock market crashes, inflation, interest rates etc. can influence systematic
risk.
Figure 2.5: Systematic and unsystematic risk
2.8.3 Risk-Free Rate
The risk-free rate concept is important in Modern Portfolio Theory such that it
is the rate of return that a rational investor could expect to earn on his investment
asset with zero risk taken. Indeed, the Modern Portfolio Theory, the Capital Asset
Pricing Model use the risk-free rate as the main component factor from which other
essential valuations are derived.
Thus, the risk-free rate indicates that such a particular financial asset if
invested will have no risk, thus investors will not generate a loss. In fact, BMA
government bonds are considered as to be risk-free assets since they are backed by the
full faith of the Thai government. Thus, BMA government bonds are used as the risk-
free rate indicator in this thesis study.
47
2.8.4 Portfolio Frontier
The portfolio frontier is a parametric plot of the expected return and the
expected risk for different correlations.
2.8.5 Expected Return from Portfolio
Thus, the expected return E(Rp) of the portfolio can be calculated by the
weighted average of return on the included Assets with the portfolio as follows:
E = E()
E(Rp) = w1 * E(R1) + w2 * E(R2) + w3 * E(R3) + ….+ wn * E(Rn),
whereas: wi = the individual proportion of value, invested in the total portfolio,
E(Rn) = the expected rate of return of the respective individual security i,
n = the number of total securities allocated in the total portfolio.
2.8.6 Standard Deviation of Portfolio
Furthermore, the total risk of the portfolio is measured by the total portfolio
standard deviation. Thus, the relationship between the chosen assets, covariance and
coefficient of correlation must be considered. In fact, the covariance could range from
negative (“-”) to positive (“+”), while the coefficient of correlation (ρ) could range
from -1 to +1.
Thus, for 2 Assets in the Portfolio, the total portfolio standard deviation could
be calculated as below:
= + + 2(1, 2)
48
where: covariance (Asset1, Asset2) = ρ12 * σ1 * σ2
ρ12 = correlation between Asset 1 and Asset 2
Furthermore, for 3 Assets, the total portfolio standard deviation formula would
be calculated as below:
= + +
+2(1, 2)
+2(1, 3)
+ 2(2, 3)
Furthermore, for 4 Assets, the total portfolio standard deviation formula would
be calculated as below:
=
+ + +
+2(1, 2)
+2(1, 3)
+2(1, 4)
+ 2(2, 3)
+ 2(2, 4)
+ 2(3, 4)
Furthermore, for 5 Assets, the total portfolio standard deviation formula would
be calculated as below:
49
=
+ + + +
+2(1, 2)
+2(1, 3)
+2(1, 4)
+ 2(2, 3)
+ 2(2, 4)
+ 2(3, 4)
+ 2(1, 5)
+ 2(2, 5)
+ 2(3, 5)
+ 2(4, 5)
2.8.7 Efficient Frontier
The portfolio frontier is only considered “efficient” if the parametric plot has
the best possible expected return for its level of risk, which is measured by the
standard deviation of the total portfolio’s return. Indeed, according to (Kierkegaard et
al., 2006), the efficient frontier is a parametric plot of efficient portfolios that will be
plotted along the curve, with the X-axis, indicating total standard deviation risk, while
Y-axis indicating the expected return E(Rp).
Figure 2.6: the efficient frontier parametric plot of a portfolio
50
Therefore, the efficient frontier is a curve, drawn from the left to the right,
where each point lying on the curve indicates the highest expected return respective to
its corresponding risk, in which the assets will be allocated correctly to achieve those
outcomes.
The efficient frontier curve can then be constructed by using statistical
software (such as Excel, SPSS etc.). Firstly, by using the Monte-Carlo simulation, the
various random amounts of portfolio weight allocations are generated and plotted on
the curve. Secondly, by utilizing the linear-programming functions to find the highest
Sharpe Ratio for a specific risk, ranging from minimum range to maximum range. In
this way, optimal portfolios can be identified along the percent of risk taken, which in
turn, will be the efficient frontier curve connected along.
The efficient frontier curve is important to the modern portfolio theory such
that finding the optimum asset weight allocations for a specific portfolio lies
anywhere along the efficient frontier (Kierkegaard et al., 2006). Therefore, portfolios
lying above the efficient frontier curve is not possible while portfolios lying below the
efficient frontier curve indicate that those portfolios are not efficient and cannot be
considered.
The efficient frontier curve is so important to this thesis study that investors
who want the highest risk-adjusted rate of return can only invest in portfolios that
reside on the efficient frontier curve.
2.8.8 Capital Allocation Line
The capital allocation line, also known as the capital market line, is
constructed on the graph with the possible combination of risk-free and risky assets. It
51
indicates the amount of returns rational investors could earn by accepting a certain
level of risk with their investment. Indeed, it is also known as the reward-to-
variability ratio.
Moreover, the concept of capital allocation line is crucial to the modern
portfolio theory approach such that it indicates how much risk investors need to take
on the portfolio of risky and risk-free investment assets.
In addition, according to figure 2.7 below, the intersection of Efficient Frontier
and the capital allocation line indicates the optimal assets allocation of the portfolio.
Figure 2.7: the Capital Allocation Line and Efficient Frontier
2.8.9 Assumptions of Modern Portfolio Theory
According to (Markowitz, 1952), the Modern Portfolio Theory approach will
need to be utilized under the following assumptions:
1. The market will be considered efficient, such that any investor will have
transparent information from the market. That is, all investors would have
52
access to the same information.
2. All investors will be considered risk-averse, such that they would only
accept additional risk if certain additional return could be achieved.
3. It is assumed that all investors want the same maximum rate of return that
they could possibly achieve from their selected investment assets.
4. The additional costs associated with trading costs and taxes would not be
highly considered in these investment decisions.
5. There are no capital restrictions such that investors could borrow unlimited
capital, considered at the risk-free rate of return.
6. External shocks and external market fluctuations or other macroeconomic
changes will not be considered. In fact, it is assumed that the market is
constant and unchanged.
2.8.10 Benefits and Limitations of Modern Portfolio Theory
The benefits of utilizing MPT theory approach for constructing optimal
portfolios in this study are as follows:
1. A form of diversification: The theory developed by Harry Markowitz proved
that modern portfolio theory is very useful for any rational investor who wants
to build a set of diversified portfolios.
53
2. Volatility management: Based on the Efficient Frontier curve, investors can
choose the amount of risk willingness to take, to achieve the expected amount
of return. For risk-averse investors, they can choose less risk of efficient
portfolios, while accepting to choose fewer expected return, and vice versa.
3. Efficient sets of portfolios: Numerous combinations of different asset classes
of those portfolios can be plotted on the typical graph, with the horizontal axis
(X-axis) showing the standard deviation (portfolio risk) while the vertical axis
(Y-axis) showing the expected return (Markowitz, 1952).
For example, considering two portfolios such as:
Portfolio A: expected return = 15 percent, standard deviation = 9.6 percent
Portfolio B: expected return = 13 percent, standard deviation = 11 percent
In this case, Portfolio A would be considered as the more efficient portfolio
since it offers rational investors a higher expected return at lower risk than
other inefficient portfolios.
However, the modern portfolio theory has some limitations to the extent that it
neglects the effect of macro-Economic events, as well as possible technological
changes, and market changes of the equity companies (Yu & Zhang, 2023). In fact,
unlike “Post-Modern Portfolio Theory”, the current theory focuses on mean variance
of investment returns, rather than using the downside risk. Moreover, in this modern
portfolio theory, trading costs, broker fees and commission fees are not typically
54
considered in this case.
2.9 Capital Asset Pricing Model (CAPM)
2.9.1 What is CAPM
The CAPM model represents certain trade-offs between expected risk and
return for efficient portfolios. That is, the risks based on CAPM model are considered
as “total risks”, which is then identified by the variance (or) standard deviation of the
portfolio returns. Therefore, the total risks include two types of risks: systematic risks
and unsystematic risks.
According to (Irfanullah, 2022), the Capital Market Line (CML), that connects
from the Rf point (Risk-Free Rate) to the tangency point on the Efficient Frontier of
the following portfolios. The tangency point is the point where optimal portfolio can
be achieved, due to the highest Sharpe ratio. Investors will maximize their expected
return E(Rp) for the given amount of total variance risk. In other words, it will be the
point where the value of the Sharpe ratio will be highest.
Figure 2.8: the Capital Market Line (CML) and Efficient Frontier
55
2.9.2 CAPM Formula and Calculation
The CAPM model is formulated using the below formula:
E(Ri) = Rf + Beta * (E(Rm) – Rf)
whereas: E(Ri) = Total portfolio’s expected return
Rf = Risk-free rate available, regarding to Thai capital markets
Beta = Total beta of the investment portfolio
(E(Rp) – Rf) = The market premium
2.9.3 Expected Return
The expected return on the investment portfolio is equal to the risk-free rate of
return plus a risk premium, which is based on the beta of that portfolio.
2.9.4 Risk free rate and market rate of return
The “Rf” notation is for the risk-free rate, in which the Thai BMA government
bond’s yield will be used. Indeed, since the investment assets are based on Thai
capital markets, the risk-free rate must be based on the corresponding country.
The “E(Rm)” notation is for the market rate of return, where the portfolio will
achieve if invested in Thai capital markets.
2.9.5 Beta
The Beta is the measure of the portfolio’s risk which is mainly reflected by the
price fluctuations relative to the overall market. Indeed, it is all the portfolio assets’
sensitivity to the market risk. Thus, if beta is 1.3, the portfolio will exhibit 130%
volatility based on the average market.
56
2.9.6 Market Risk Premium
The notation “(E(Rm) – Rf)” refers to the market risk premium, in which it
indicates the additional return over the risk-free rate. Based on investors’ risk
tolerance, it is the reward compensation to the investors for investing in a risker
investment asset.
2.10 Risk-Adjusted Rate of Return
2.10.1 Sharpe Ratio
Furthermore, according to (Yang, 2021), there is also a relationship between
the CAPM and Sharpe ratio. The Sharpe ratio is considered one of the indices derived
from the CAPM such that it is often utilized to calculate the portfolio investment
value.
In fact, Sharpe ratio, is the measurement value of risk-adjusted return. In fact,
the Sharpe ratio evaluates how much excess return that investors could achieve for per
unit of additional risk (i.e., total variance). Thus, portfolios with the highest Sharpe
ratio indicate that it is the optimum portfolio, lying on the efficient frontier curve,
while being tangent to the CML line.
2.10.2 Sharpe Ratio Formula and Calculation
The formula for Sharpe Ratio is defined as follows:
=
whereas:
E(Rp) = Expected Return of the Total Portfolio
57
Rf = risk-free rate of return
σp = risk/standard deviation of total portfolio
2.10.3 Treynor Ratio
According to (Hübner, 2005), the Treynor ratio evaluates the portfolio
performance against a different benchmark than the previous Sharpe ratio. In fact, the
Treynor ratio evaluates the portfolio performance against the equity market index,
which is the Beta. Therefore, the Treynor ratio can be interpreted as whether the
constructed portfolios are outperforming against the market or not.
2.10.4 Treynor Ratio Formula and Calculation
The formula for Treynor ratio is defined as follows:
=
whereas:
E(Rp) = Expected return of the total portfolio
Rf = risk-free rate of return
Betap = beta of the portfolio
However, Treynor ratio has several limitations (Tamplin, 2023). If the beta
values are negative, Treynor ratio will not give meaningful results. In fact, the
Treynor ratios value cannot be compared with each other since the values are not
regarded as ordinal.
58
2.10.5 Sharpe ratio vs Treynor ratio
Both the Sharpe ratio and the Treynor ratio are designed to measure the risk-
adjusted rate of return. Indeed, the Sharpe ratio’s calculation is based on measuring
the excess return by the total portfolio’s standard deviation. However, the Treynor
ratio is based on measuring the excess return by the portfolio investment Beta.
Therefore, using the Sharpe ratio will be able to analyze the portfolio
investment’s return compared to its total risk while the Treynor ratio only discovers
the excess return for each unit of risk in that portfolio.
2.11. Related research
According to the literature review, (Kong-ied, 2017) examined the returns of
mutual fund firms in Thailand. The research was conducted on mutual funds of two
investment firms: Thanachart Securities Public Company Limited and Asset Plus
Fund Management Company Limited, which tries to discover the range of average
return if invested in mutual funds of Thai capital markets. The results revealed that
the lowest and highest range bounds from 4.69 percent - 17.25 percent, while data
analysis was conducted using multiple regression analysis. The research paper tried to
conduct comparisons on performance of two asset classes: mutual funds with equity
assets and mutual funds with fixed-income assets. Thus, the results revealed that the
performance of Thai mutual funds is considered quite volatile and is also based on the
firms.
Moreover, (Sae-ue, 2017) examined the portfolio performance of several asset
classes including Thai equities, government bonds, corporate bonds, global equities,
and property funds. In fact, the modern portfolio theory and Sharpe ratio are used to
59
construct the optimal portfolios, using the data sources from Jan 1, 2015 to Dec 31,
2016. Rebalancing policies are applied annually in that case; however, it was revealed
that rebalancing was not quite effective, due to short-term consideration on that
timeframe.
In addition, according to another literature review, (Kierkegaard et al., 2006)
examined the practical application of modern portfolio theory. They found that for
any rational investor to construct optimal portfolios requires the portfolio to be
actively managed, as well as investing for long-term tenure, while depending on those
Investors’ risk appetite.
Moreover, according to another relevant literature review, (Jitteemet, 2016)
examined the return and risk in market for alternative investment (MAI) using Farma-
French Three-Factor Model. The research revealed the possible expected return and
risks for MAI (Market for Alternative Investments of Thailand SET). Contrary to
Farma-French 3 model, the research revealed that big market cap portfolio exhibits
lower risk than small market cap portfolio. Thus, utilizing Modern Portfolio Theory
would be a better choice than applying the Farma-French 3 Model.
Moreover, according to (Sungkarat, 2016), he examined the expected returns
and systematic risks of infrastructure funds. Five Infrastructure funds, namely
“ABPIF”, “BTSGIF”, “DIF”, “EGATIF” and “JASIF” are studied to examine
overvalued and undervalued stocks, while proving as those infrastructure funds
offering diversification effects compared with the SET Index. In fact, fundamental
analysis using CAPM Model (Capital-Asset Pricing Model) is considered. He
discovered that four of the five selected stocks are undervalued. In fact, he also found
that alternative assets have low correlation with traditional Assets (i.e., SET Index),
60
which proved such that the alternatives Assets of Thai capital markets offer the
possible diversification in the optimal portfolios.
According to another literature review, (Radović et al., 2018) examined the
application of modern portfolio theory in the Serbia capital markets. They found that
in emerging countries, the Markowitz’s theory had limitations to the extent that only
liquid stocks will result in efficient portfolios, while the related Serbian market
exhibits the illiquidity issues, thus quite resulting in applying the theory difficult.
However, according to another literature review, (Abdelmalak, 2017)
examined the modern portfolio theory application on U.S equity markets. He found
that customized chosen target portfolios perform better than the tangent portfolios
(from the modern portfolio theory) and the overall market. In fact, there are no
liquidity issues since the market is efficient in developed countries.
In addition, according to another literature review, (Rhoads, 2021) examined
the construction of efficient portfolios on S&P 500. He found the importance of
utilizing the modern portfolio theory to construct efficient frontier curves and the
efficient portfolios of S&P500. 34 Portfolios are constructed, which reveal the
maximized expected return at the market risk or reveal the minimized Risk at the
market return.
Moreover, according to another literature review, (Achudume & Ugbebor,
2021) examined the optimal portfolios using the MatLab mathematical software, to
compute investors’ varying rates of return. They found that risk-averse investors have
the possibility to earn expected returns with diversification. However, they found that
investment policies and management are important factors for portfolio optimization.
Indeed, Choi & Mukherji (2010) also examined the construction of optimal
61
portfolios using six major financial assets on different holding periods. They found
that as the holding period time increases, the allocation of riskier assets are being
increased more, while the allocations of safer assets are being decreased more. The
most risk-minimizing assets include treasury bills, while the equity stocks are
allocated increasingly as the time horizon lengthens. They also found that the equity
stocks become the major assets involved when considering the optimal portfolios for
10 years. They also examined the construction of efficient portfolios using six major
assets including treasury bills, intermediate government bonds, long government
bonds, long corporate bonds, and large company stocks. 3 different holding periods
are analyzed and examined for the 1, 5, and 10 years, which are short, medium, and
long holding investment periods. Moreover, they also found that for just 1-year
returns, 96percent is allocated to treasury bills, and the rest 4.24percent to the small
company stocks. However, for 5-year returns in optimal portfolios, 88.42percent is
allocated to treasury bills, while the rest 11.58percent to the small company stocks.
Also, for 10-year returns in optimal portfolios, 80.56percent is allocated to treasury
bills, while the rest 19.44percent to the small company stocks. Thus, they revealed
that as the investment horizon lengthens, risky assets are increasingly more allocated
to the optimal portfolios, while decreasing allocation on the safer assets (treasury
bills).
Moreover, other researchers examined that stocks become less risky when
considering over the long-term horizon. Thus, according to (Butler & Domian, 1991),
there is only 11percent probability of stocks underperforming against bonds for 10-
year returns, and even only 5percent over 20-year returns.
According to another literature review, (Levy & Spector, 1996) examined the
62
investors’ utility functions on asset allocations of optimal portfolios. They found that
investors who have a log-wealth utility functions should only invest 90percent in
small stocks. Indeed, (Hansson & Persson, 2000) reported that efficient portfolios
have been allocated with the rising stock allocations and declining bonds (T-bills)
allocations as the investment term increases from 1 to 10 years.
Moreover, regarding new alternative assets, (Kyriazis, 2022) examined a large
basket of national currencies, commodities, precious metals, fuel, and
cryptocurrencies due to the impacts of the Russian-Ukrainian conflict. Optimal
portfolios are constructed for different risk-aversion levels by investors. He found that
the Chinese yuan, gold, soybeans, sugar, corn, and Bitcoin are considered safe-haven
assets while the Japanese yen, wheat, natural gas, and combination of Bitcoin and
Ethereum are regarded as suitable assets for risk-seekers for profit opportunities.
However, he examined that the agriculture assets’ portfolio is the best performing
while the cryptocurrency portfolio is the least performing for risk-return trade-off
considerations. Overall, he examined the useful investment insights on decision-
making factors during geopolitical uncertainties and enough recommendations on
improving welfare during global conflicts.
Moreover, according to literature review, (Zhang, 2022) examined the
construction of optimal portfolios using ten stocks of S&P 500 Index, from year 2000
to 2020. He examined the comparison between the two models of portfolio:
Markowitz model and the Single Index model. He found that the optimal portfolio of
the Markowitz model is better than the portfolio of the Single Index model such that
the Markowitz model can get higher returns than the Single Index Model. (Zhang,
2022) also examined that investors can prevent risks through portfolio investing by
63
following the long-term strategy.
In addition, regarding the role of cryptocurrencies, (Inci & Lagasse, 2019)
examined the role of cryptocurrencies in improving the performance of optimal
portfolios mainly from traditional assets. They found that when considered as a single
investment, Ripple is the best cryptocurrency to invest in, followed by Bitcoin and
Litecoin. However, they also examined that Bitcoin is the only cryptocurrency asset
that enhances the characteristics of the constructed optimal portfolios. In fact,
according to (Inci & Lagasse, 2019), the weights percent allocation of “optimal
portfolio no cryptocurrency” include 48.6percent in DJIA, 4.4percent in VIX,
0.90percent in Real Estate and 46percent in Bonds. However, for optimal portfolio
with BTC, the allocation results in 3percent in BTC, 47percent in DJIA, 4.3percent in
VIX, 0.77percent in Real Estate and 44.8percent in Bonds. Moreover, they also
further found that the weights allocation of optimal portfolio with XRP results in
1.47percent in XRP, 49percent in DJIA, 4.5percent in VIX, 1.11percent in Real Estate
and 43.7percent in Bonds. However, when Litecoin (LTC) is considered in the
optimal portfolio, the weight percent allocation results in 1.33percent in LTC,
47.9percent in DJIA, 4.4percent in VIX, 0.87percent in Real Estate, and 45.5percent
in Bonds.
Moreover, according to (Lee & Eid, 2018), they examined the theories found
in academic research and the actual practices of investment managers in Brazil. They
found that investment managers have not followed the best practices recommended by
the academic theories and literature, and there is a wide gap between the academic
theories and the actual asset management practices. However, (Lee & Eid, 2018)
revealed that the modern portfolio theory is still currently more widely used than the
64
post-modern portfolio theory in practice. Moreover, in practical terms, quantitative
portfolio optimization is not widely used as the simple rule of maximum limit on each
single asset.
In addition, according to another literature review, Yu, J., & Zhang, J. (2023,
March 2) examined the emergence of modern portfolio theory in this 21st century.
They examined the importance of diversification that led to reducing the overall
portfolio risk.
According to (Bary, 2023), the inclusion of alternative assets reaches up to
50percent (maximum) of total asset portfolio in top US endowment funds.
For example, according to figure 2.9, it shows that US Endowment Funds
place significant exposures to assets that are regarded as alternatives. This includes:
“real-estate” in 7percent of total portfolio, while 19percent in “private equity,
7percent in “natural resources”. This indicates that the big endowment funds of the
United States, will give inspiration and reference methodologies and asset allocations
strategies for smaller firms, as well as individual investors for considerable exposure
of alternatives after the 2008 financial crash.
Figure 2.9: Analysis of Top US Endowment Funds with Net Asset Value > USD 1
billion in 2016
65
Moreover, according to (Bary, 2023), there is a significant contribution to total
investment returns due to alternative investments.
In fact, they stated that the allocation of 0percent in alternative assets, would
only result in 4.9 percent for 10-year return, while 45percent allocation in alternatives,
would result in 6.9 percent for 10-year return.
Figure 2.10: Top US Endowment Funds in comparison with Traditional Portfolios
Thus, investors should regard alternatives as one of the assets that could
achieve superior investment returns with consistent figures. This had been shown by
the Alternatives Allocation of US Endowment Funds.
Regarding the below figure, it indicates that the Top 5 US Endowment
50Funds & Super Endowments place allocation of up to 50percent on Alternatives.
This, in turn, resulted in higher superior returns than 60:40 (Equity/Bond) portfolios
as stated below figure 2.28.
Figure 2.11: Percentage allocation on Alternatives, for 20 year annualized returns
66
In addition, regarding real-estate investments, Janta (2014) examined the
influence of direct and indirect real-estate investments in multiple-assets portfolios.
She found that real-estate returns proved to exhibit very low correlations with
traditional financial assets whether be direct investments or indirect investments. In
fact, she examined and constructed the portfolios with Efficient Frontier and Modern
Portfolio Theory as well. In fact, she also examined the comparison of direct and
indirect investments in portfolios with the inclusion of a single detached house price
index, townhouse price index, condominium price index, land price index, and
property fund index. The time scope is from March 2009 to September 2014, with a
resulting a total of 67 months, while the total constructed of 7 portfolios were
conducted. The results revealed that optimal portfolios of stocks + bonds + real-estate
(direct and indirect) should include 4.39% of bonds, 5.21% of stocks, 28.07% of
property funds, 31.85% of townhouses and land, and 9.77% of condominiums.
However, she examined that only indirect real estate assets can perform risk reduction
and increment of returns, while direct real estate assets only perform the risk
reduction in the constructed portfolios.
Moreover, regarding growth stocks and cryptocurrencies, Onniwattananon
(2023) examined the risk and return of growth stocks portfolios with Bitcoin. She
found that in terms of risk-adjusted return, Sharpe ratios and Treynor ratios are greatly
improved if Bitcoin is considered in the portfolio. The time studied is from 2018 to
2022. The investment assets include SET stocks that has 5 years or longer consecutive
growth, positive Earnings-per-Share for 6 years and above, and Return-On-Equity
with 20% and above stocks. 3 Portfolios are constructed with 100% Thai growth
stocks, 80% on growth stocks and 20% on Bitcoin, and another third portfolio with
67
70% Thai growth stocks and 30% bitcoin. The results revealed that the portfolio with
the highest amount of Bitcoin proved to result in highest risk-adjusted return (Sharpe
ratio). However, she highlighted the highly volatile and risky nature of
cryptocurrencies such that proper cautions should be made into considerations.
However, she also examined that the portfolio with only Thai growth stocks still
outperform the targeted market situations.
2.12. Research Gaps
Therefore, based on previous research results, it is evident that Markowitz’s
theory only worked on markets which don’t have liquidity issues, while some of the
target-chosen portfolios proved better than the tangential portfolios as a result. In fact,
there is not much analysis and research studies on investment portfolios in Thai
capital markets, together with alternative assets, over the longer timeframe, especially
from 5 to 10 Year interval periods.
Therefore, this thesis study research will fill these gaps by analyzing 5 major
assets: stocks, bonds, real-estate, 96.5% gold and Bitcoin, over the longer timeframe,
which Markowitz’s Theory proved to be efficient.
2.13. Conceptual Framework of the Study
The following describes the conceptual framework, with respect to this
research study. The left 3 boxes show the independent variables with respect to each
sub variable, while the right box shows the dependent variable with respect to each
sub variable.
68
Figure 2.12: Conceptual Framework Diagram of the thesis study
69
Independent Variables
1. “Portfolio Composition”, which includes the 8 sub-variables of several assets’
combinations of portfolios. Therefore, a total of 8 types of portfolios will have
to be constructed for both 5-year and 10-year investment horizons, so that
comparisons could be made and examine the best performance portfolio
available.
2. “Assets Allocation”, in which how much percent weights of each asset to be
included, would impact on the portfolios’ results. In fact, the optimum
portfolios would reveal the best possible percent weight allocations of asset
types, based on the highest Sharpe Ratio. However, respective to the selected
percent of risk taken by investors, the portfolios’ results could vary.
3. “Investment Period”, which is selected on two Interval Periods: 5-year period
(From 2018-2022) and 10-year period (From 2013-2022). The two difference
intervals could reveal on the difference in investment portfolios, and
comparisons would be examined on which timeframe is better.
Dependent Variables
1. The last dependent variable is “Optimal Portfolios”, which will examine the
rate of return, the risk and risk-adjusted rate of return, due to the variations of
3 prior independent variables.
70
CHAPTER 3
METHODOLOGY
The methodology of this study can be divided into four sections, including (1)
data and sources, (2) analytical method (3) research hypotheses and (4) limitation of
the study.
3.1. Data and Sources
This study covers two traditional assets, including common stocks and
corporate bonds, and three alternative assets, including real estate, gold, and
cryptocurrency. In addition, common stocks are represented by SET50 index,
corporate bonds are represented by Thai BMA corporate bond index, real estate is
represented by physical real estate price index as well as property fund & real-estate
investment trusts index, gold is represented by 96.5 percent gold price and
cryptocurrency is represented by Bitcoin price.
This study relies on monthly price data of each asset mentioned above during
January 2013 and December 2022, a total of 5 years or 120 months. All data are
obtained from the secondary sources. Data to be analyzed and their sources can be
summarized by Table 3.1.
71
Table 3.1: Data and Sources
No.
Assets
Price Indicator
Sources
1
Common Stocks
SET50 Index
Stock Exchange of Thailand (SET)
URL:
https://www.set.or.th/en/market/ind
ex/set50/overview
2
Corporate Bonds
Corporate Bond Index
(Rating: A- & above)
The Thai Bond Market Association
(Thai BMA)
URL:
https://www.thaibma.or.th/EN/Mark
et/Index/MTMCorpIndex.aspx
3
Real Estate
1. Real Estate Index
2. SET Property Fund &
REIT Index
Bank of Thailand (BOT)
URL:
https://app.bot.or.th/BTWS_STAT/s
tatistics/BOTWEBSTAT.aspx?repo
rtID=920&language=ENG
SET PF&REIT
URL:
https://www.set.or.th/en/market/ind
ex/set/propcon/pf&reit
72
Table 3.1(Continued): Data and Sources
No.
Assets
Price Indicator
Sources
4
Gold
96.5 percent purity gold
price
(THB / 1 Baht Gold)
Stock Exchange of Thailand (SET)
URL:
https://www.set.or.th/en/market/ind
ex/set/propcon/pf&reit
5
Cryptocurrency
Bitcoin Price
(USD / 1 BTC)
Gold Traders Association
URL:
https://www.goldtraders.or.th/AvgP
riceList.aspx
6
Risk-Free Asset
10-Year Government
Bond Yield
Yahoo Finance
URL:
https://finance.yahoo.com/quote/BT
C-USD/
Asset 1: Common Stocks as represented by SET50 index
The secondary data sources will be officially collected by extracting from
SETSMART platform. Through the SETSMART Platform, the following data sources
will be extracted:
1. 5 Year term historical closing price of SET50 index
(1st Jan 2018 – 31st Dec 2022)
2. 10 Year term historical closing Price of SET50 index
73
(1st Jan 2013 – 31st Dec 2022)
Asset 2: Fixed-Income Securities as represented by Corporate bond’s index
Regarding corporate bonds (MTM corporate bond A- up rating), its secondary
data will be gathered from the Thai BMA Association. In fact, according to (thaibma,
2023), it is the official business-related organization, under the Securities and
Exchange Commission Act B.E. 2535 of Thailand. Therefore, the data gathered can
be considered as final, thorough, and accurate.
The official data sources of MTM corporate A- up will be extracted as
follows:
1. 5 Year term historical total return price of MTM A- up
(1st Jan 2018 – 31st Dec 2022)
2. 10 Year term historical total return price of MTM A- up
(1st Jan 2013 – 31st Dec 2022)
Asset 3: Real-estate as represented by Real Estate Price Index and Property Fund &
REIT Index
Furthermore, two types of real-estate indices: physical real-estate (by BOT)
and real-estate funds (by SET).
For physical real-estate (by BOT), the Nationwide category will be used to
present the whole real-estate index in Thailand property market.
74
For real-estate funds, the PF&REIT index presents all the REITs available in
the Thai SET market.
The extracted data sources will be as follows:
1. 5 Year term historical closing price of physical real-estate index (BOT)
(1st Jan 2018 – 31st Dec 2022)
2. 10 Year term historical closing price of real-estate index (BOT)
(1st Jan 2013 – 31st Dec 2022)
3. 5 Year term historical closing price of PF&REIT index (SET)
(1st Jan 2018 – 31st Dec 2022)
4. 10 Year term historical closing price of PF&REIT index (SET)
(1st Jan 2013 – 31st Dec 2022)
Asset 4: Gold as represented by 96.5 percent purity Gold (1 Baht Standard)
Furthermore, regarding the gold assets, the secondary data sources of Thai
Gold bars (96.5percent purity), will also be retrieved through the Gold Traders
Association of Thailand website. The details extracted will be as follows:
1. 5 Year term historical closing price of Thai 1Baht Gold (96.5 percent purity)
(1st Jan 2018 – 31st Dec 2022)
2. 10 Year term historical closing price of Thai 1Baht Gold (96.5 percent purity)
75
(1st Jan 2013 – 31st Dec 2022)
Asset 5: Cryptocurrency as represented by Bitcoin (BTC)
Furthermore, regarding this cryptocurrency asset, the secondary trading
historical data Sources of Bitcoin, will also be retrieved through the yahoo finance
portal website. The details extracted will be as follows:
1. 5 Year term historical closing price of Bitcoin
(1st Jan 2018 – 31st Dec 2022)
2. 10 Year term historical closing price of Bitcoin
(1st Jan 2013 – 31st Dec 2022)
Asset 6: Risk-Free Asset as represented by 10-Year Thai BMA Government Bond
Yield
This BMA government yield will be used as the risk-free rate in calculating
the portfolios. The current average rate is 2.9747 percent.
76
3.2 Analytical Methods
In this section, the optimal portfolios construction will be conducted in 6 steps
as follows:
Step 1: Calculate Returns and Risk of Each Single Asset
Monthly rate of return: Each monthly price return between adjacent interval periods
will be evaluated using the below formula in excel:
Rate of Return () = Final Value
Initial Value 1 = P
P
1
whereas: P1 = current Monthly Price, P0 = Previous Monthly Price
For 5-year investment period, there will be a total of 60 values of monthly
returns, while a 10-year investment period, will include a total of 120 values of
monthly returns. The monthly return values are required since the variance,
covariance, standard deviation, and mean values of the assets need to be calculated
further for portfolio construction.
Mean return of each asset: Using the monthly returns data, the mean value of each
asset will be calculated as below:
=
whereas PMi = Each monthly price return of a particular asset, N = Total number of
months for the selected investment period
77
The calculation of mean returns will be used to process further variance and
covariance results, which are essential to estimate portfolio expected return and risk.
Variance of each asset: Using the monthly price returns data, the variance of each
asset will be calculated as follows:
= (
)
Whereas: σ2 = Variance of each asset, PMi = Each monthly price return of a particular
asset, = Mean return of Each Asset, N = Total number of months included
Standard Deviation of Each Asset: Using the monthly price returns data, the standard
deviation of each asset will be calculated as follows:
= (
)
Whereas: σ = standard deviation of each asset, PMi = each monthly price return of a
particular asset, = mean return of each asset, N = Total number of months
included
The calculation of variance and standard deviation will be used to process
further variance and covariance results, which are essential to estimate portfolio
expected return and risk.
78
Step 2: Examine the relationship between a pair of assets.
In this step, the correlation coefficient and covariance between one asset and
another need to be calculated. The covariance matrix and correlation coefficient
matrix will be constructed using the below formulas in excel:
Covariance calculations between one asset and another
In Mathematical terms,
(,)= (
)()
(1)
Where: Cov(X,Y) = Covariance of the asset X and asset Y
Xi = the individual value of the asset X
X
= the mean value of the asset X
Yi = the individual value of the asset Y
Y
= the mean value of the asset Y
However, since the Actual Calculation will be done in Excel, the Computerized
Formula is:
Excel Formulas: COVARIANCE.P(array1, array2)
where Array1 = series of asset X dataset values,
Array2 = series of asset Y dataset values
79
Correlation calculations of the assets included:
In mathematical terms,
=(,)
where: = correlation between asset X and asset Y
(,) = covariance of the asset X and asset Y
= standard deviation of the asset X
= Standard deviation of the asset Y
However, since the actual calculation will be done in excel, the computerized formula
is:
Excel Formulas: CORREL.P( array1, array2),
where Array1 = series of asset X dataset values,
Array2 = series of Asset Y dataset values
The correlation coefficients are constructed to examine the statistical
relationship, i.e., how a change in one asset affects a change in another asset, while
the covariance values will be calculated to examine how two assets change the same
way.
Step 3: Calculate portfolio return and risk
In this step, using statistical data from previous steps, the portfolio expected
return E(Rp), variance and standard deviation (for 2-asset portfolios, 3-asset
80
portfolios, and 4-asset portfolios) will be calculated as follows:
Portfolio Standard Deviation:
In mathematical terms, for 2-asset portfolio,
= + + 2(1, 2)
whereas for 3-asset portfolio,
= + +
+2(1, 2)
+2(1, 3)
+ 2(2, 3)
whereas for 4-asset portfolio,
=
+ + +
+2(1, 2)
+2(1, 3)
+2(1, 4)
+ 2(2, 3)
+ 2(2, 4)
+ 2(3, 4)
Whereas for 5-asset portfolio,
81
=
+ + + +
+2(1, 2)
+2(1, 3)
+2(1, 4)
+ 2(2, 3)
+ 2(2, 4)
+ 2(3, 4)
+ 2(1, 5)
+ 2(2, 5)
+ 2(3, 5)
+ 2(4, 5)
where: = the total standard deviation (risk) of the portfolio
w1, w2, w3, w4, w5= percent weight of each asset allocated in the total portfolio
σ1, σ2, σ3, σ4, σ5 = standard deviation of each respective asset
Moreover, the above stated portfolio standard deviations are calculated to
measure the total risk of the constructed portfolios.
Individual asset’s expected return E(Rn):
In mathematical terms,
()= 1
= 12
whereas: = Annualized return,
E(Rn) = Expected Return, in annual terms
The Expected Return of each individual asset needs to be initially examined,
82
so that the total expected return of the portfolio can be calculated, when the percent
asset weights allocations are discovered.
Total Expected Return E(Rp) of the Portfolio: (Modern Portfolio Theory)
In mathematical terms,
= ()
whereas: E(Rp) = Total expected return of the portfolio
wi = percent weight of each asset allocated in the total portfolio
E(Rn) = Individual expected return of the particular asset
Therefore, using the above formulas, the constructed portfolios will then be
plotted on the graph, by utilizing the Monte Carlo simulation in excel, of 5,000
number of trials.
Therefore, based on the calculated risks (standard deviation), the risk category
of assets will be categorized as follows:
1. Moderate to high-risk investment assets: 96.5% percent Gold, Bitcoin
2. Low to moderate investment assets: physical real-estate index, real-estate
funds (PF&REIT index), SET50 index
3. Lowest risk investment asset: MTM corporate bonds (A- up)
83
Thus, regarding this thesis research study, the initial Net Asset Value of the
investment will be initially assumed at 10,000,000 Baht, while the possible
combinations of below 5 portfolios will be constructed, to find each respective
optimal portfolio.
Step 4: Construct Efficient Frontier
In this step, the efficient frontier curve will have to be constructed, using the
previous statistical data: expected return and standard deviation. The construction of
an efficient frontier curve is so essential that any arbitrary number of portfolios
residing on the curve gives the highest risk-adjusted expected return for the investor.
In fact, portfolios residing above the curve is impossible to achieve, while portfolios
residing below the efficient frontier curve is not considered efficient.
For this thesis study, the Microsoft excel additional Add-In: Analysis ToolPak
and the Monte-Carlo simulation will be used to plot numerous possible Portfolios on
the curve.
The Monte Carlo simulation of 5000 number of trials will be conducted on 2
variables: Expected return E(Rp) and standard deviation (σ). In fact, the efficient
frontier curves were constructed based on historical prices of 2 time-intervals: 5-year
and 10-year, so that comparisons and further findings could be conducted.
The horizontal X-axis will be the portfolio risk, while the vertical Y-axis will
be the total expected return of the portfolio.
84
Table 3.2: optimal portfolios construction on 16 possible scenarios
NO: Portfolios Investment
Period
Asset
Categories Details
1 2-Assets
Portfolio
5-Year
(2018-
2022)
Stocks + Bond SET50 + Corporate Bonds
2 2-Assets
Portfolio
10-Year
(2013-
2022)
Stocks + Bond SET50 + Corporate Bonds
3 3-Assets
Portfolio A
5-Year
(2018-
2022)
Stocks + Bond
+ Gold
SET50 + Corporate Bonds
+ 96.5 percent Gold
4 3-Assets
Portfolio A
10-Year
(2013-
2022)
Stocks + Bond
+ Gold
SET50 + Corporate Bonds
+ 96.5 percent Gold
5 3-Assets
Portfolio B
5-Year
(2018-
2022)
Stocks + Bond
+ Real Estate
Funds
SET50 + Corporate Bonds
+ PF&REIT Index (SET)
6 3-Assets
Portfolio B
10-Year
(2013-
2022)
Stocks + Bond
+ Real Estate
Funds
SET50 + Corporate Bonds
+ PF&REIT Index (SET)
7 3-Assets
Portfolio C
5-Year
(2018-
2022)
Stocks + Bond
+ Physical Real
Estate
SET50 + Corporate Bonds
+ Real Estate Index (BOT)
8 3-Assets
Portfolio C
10-Year
(2013-
2022)
Stocks + Bond
+ Physical Real
Estate
SET50 + Corporate Bonds
+ Real Estate Index (BOT)
9 4-Assets
Portfolio A
5-Year
(2018-
2022)
Stocks + Bond
+ Real Estate
Funds + Gold
SET50 + Corporate Bonds +
PF&REIT Index + 96.5 percent
Gold
10 4-Assets
Portfolio A
10-Year
(2013-
2022)
Stocks + Bond
+ Real Estate
Funds + Gold
SET50 + Corporate Bonds +
PF&REIT Index + 96.5 percent
Gold
85
Table 3.2 (Continued): optimal portfolios construction on 16 possible scenarios
NO: Portfolios Investment
Period
Asset
Categories Details
11 4-Assets
Portfolio B
5-Year
(2018-
2022)
Stocks + Bond
+ Physical Real
Estate + Gold
SET50 + Corporate Bonds +
+ Real Estate Index (BOT) +
96.5 percent Gold
12 4-Assets
Portfolio B
10-Year
(2013-
2022)
Stocks + Bond
+ Physical Real
Estate + Gold
SET50 + Corporate Bonds +
+ Real Estate Index (BOT) +
96.5 percent Gold
13 5-Assets
Portfolio A
5-Year
(2018-
2022)
Stocks + Bond
+ Real Estate
Funds + Gold +
Cryptocurrency
SET50 + Corporate Bonds +
PF&REIT Index + 96.5 percent
Gold + BTC
14 5-Assets
Portfolio A
10-Year
(2013-
2022)
Stocks + Bond
+ Real Estate
Funds + Gold +
Cryptocurrency
SET50 + Corporate Bonds +
PF&REIT Index + 96.5 percent
Gold + BTC
15 5-Assets
Portfolio B
5-Year
(2018-
2022)
Stocks + Bond
+ Physical
RealEstate +
Gold + BTC
SET50 + CorporateBonds +
+ RealEstate Index (BOT) +
GoldBars + BTC
16 5-Assets
Portfolio B
10-Year
(2013-
2022)
Stocks + Bond
+ Physical
RealEstate +
Gold + BTC
SET50 + CorporateBonds +
+ RealEstate Index (BOT) +
GoldBars + BTC
To construct the efficient frontier curve, the portfolios with the highest Sharpe
ratio with respect to each additional risk must be identified, using the Linear Solver
Statistical Package of Excel, as shown below.
Step 1: The finding variables will be the weight allocations of each asset included in
the portfolio.
86
Step 2: The objective set will be the highest Sharpe ratio of the portfolio.
Step 3: The following 2 constraints will also need to be added to find the efficient
portfolio plots on the efficient frontier curve:
Constraint 1: All asset weights must sum up to 100 percent
Constraint 2: the desired risk taken must be added into the input constraint,
before running the solver equation.
Figure 3.1: Construction of the Efficient Frontier curve using Excel Linear Solver
Package
87
Table 3.3: Sample of the Efficient Portfolios of 5-Asset Portfolios
x-axis y-axis SET50 Bonds Gold PF&REIT BTC
Sharpe
Ratio
0.0%
No
solution
1.0%
No
solution
2.5%
16.5%
5.4%
78.9%
2.0%
13.3%
0.3%
5.42
4.0%
27.3%
9.7%
63.8%
1.3%
24.7%
0.6%
6.09
5.0%
33.9%
12.3%
54.5%
0.9%
31.6%
0.7%
6.19
7.0%
46.7%
17.3%
36.7%
0.0%
45.0%
1.1%
6.24
9.0%
59.2%
22.3%
18.3%
0.0%
58.1%
1.4%
6.24
10.0%
65.4%
24.7%
9.1%
0.0%
64.6%
1.5%
6.24
12.5%
78.4%
14.4%
0.0%
0.0%
82.6%
3.0%
6.03
15.0%
86.3%
0.0%
0.0%
0.0%
95.5%
4.5%
5.56
17.5%
92.1%
0.0%
0.0%
0.0%
93.0%
7.0%
5.09
20.0%
96.8%
0.0%
0.0%
0.0%
91.0%
9.0%
4.69
25.0%
104.9%
0.0%
0.0%
0.0%
87.5%
12.5%
4.08
30.0%
112.4%
0.0%
0.0%
0.0%
84.2%
15.8%
3.65
35.0%
119.6%
0.0%
0.0%
0.0%
81.1%
18.9%
3.33
40.0%
126.7%
0.0%
0.0%
0.0%
78.1%
21.9%
3.092
45.0%
133.6%
0.0%
0.0%
0.0%
75.1%
24.9%
2.902
50.0%
140.4%
0.0%
0.0%
0.0%
72.2%
27.8%
2.749
55.0%
147.2%
0.0%
0.0%
0.0%
69.2%
30.8%
2.623
60.0%
154.0%
0.0%
0.0%
0.0%
66.3%
33.7%
2.517
70.0%
167.4%
0.0%
0.0%
0.0%
60.6%
39.4%
2.35
80.0%
180.8%
0.0%
0.0%
0.0%
54.8%
45.2%
2.22
The above table is one of the samples that indicate the set of efficient
portfolios, with regards to each risk taken (X-Axis), that would result in above highest
risk-adjusted return (Y-Axis). Connecting the data values of X-axis and Y-axis would
reveal the efficient frontier curve as shown below.
88
Figure 3.2: A sample of efficient frontier curve constructed on 5-Assets Portfolio
Therefore, based on the above figure 3.2, it reveals that efficient portfolios
could not exist above the efficient frontier curve, while the portfolios below the
efficient frontier curve are considered inefficient (i.e., rejected). This is because the
portfolios below the efficient frontier curve exhibits higher excess risk than efficient
portfolios’ results.
Thus, in this step, other efficient frontier Curves will further then be
constructed on other possible scenario of portfolios, using the stated above
procedures.
89
Step 5: Calculate the optimal combination of assets in portfolios.
In this step, the optimal combination of assets in portfolio needs to be
identified, that offers the highest risk-adjusted rate of return.
(Note: for this scenario, investors are assumed as being rational and risk-
averse, such that for every additional risk taken, investors must be awarded with the
highest risk-adjusted returns!)
Therefore, to calculate the risk-adjusted rate of return of the portfolios, the
Sharpe ratio will be evaluated in this step, to measure the excess return, per unit of
additional risk taken. Thus, the Portfolio with the highest Sharpe ratio will be the
optimal combination of assets involved.
According to (CFI Team, 2024), the Sharpe ratio grading can be regarded as
follows:
1. Sharpe ratio grading less than 1: considered as “Bad” investment.
2. Sharpe ratio value is between 1 and 1.999: is considered as moderate good
investment.
3. Sharpe ratio value is greater than 3; is considered the best (or) excellent
investment.
90
Sharpe ratio for discovery of the optimal portfolio point on efficient frontier curve
In Mathematical terms,
=
Where:
= Expected return of the total portfolio
= Risk-free rate related to the Thai capital markets
= Standard deviation of the total portfolio
Like the previous step, the Linear Solver Statistical Package of excel needs to
be used to find the optimum portfolio with highest Sharpe ratio. The steps are as
below:
Step 1: The finding variables will be the weight allocations of each asset included.
Step 2: The objective set will be the highest Sharpe ratio of the portfolio.
Step 3: The following 1 constraint will also need to be added to find the efficient
portfolio plots on the efficient frontier curve:
Constraint 1: All Asset Weights must sum up to 100%
91
Figure 3.3: Solution of the optimum portfolio with excel linear solver package
The Optimum Portfolio would only need to be identify once, such that the
point can be plotted on the efficient frontier curve as shown in figure 3.4 below.
92
Figure 3.4: Allocation of the optimal portfolio on the Efficient Frontier
Therefore, like this procedure, further optimum portfolios will be discovered
in other listed portfolio scenarios of this thesis study.
Step 6: Compare Efficient Frontier and the Optimal Combination of Assets in
Portfolio.
In this section, a total of 16 optimal portfolios and asset weight allocations will
be identified on 16 combinations of portfolios as stated above. The criteria will be
based on 3 factors:
93
1. portfolios with the highest return,
2. portfolios with the lowest risk (standard deviation) and
3. portfolios with the highest risk-adjusted return (Sharpe ratio).
Firstly, efficient frontier curves of traditional portfolios with only stocks
(SET50) and bonds (A- Rating corporate bonds) will be examined. Then, they will be
analyzed and compared based on the above 3 specific criteria. Optimal portfolios will
be identified on each criterion.
Secondly, efficient frontier curves of alternative asset portfolios with high-risk
assets portfolio will be examined. They include 7 types of portfolios as follows:
1. 3-Assets Portfolio with Stocks and Bonds and Physical real estate
2. 3-Assets Portfolio with Stocks and Bonds and Real estate funds
3. 3-Assets Portfolio with Stocks and Bonds and Gold
4. 4-Assets Portfolio with Stocks and Bonds and Gold and Physical real estate
5. 4-Assets Portfolio with Stocks and Bonds and Gold and Real estate funds
6. 5-Assets Portfolio with Stocks and Bonds and Gold and Physical real estate
and Bitcoin
7. 5-Assets Portfolio with Stocks and Bonds and Gold and Real estate funds
and Bitcoin
Then, they will be analyzed and compared based on the above 3 specific
94
criteria. Optimal portfolios will be identified on each criterion.
3.3 Research Hypothesis of this thesis study
The research hypotheses of this study will be stated as follows:
1. Traditional Portfolio with ONLY stocks and bonds exhibits the lower return
and risk than the active portfolios with alternative assets.
2. Portfolios with the most assets exhibit the highest risk adjusted rate of return
as measured by Sharpe Ratio.
3. Return and risk from a 5-year portfolio are higher than those from 10-year
portfolio.
4. The inclusion of Bitcoin does not offer a Higher Risk-Adjusted Rate of Return
as measured by Sharpe Ratio in the Portfolios.
3.4 Limitations of this thesis study
Although the following thesis research study has been conducted with the
author’s best efforts and in full faith, there are some limitations disclosed as follows
in this study.
The first limitation is on the selection criteria of investment assets on
improving risk-return rewards for investors. This is because investment assets
involved a broad area of selection, which could include derivatives, equities (both
95
public and private), fixed-income securities, futures, forwards and so on. Therefore, in
this study, only 5 investment assets categories: Thai SET stocks, Thai corporate
bonds, Gold, real-estate & Bitcoin assets are only selected in construction of the
optimal portfolios.
The second limitation is the type of investors targeted for constructing this
optimal portfolio. Based on investors’ risk-tolerance, & risk-taking ability such as
risk-averse, risk-seeking or risk-avoidance, there would be many different types of
optimal portfolio Construction to be taken. However, in this research study, this
research will only be focusing on investors who will all be assumed risk-averse (i.e.,
investors only expect more additional risk when only offer with additional expected
return). Considerations of irrational behavior by investors will be ignored, to limit the
scope of this thesis study. In other words, the application of behavior finance theory
will not be considered in this thesis study.
The third limitation is the buy and hold Strategy, i.e., passive investing is
assumed here, in the investment returns, without considering the rebalancing strategy.
The portfolio expected return E(Rp) and risk (σ) are Annualized. It is also assumed
that past market prices influenced investment returns and risk of future performance.
96
CHAPTER 4
EMPIRICAL RESULTS
This chapter presents the optimal portfolio research findings and hypothesis
conclusions from the data gathered in the above section. The constructed portfolios
will be analyzed and presented in 6 sections as follows:
4.1. Descriptive statistics
4.2. Correlation analysis
4.3. Efficient frontier and optimal portfolio: traditional investment portfolio
4.4. Efficient frontier and optimal portfolio: alternative investment portfolio with
high-risk assets
4.5. Comparison of the Optimal Portfolio
4.6. Hypothesis testing
97
4.1. Descriptive Statistics
Table 4.1: Descriptive Statistics of monthly return during 5-year period
Monthly
Return SET50
Corporate
Bonds (A-
up)
96.5%
Gold
Physical
Real
Estate
Real Estate
Funds
(PF&REIT)
Bitcoin
Mean 2.72% 0.22% 0.72% 0.26% 4.49% 2.62%
Standard
Deviation 5.37% 0.56% 2.64% 0.45% 4.97% 22.00%
Minimum -11.76% -1.55% -4.09% -0.66% -13.95% -37.32%
Maximum 24.85% 1.58% 6.83% 1.33% 19.23% 60.85%
In this section, the descriptive statistics of the investment portfolios are
revealed as follows. For a 5-year investment interval, the traditional investment assets
yield the average monthly return of 2.72% for SET50 index (stock), while 0.22% for
rating A- up corporate bonds (bonds). The highest monthly rate of return is 24.85%
for SET50 Index, and 1.58% for corporate bonds respectively. Meanwhile, the lowest
monthly rate of return is -11.76% for SET50 index, and -1.55% respectively.
However, the SET50 exhibits a moderate level of volatility of 5.37%, while bonds
only exhibit low volatility of 0.56%.
Meanwhile, the alternative assets yield the average monthly return of 0.72%
for 96.5% gold, 0.26% for physical real-estate, 4.49% for PF&REIT (property funds),
and 2.62% for Bitcoin. The highest monthly rate of return is 6.83% for 96.5% gold,
1.33% for physical real-estate, 19.23% for PF&REIT, and 60.85% for Bitcoin.
Meanwhile, the lowest monthly rate of return is -4.09% for 96.5% gold, -0.66% for
98
physical real-estate, and -13.95% for PF&REIT, and -37.32% for Bitcoin. However,
96.5% gold and physical real-estate exhibits a low volatility of 2.64% and 0.45%,
while PF&REIT exhibits moderate volatility of 4.97%, and Bitcoin, high volatility of
22%.
Table 4.2: Descriptive Statistics of monthly return during 10-year period
Monthly
Return SET50
Corporate
Bonds (A-
up)
96.5%
Gold
Physical
Real
Estate
Real Estate
Funds
(PF&REIT)
Bitcoin
Mean 3.13% 0.31% 0.20% 0.33% 4.70% 11.72%
Standard
Deviation 4.53% 0.49% 2.84% 0.49% 3.83% 50.85%
Minimum -11.76% -1.50% -7.47% -1.17% -13.95% -38.90%
Maximum 24.85% 1.60% 7.51% 1.45% 19.23% 470.90%
For 10-year investment interval, the traditional investment assets yield the
average monthly return of 3.13% for SET50 index (stock), while 0.31% for Rating A-
up corporate bonds (bonds). The highest monthly rate of return is 24.85% for SET50
index, and 1.60% for corporate bonds respectively. Meanwhile, the lowest monthly
rate of return is -11.76% for SET50 index, and -1.50% respectively. However, the
SET50 exhibits a moderate level of volatility of 4.53%, while Bonds only exhibit low
volatility of 0.49%.
Meanwhile, the alternative assets yield the average monthly return of 0.2% for
96.5% gold, 0.33% for physical real-estate, 4.70% for PF&REIT, and 11.72% for
Bitcoin. The highest monthly rate of return is 7.51% for 96.5% gold, 1.45% for
99
physical real-estate, 19.23% for PF&REIT, and 470.90% for Bitcoin. Meanwhile, the
lowest monthly rate of return is -7.47% for 96.5% gold, -1.17% for physical real-
estate, and -13.95% for PF&REIT, and -38.9% for Bitcoin. However, 96.5% gold and
physical real-estate exhibits a low volatility of 2.84% and 0.49%, while PF&REIT
exhibits moderate volatility of 3.83%, and Bitcoin, high volatility of 50.85%.
Rate of Return and Price chart of SET50 index over 5-year period
In the below section, the rate of return and price chart of SET50 index graph is
shown over a 5-year period. The most prominent price changes happen during the
Covid-19 pandemic; from the year 2019 to year 2021, where price returns rapidly
drop.
Figure 4.1: Monthly rate of return from SET50 index during 5-year period
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
SET50 Index Rate of Return
100
Figure 4.2: Monthly price data from SET50 index during 5-year period
Rate of Return and Price chart of SET50 index over 10-year period
In the below section, the rate of return and price chart of SET50 index graph is
shown over a 10-year period. Over the long-term period, extreme volatility only
occurred in macroeconomic shocks and exogeneous impacts. Other than those factors,
SET50 exhibits a normal range between the typical upper and lower bounds range.
Figure 4.3: Monthly rate of return from SET50 index during 10-year period
0
200
400
600
800
1000
1200
1400
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
SET50 Price Graph
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
SET50 Index Rate of Return
101
Figure 4.4: Monthly price data from SET50 index during 10-year period
Rate of Return and Price chart of Corporate Bond index over 5-year period
In this below section, the rate of return and price chart of corporate bond index
is shown over a 5-year period. The corporate bond price exhibits steady growth in
price, while exhibiting a few margins of volatility on the rate of return.
Figure 4.5: Monthly rate of return from rating A- corporate bonds during 5-year
period
0
200
400
600
800
1000
1200
1400
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
SET50 Price Graph
-2.0%
-1.5%
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
2.0%
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
Corporate Bond Rate of Return
102
Figure 4.6: Monthly price data from rating A- corporate bonds during 5-year period
Rate of Return and Price chart of Corporate Bond index over 10-year period
In this below section, the rate of return and price chart of corporate bond index
graph is shown over 10-year period. The bond price increases steadily over the long-
term horizon.
Figure 4.7: Monthly rate of return from rating A- corporate bonds during 10-year
period
165
170
175
180
185
190
195
200
205
210
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
Corporate Bonds Price Graph
-2%
-2%
-1%
-1%
0%
1%
1%
2%
2%
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
Corporate Bond Rate of Return
103
Figure 4.8: Monthly price data from rating A- corporate bonds during 10-year period
Rate of Return and Price chart of physical real-estate index over 5-year period
In this below section, the rate of return and price chart of physical real-estate
index graph is shown over 5-year period.
Figure 4.9: Monthly rate of return from physical real-estate during 5-year period
0
50
100
150
200
250
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
Corporate Bond Price Graph
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
Physical RealEstate Index Rate of Return
104
Figure 4.10: Monthly price index from physical real-estate during 5-year period
Rate of Return and Price chart of physical real-estate index over 10-year period
In this section, the rate of return and price chart of physical real-estate index
graph is shown over 10-year period.
Figure 4.11: Monthly rate of return from physical real-estate during 10-year period
120
125
130
135
140
145
150
155
160
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
Physical Real Estate Price Graph
-1.5%
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
2.0%
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
Physical RealEstate Index Rate of Return
105
Figure 4.12: Monthly price data from physical real-estate during 10-year period
Rate of Return and Price chart of 96.5% Gold over 5-year period
In this below section, the rate of return and price chart of 96.5% Gold is
shown over the 5-year investment interval as stated above.
Figure 4.13: Monthly rate of return from 96.5% gold during 5-year period
0
20
40
60
80
100
120
140
160
180
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
Physical Real Estate Index Price Graph
-6%
-4%
-2%
0%
2%
4%
6%
8%
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
96.5% Gold Rate of Return
106
Figure 4.14: Monthly price data from 96.5% Gold during 5-year period
Rate of Return and Price chart of 96.5% Gold over 10-year period
In this section, the rate of return and price chart of 96.5% Gold is shown over
10-year period. There is a significant increase of Gold price from the year 2019
onwards.
Figure 4.15: Monthly rate of return from 96.5% Gold during 10-year period
0
5000
10000
15000
20000
25000
30000
35000
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
96.5% Gold Price Chart
-10%
-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
96.5% Gold Rate of Return
107
Figure 4.16: Monthly price data from 96.5% Gold during 10-year period
Rate of Return and Price chart of PF&REIT over 5-year period
In this below section, the rate of return and price chart of PF&REIT is shown
over 5-year investment interval as below.
Figure 4.17: Monthly rate of return from real-estate funds (PF&REIT) during 5-year
period
0
5000
10000
15000
20000
25000
30000
35000
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
96.5% Gold Price Graph
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
Real Estate Funds (PF&REIT) Rate of Return
108
Figure 4.18: Monthly price data from real-estate funds (PF&REIT) during 5-year
period
Rate of Return and Price chart of PF&REIT over 10-year period
In this below section, the rate of return and price chart of PF&REIT is shown
over 10-year investment period.
Figure 4.19: Monthly rate of return from real-estate funds during 10-year period
0
50
100
150
200
250
300
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
PF&REIT Price Graph
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
Real Estate Funds (PF&REIT) Rate of Return
109
Figure 4.20: Monthly price data from real-estate funds (PF&REIT) during 10-year
period
Rate of Return and Price chart of Bitcoin over 5-year period
In this below section, the rate of return and price chart of Bitcoin is shown
over a 5-year period. Unlike other assets, Bitcoin exhibits extreme volatility and rate
of return over the previous 5-year period. Nevertheless, bitcoin still proved to be
valuable in improving the performance of optimal portfolios.
Figure 4.21: Monthly rate of return from Bitcoin during 5-year period
0
50
100
150
200
250
300
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
PF&REIT Index Price Graph
-60%
-40%
-20%
0%
20%
40%
60%
80%
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
Bitcoin Rate of Return
110
Figure 4.22: Monthly price data from Bitcoin during 5-year period
Rate of Return and Price chart of Bitcoin over 10-year period
In this section, the rate of return and price chart of Bitcoin is shown over a 10-
year period.
Figure 4.23: Monthly rate of return from Bitcoin during 10-year period
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
JAN 2018
MAR 2018
MAY 2018
JUL 2018
SEP 2018
NOV 2018
JAN 2019
MAR 2019
MAY 2019
JUL 2019
SEP 2019
NOV 2019
JAN 2020
MAR 2020
MAY 2020
JUL 2020
SEP 2020
NOV 2020
JAN 2021
MAR 2021
MAY 2021
JUL 2021
SEP 2021
NOV 2021
JAN 2022
MAR 2022
MAY 2022
JUL 2022
SEP 2022
NOV 2022
Bitcoin Price
-100%
0%
100%
200%
300%
400%
500%
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
Bitcoin Rate of Return
111
Figure 4.24: Monthly price data from Bitcoin during 10-year period
In this section, the following table shows the monthly rate of return for the
price indicators of 5 investment assets for a 5–year period, from 1st Jan 2018 – 31st
Dec 2022.
Table 4.3: Monthly Rate of Return for 5-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
DEC 2022 3.50% 0.89% 0.90% -0.45% 7.56% -3.65%
NOV 2022 3.83% 1.58% 1.04% -0.60% 4.19% -16.26%
OCT 2022 5.03% 0.36% 0.39% 1.28% 2.91% 5.53%
SEP 2022 -1.72% -1.55% -0.20% -1.42% 1.02% -3.10%
AUG 2022 6.29% 0.64% -0.33% -0.05% 7.37% -13.99%
0
10000
20000
30000
40000
50000
60000
70000
JAN 2013
MAY 2013
SEP 2013
JAN 2014
MAY 2014
SEP 2014
JAN 2015
MAY 2015
SEP 2015
JAN 2016
MAY 2016
SEP 2016
JAN 2017
MAY 2017
SEP 2017
JAN 2018
MAY 2018
SEP 2018
JAN 2019
MAY 2019
SEP 2019
JAN 2020
MAY 2020
SEP 2020
JAN 2021
MAY 2021
SEP 2021
JAN 2022
MAY 2022
SEP 2022
Bitcoin Price Graph
112
Table 4.3 (Continued): Monthly Rate of Return for 5-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
JUL 2022 3.28% 0.89% 0.59% -1.45% 3.05% 16.95%
JUN 2022 -2.49% -0.13% 1.33% 0.78% 2.15% -37.32%
MAY 2022 4.38% 0.24% 0.27% -2.61% 2.81% -15.56%
APR 2022 -1.00% -1.51% 0.20% 1.26% 3.02% -17.30%
MAR 2022 2.89% -0.42% -0.33% 6.64% 3.52% 5.41%
FEB 2022 4.91% 0.26% 0.74% 0.33% 10.20% 12.18%
JAN 2022 2.32% -0.16% 0.07% 0.40% -3.02% -16.70%
DEC 2021 7.97% 0.12% 0.34% -0.23% 3.95% -18.75%
NOV 2021 -1.92% 0.53% 0.41% 1.53% 0.47% -7.22%
OCT 2021 3.14% -0.18% 0.47% 0.91% 8.44% 39.90%
SEP 2021 -0.70% -0.62% 0.41% -0.44% 1.04% -7.02%
AUG 2021 10.99% 0.30% 0.55% 0.38% 7.66% 13.42%
JUL 2021 -2.30% 0.54% 0.69% 2.13% 0.43% 18.63%
JUN 2021 1.12% 0.69% 0.55% -0.06% 9.19% -6.09%
MAY 2021 2.73% 0.38% 0.21% 4.87% 4.90% -35.38%
APR 2021 0.49% 0.62% 0.00% 3.69% 2.07% -1.78%
MAR 2021 6.52% 0.43% -0.42% -2.40% 17.19% 30.11%
FEB 2021 3.76% -0.70% -0.28% -3.05% 0.97% 36.41%
JAN 2021 3.61% 0.23% -0.55% 0.35% 1.26% 14.37%
DEC 2020 3.29% 0.77% 0.21% -2.00% 4.59% 46.97%
NOV 2020 24.85% 0.60% 0.00% -4.09% 15.22% 42.77%
113
Table 4.3 (Continued): Monthly Rate of Return for 5-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
OCT 2020 -0.55% 0.34% 0.62% -1.41% -3.70% 28.04%
SEP 2020 -3.60% 0.41% 0.07% -1.78% 2.57% -7.46%
AUG 2020 1.42% -0.17% 0.00% 6.49% 6.06% 2.74%
JUL 2020 1.02% 0.23% 0.07% 6.69% 1.41% 24.06%
JUN 2020 2.25% 0.16% -0.28% -1.95% -0.55% -3.38%
MAY 2020 5.98% -0.75% 0.42% 1.31% 9.91% 9.57%
APR 2020 18.96% 0.10% 0.49% 6.83% 19.23% 34.57%
MAR 2020 -11.76% -1.01% 0.42% 1.80% -13.95% -24.94%
FEB 2020 -8.43% 1.02% 0.85% 5.55% -2.74% -8.61%
JAN 2020 -1.67% 0.79% 1.00% 5.85% 6.65% 29.91%
DEC 2019 2.70% 0.58% 0.50% 0.44% 1.38% -4.64%
NOV 2019 2.58% 0.29% 0.79% -1.83% 0.83% -17.55%
OCT 2019 1.30% 0.10% 0.95% -1.59% 1.67% 10.48%
SEP 2019 2.32% 0.05% 0.73% 0.14% 7.73% -13.65%
AUG 2019 -0.15% 1.25% -0.15% 5.79% 8.42% -4.84%
JUL 2019 1.08% 0.78% -0.66% 3.11% 3.65% -6.81%
JUN 2019 9.86% 0.79% -0.51% 3.41% 12.75% 26.41%
MAY 2019 -0.37% 0.52% -0.36% -0.48% 5.61% 60.85%
APR 2019 4.87% 0.27% 0.22% -0.69% 5.75% 29.70%
MAR 2019 2.19% 0.44% -0.07% -0.01% 10.56% 7.49%
FEB 2019 3.17% 0.08% 0.29% 0.48% 5.29% 11.04%
JAN 2019 7.91% 0.27% -0.29% 0.47% 5.58% -7.34%
114
Table 4.3 (Continued): Monthly Rate of Return for 5-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
DEC 2018 -1.11% 0.56% 0.66% 1.61% 3.00% -8.18%
NOV 2018 2.01% 0.51% -0.29% 1.03% 3.65% -36.54%
OCT 2018 -1.89% -0.01% 0.00% 1.82% 1.74% -4.06%
SEP 2018 4.58% -0.04% 0.00% -1.68% 5.96% -5.67%
AUG 2018 3.87% 0.07% 0.44% -3.52% 7.39% -9.00%
JUL 2018 10.12% 0.06% 0.73% -0.90% 6.22% 20.79%
JUN 2018 -4.18% 0.25% 0.52% -0.12% 2.07% -14.71%
MAY 2018 -0.67% -0.33% 0.22% -0.24% 4.56% -18.85%
APR 2018 3.27% 0.04% -0.07% 0.70% 5.28% 33.25%
MAR 2018 0.03% 0.27% -0.29% -1.22% 3.96% -32.86%
FEB 2018 4.36% 0.18% 0.82% -1.10% 4.52% 0.67%
JAN 2018 7.07% 0.54% 0.52% 2.53% 5.06% -25.88%
In this section, the following table shows the monthly rate of return for the
price indicators of 5 investment assets for a 10–year period, from 1st Jan 2013 – 31st
Dec 2022.
115
Table 4.4: Monthly Rate of Return for 10-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
DEC 2022 3.50% 0.89% 0.90% -0.45% 7.56% -3.65%
NOV 2022 3.83% 1.58% 1.04% -0.60% 4.19% -16.26%
OCT 2022 5.03% 0.36% 0.39% 1.28% 2.91% 5.53%
SEP 2022 -1.72% -1.55% -0.20% -1.42% 1.02% -3.10%
AUG 2022 6.29% 0.64% -0.33% -0.05% 7.37% -13.99%
JUL 2022 3.28% 0.89% 0.59% -1.45% 3.05% 16.95%
JUN 2022 -2.49% -0.13% 1.33% 0.78% 2.15% -37.32%
MAY 2022 4.38% 0.24% 0.27% -2.61% 2.81% -15.56%
APR 2022 -1.00% -1.51% 0.20% 1.26% 3.02% -17.30%
MAR 2022 2.89% -0.42% -0.33% 6.64% 3.52% 5.41%
FEB 2022 4.91% 0.26% 0.74% 0.33% 10.20% 12.18%
JAN 2022 2.32% -0.16% 0.07% 0.40% -3.02% -16.70%
DEC 2021 7.97% 0.12% 0.34% -0.23% 3.95% -18.75%
NOV 2021 -1.92% 0.53% 0.41% 1.53% 0.47% -7.22%
OCT 2021 3.14% -0.18% 0.47% 0.91% 8.44% 39.90%
SEP 2021 -0.70% -0.62% 0.41% -0.44% 1.04% -7.02%
AUG 2021 10.99% 0.30% 0.55% 0.38% 7.66% 13.42%
JUL 2021 -2.30% 0.54% 0.69% 2.13% 0.43% 18.63%
JUN 2021 1.12% 0.69% 0.55% -0.06% 9.19% -6.09%
116
Table 4.4 (Continued): Monthly Rate of Return for 10-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
MAY 2021 2.73% 0.38% 0.21% 4.87% 4.90% -35.38%
APR 2021 0.49% 0.62% 0.00% 3.69% 2.07% -1.78%
MAR 2021 6.52% 0.43% -0.42% -2.40% 17.19% 30.11%
FEB 2021 3.76% -0.70% -0.28% -3.05% 0.97% 36.41%
JAN 2021 3.61% 0.23% -0.55% 0.35% 1.26% 14.37%
DEC 2020 3.29% 0.77% 0.21% -2.00% 4.59% 46.97%
NOV 2020 24.85% 0.60% 0.00% -4.09% 15.22% 42.77%
OCT 2020 -0.55% 0.34% 0.62% -1.41% -3.70% 28.04%
SEP 2020 -3.60% 0.41% 0.07% -1.78% 2.57% -7.46%
AUG 2020 1.42% -0.17% 0.00% 6.49% 6.06% 2.74%
JUL 2020 1.02% 0.23% 0.07% 6.69% 1.41% 24.06%
JUN 2020 2.25% 0.16% -0.28% -1.95% -0.55% -3.38%
MAY 2020 5.98% -0.75% 0.42% 1.31% 9.91% 9.57%
APR 2020 18.96% 0.10% 0.49% 6.83% 19.23% 34.57%
MAR 2020 -11.76% -1.01% 0.42% 1.80% -13.95% -24.94%
FEB 2020 -8.43% 1.02% 0.85% 5.55% -2.74% -8.61%
JAN 2020 -1.67% 0.79% 1.00% 5.85% 6.65% 29.91%
DEC 2019 2.70% 0.58% 0.50% 0.44% 1.38% -4.64%
NOV 2019 2.58% 0.29% 0.79% -1.83% 0.83% -17.55%
117
Table 4.4 (Continued): Monthly Rate of Return for 10-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
OCT 2019 1.30% 0.10% 0.95% -1.59% 1.67% 10.48%
SEP 2019 2.32% 0.05% 0.73% 0.14% 7.73% -13.65%
AUG 2019 -0.15% 1.25% -0.15% 5.79% 8.42% -4.84%
JUL 2019 1.08% 0.78% -0.66% 3.11% 3.65% -6.81%
JUN 2019 9.86% 0.79% -0.51% 3.41% 12.75% 26.41%
MAY 2019 -0.37% 0.52% -0.36% -0.48% 5.61% 60.85%
APR 2019 4.87% 0.27% 0.22% -0.69% 5.75% 29.70%
MAR 2019 2.19% 0.44% -0.07% -0.01% 10.56% 7.49%
FEB 2019 3.17% 0.08% 0.29% 0.48% 5.29% 11.04%
JAN 2019 7.91% 0.27% -0.29% 0.47% 5.58% -7.34%
DEC 2018 -1.11% 0.56% 0.66% 1.61% 3.00% -8.18%
NOV 2018 2.01% 0.51% -0.29% 1.03% 3.65% -36.54%
OCT 2018 -1.89% -0.01% 0.00% 1.82% 1.74% -4.06%
SEP 2018 4.58% -0.04% 0.00% -1.68% 5.96% -5.67%
AUG 2018 3.87% 0.07% 0.44% -3.52% 7.39% -9.00%
JUL 2018 10.12% 0.06% 0.73% -0.90% 6.22% 20.79%
JUN 2018 -4.18% 0.25% 0.52% -0.12% 2.07% -14.71%
MAY 2018 -0.67% -0.33% 0.22% -0.24% 4.56% -18.85%
APR 2018 3.27% 0.04% -0.07% 0.70% 5.28% 33.25%
118
Table 4.4 (Continued): Monthly Rate of Return for 10-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
MAR 2018 0.03% 0.27% -0.29% -1.22% 3.96% -32.86%
FEB 2018 4.36% 0.18% 0.82% -1.10% 4.52% 0.67%
JAN 2018 7.07% 0.54% 0.52% 2.53% 5.06% -25.88%
DEC 2017 7.31% 0.32% 1.21% -1.93% 3.92% 39.24%
NOV 2017 2.07% 0.20% 0.23% -0.84% 5.15% 54.19%
OCT 2017 5.28% 0.34% 0.92% -2.31% 5.88% 47.94%
SEP 2017 6.28% 0.42% 0.62% 2.34% 8.83% -7.91%
AUG 2017 6.05% 0.66% 0.31% 2.03% 7.24% 64.23%
JUL 2017 4.10% 0.50% 0.47% -2.75% 10.39% 16.23%
JUN 2017 4.08% 0.56% 0.23% -0.09% 3.22% 7.70%
MAY 2017 2.47% 0.44% 1.02% -1.59% 5.05% 70.38%
APR 2017 3.03% 0.29% 0.71% 1.68% 2.60% 25.28%
MAR 2017 5.11% 0.36% 0.16% -0.51% 3.76% -9.27%
FEB 2017 2.54% 0.39% -0.16% 2.04% 4.57% 23.18%
JAN 2017 5.10% 0.21% -0.63% 2.47% 5.30% 0.22%
DEC 2016 5.46% 0.02% 0.55% -5.73% 2.79% 29.75%
NOV 2016 3.94% -0.39% 0.24% -1.60% 4.49% 6.27%
OCT 2016 2.44% -0.02% -0.16% -3.37% 3.85% 14.90%
SEP 2016 -0.72% 0.38% -0.39% -0.97% 5.92% 5.96%
119
Table 4.4 (Continued): Monthly Rate of Return for 10-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
AUG 2016 5.06% 0.00% -1.17% -0.79% 5.41% -7.72%
JUL 2016 9.09% 0.12% -0.70% 4.32% 5.93% -7.18%
JUN 2016 3.49% 0.96% -0.08% 0.85% 7.10% 26.68%
MAY 2016 5.71% -0.97% 0.31% 2.24% 1.18% 17.93%
APR 2016 2.05% -0.01% 0.86% -0.49% 8.82% 7.89%
MAR 2016 10.02% 0.98% -0.08% 2.91% 8.20% -4.70%
FEB 2016 6.67% 0.67% 0.00% 7.51% 8.57% 17.96%
JAN 2016 5.29% 0.76% 0.08% 2.66% 6.16% -14.00%
DEC 2015 -2.65% 0.44% 0.47% -1.15% 5.38% 13.76%
NOV 2015 0.12% 0.31% 0.79% -5.48% 4.85% 21.47%
OCT 2015 6.90% 0.52% 0.08% 1.90% 6.57% 31.92%
SEP 2015 -0.58% 0.32% 0.48% 2.29% 3.30% 2.79%
AUG 2015 -1.35% 0.24% 0.00% 1.61% 4.49% -19.10%
JUL 2015 -0.72% 0.69% -0.16% -2.36% 5.71% 7.42%
JUN 2015 3.50% 0.10% -0.08% -0.70% 9.26% 14.93%
MAY 2015 1.06% 0.10% 0.24% 3.02% 5.48% -2.54%
APR 2015 3.91% 0.93% 1.05% 1.22% 5.41% -3.40%
MAR 2015 -1.27% 0.72% 0.49% -3.84% 4.71% -3.94%
FEB 2015 2.20% 0.10% 0.32% -2.09% 4.58% 16.29%
120
Table 4.4 (Continued): Monthly Rate of Return for 10-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
JAN 2015 7.78% 0.80% -0.08% 3.72% 3.85% -31.33%
DEC 2014 -3.28% 0.35% 0.57% 2.20% 3.73% -15.12%
NOV 2014 4.45% 1.19% 0.49% -2.79% 3.36% 10.95%
OCT 2014 2.59% 0.96% 0.58% -0.61% 2.57% -12.96%
SEP 2014 4.20% 0.56% 0.08% -3.62% 2.89% -19.43%
AUG 2014 6.71% 0.56% 0.41% -1.57% 2.97% -18.27%
JUL 2014 3.88% 0.59% 0.50% 1.30% 6.44% -7.18%
JUN 2014 8.12% 0.29% 1.26% -0.81% 7.05% 1.15%
MAY 2014 2.53% 0.13% 0.76% -0.05% 3.68% 40.91%
APR 2014 5.77% 0.65% 0.86% -3.08% 5.75% 0.20%
MAR 2014 7.33% 0.40% 0.43% 2.32% 4.35% -22.51%
FEB 2014 7.21% 0.84% 0.69% 3.26% 5.52% -38.87%
JAN 2014 1.56% 0.57% 0.70% 3.49% 2.61% 16.49%
DEC 2013 -2.18% 0.63% -0.09% -2.18% 3.96% -33.16%
NOV 2013 -2.12% 0.29% 1.06% -1.35% 3.33% 470.88%
OCT 2013 7.88% 0.54% 1.34% -4.06% 4.63% 48.84%
SEP 2013 9.67% 0.88% 1.45% 0.13% 7.71% 0.64%
AUG 2013 -5.28% -0.23% 0.55% 6.73% -0.51% 32.77%
JUL 2013 1.30% -0.04% -0.18% -3.39% 3.45% 8.92%
121
Table 4.4 (Continued): Monthly Rate of Return for 10-year duration
Month SET50
Index
Corporate
Bond
Physical
RealEstate
Index
96.5%
Gold
Real Estate
Funds
(PF&REIT)
Bitcoin
JUN 2013 -1.45% -0.39% 0.27% -1.84% 0.05% -24.30%
MAY 2013 0.36% 0.33% 0.55% -3.02% 4.36% -7.47%
APR 2013 5.80% 0.64% 1.39% -7.47% 2.92% 49.68%
MAR 2013 3.91% 0.49% 0.56% -3.56% 3.11% 178.44%
FEB 2013 5.37% 0.57% 0.56% -3.10% 5.13% 63.73%
JAN 2013 7.52% 0.16% 0.38% -2.70% 7.82% 51.11%
4.2. Correlation Analysis
In this section, the correlation, and the covariance table of the investment
assets are listed below.
The correlation & covariance table of the total assets are described below over
the 5-year period.
122
Table 4.5: Correlation table for the selected total assets for 5-year duration
(Correlation
Matrix)
SET50
Index
Corporate
Bonds
96.5%
Gold
Physical
Real
Estate
Index
Real Estate
Funds
(PF&REIT)
Bitcoin
SET50
Index 1.0000 0.1517 -0.1626 -0.1331 0.7238 0.3700
MTM
Corporate
Bonds
0.1517 1.0000 0.1018 0.1198 0.2478 0.0931
96.% Gold -0.1626 0.1018 1.0000 -0.0266 0.0203 -0.0509
Physical
Real Estate
Index
-0.1331 0.1198 -0.0266 1.0000 -0.1216 -0.1400
Real Estate
Funds
(PF&REIT)
0.7238 0.2478 0.0203 -0.1216 1.0000 0.3719
Bitcoin 0.3700 0.0931 -0.0509 -0.1400 0.3719 1.0000
Table 4.6: Covariance table for the selected total assets for 5-year duration
(Covariance
Matrix)
SET50
Index
Corporate
Bonds
96.5%
Gold
Physical
Real Estate
Index
Real Estate
Funds
(PF&REIT)
Bitcoin
SET50
Index 0.0028832 0.0000459 -0.0002302 -0.0000322 0.0019322 0.0043709
Corporate
Bonds 0.0000459 0.0000317 0.0000151 0.0000030 0.0000694 0.0001153
96.% Gold -0.0002302 0.0000151 0.0006954 -0.0000032 0.0000266 -0.0002951
Physical
Real Estate
Index
-0.0000322 0.0000030 -0.0000032 0.0000203 -0.0000272 -0.0001387
Real Estate
Funds
(PF&REIT)
0.0019322 0.0000694 0.0000266 -0.0000272 0.0024719 0.0040675
Bitcoin 0.0043709 0.0001153 -0.0002951 -0.0001387 0.0040675 0.0483933
123
In this section, the correlation between each asset over the 5-year period will
be explained below. SET50 index exhibits a positive correlation with PF&REIT
(+0.73), Bonds (+0.15) and Bitcoin (+0.37), while exhibiting the negative correlation
with 96.5% Gold (-0.16), and Physical Real-estate Index (-0.13).
Corporate Bonds Asset exhibits a positive correlation with SET50 (+0.15),
Gold (+0.10), Physical real-estate Index (+0.12), PF&REIT (+0.25), and Bitcoin
(+0.09).
96.5% Gold Asset exhibits a positive correlation with Corporate Bonds
(+0.10), PF&REIT (+0.02), while exhibiting a negative correlation with SET50 (-
0.16), Physical real-estate index (-0.027), and Bitcoin (-0.05).
Physical real-estate index exhibits a positive correlation with Corporate Bonds
(+0.12), while exhibiting a negative correlation with SET50 (-0.13), 96.5% Gold (-
0.027), PF&REIT (-0.122), and Bitcoin (-0.14). In fact, the physical real-estate index
represents the physical property market nationwide, based on the official sources of
Bank of Thailand.
PF&REIT exhibits a positive correlation with SET50 (+0.72), Corporate
Bonds (+0.25), 96.5% Gold (+0.02), and Bitcoin (+0.37), while exhibiting a negative
correlation with the physical real-estate index (-0.12). The PF&REIT represents the
REIT share market, trading on the SET Stock Exchange.
Bitcoin exhibits a positive correlation with SET50 (+0.37), Corporate Bonds
(+0.093), PF&REIT (+0.371), while exhibiting a negative correlation with 96.5%
Gold (-0.05), and physical real-estate index (-0.14).
124
Table 4.7: Correlation table for the selected total assets for 10-year duration
(Correlation
Matrix)
SET50
Index
Corporate
Bonds
96.5%
Gold
Physical
Real
Estate
Index
Real Estate
Funds
(PF&REIT)
Bitcoin
SET50 Index 1.0000 0.1910 -0.0779 -0.0316 0.6456 0.0339
MTM
Corporate
Bonds
0.1910 1.0000 0.0276 0.1559 0.2617 0.0224
96.5% Gold -0.0779 0.0276 1.0000 -0.1896 0.0340 -0.1645
Physical
Real Estate
Index
-0.0316 0.1559 -0.1896 1.0000 -0.0880 0.1882
Real Estate
Funds
(PF&REIT)
0.6456 0.2617 0.0340 -0.0880 1.0000 0.0816
Bitcoin 0.0339 0.0224 -0.1645 0.1882 0.0816 1.0000
Table 4.8: Covariance table for the selected total assets for 10-year duration
(Covariance
Matrix)
SET50
Index
Corporate
Bonds 96.5% Gold
Physical Real
Estate
Index
Real Estate
Funds
(PF&REIT)
Bitcoin
SET50
Index 0.0020499 0.0000419 -0.0001003 -0.0000070 0.0011196 0.0007800
Corporate
Bonds 0.0000419 0.0000235 0.0000038 0.0000037 0.0000486 0.0000551
96.5% Gold -0.0001003 0.0000038 0.0008084 -0.0000262 0.0000371 -0.0023781
Physical
Real Estate
Index
-0.0000070 0.0000037 -0.0000262 0.0000236 -0.0000164 0.0004654
Real Estate
Funds
(PF&REIT)
0.0011196 0.0000486 0.0000371 -0.0000164 0.0014672 0.0015894
Bitcoin 0.0007800 0.0000551 -0.0023781 0.0004654 0.0015894 0.2585367
125
In this section, the correlation between each asset over the 10-year period will
be explained below. SET50 Index exhibits a positive correlation with PF&REIT
(+0.65), Bonds (+0.19) and Bitcoin (+0.034), while exhibiting the negative correlation
with 96.5% Gold (-0.07), and Physical Real-estate Index (-0.032).
Corporate Bonds Asset exhibits a positive correlation with SET50 (+0.191),
96.5% Gold (+0.03), Physical real-estate Index (+0.16), PF&REIT (+0.26), and
Bitcoin (+0.02).
96.5% Gold exhibits a positive correlation with Corporate Bonds (+0.03),
PF&REIT (+0.034), while exhibiting a negative correlation with SET50 (-0.078),
Physical real-estate (-0.19), and Bitcoin (-0.16).
Physical real-estate exhibits a positive correlation with Corporate Bonds
(+0.16) and Bitcoin (+0.19), while exhibiting a negative correlation with SET50 (-
0.03), 96.5% Gold (-0.19), PF&REIT (-0.09).
PF&REIT exhibits a positive correlation with SET50 (+0.65), Corporate
Bonds (+0.26), 96.5% Gold (+0.03), and Bitcoin (+0.08), while exhibiting a negative
correlation with the physical real-estate index (-0.09).
Bitcoin exhibits a positive correlation with SET50 (+0.03), Corporate Bonds
(+0.02), physical real-estate index (0.19), PF&REIT (+0.08), while exhibiting a
negative correlation with 96.5% Gold (-0.16).
126
4.3. Efficient frontier and Optimal portfolio: Traditional portfolio
In this section, the traditional portfolios result will be listed, which is
composed of only stocks (SET50) and bonds (corporate bonds, rating A- up).
Three types of portfolios can be greatly observed as follows:
1. Traditional portfolio 1 with the highest return
2. Traditional portfolio 2 with the lowest risk (standard deviation)
3. Traditional portfolio 3 with the highest risk-adjusted return (Sharpe ratio)
1. Traditional Portfolio 1 with the highest return
In this traditional portfolio, for the highest return objective, the results are
exhibited as follows for a 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 100% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds).
The rate of return for this portfolio is 38.62% and the risk (standard deviation)
of this portfolio is 18.6%. The Sharpe ratio is 1.916.
The efficient frontier on this portfolio is illustrated as follows:
127
Figure 4.25: Efficient frontier and highest expected return of traditional portfolio 1 for
5-year duration
For the 10-Year period, the weight allocations for highest return portfolio
resulted in 100% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds).
The rate of return for this portfolio is 45.63% and the risk (standard deviation)
of this portfolio is 15.6%. The Sharpe ratio is 2.72.
The efficient frontier on this portfolio is illustrated as follows:
2-Assets Portfolio (SET50, Corporate Bonds)
128
Figure 4.26: Efficient frontier and highest expected return of traditional portfolio 1 for
10-year duration
2. Traditional Portfolio 2 with the lowest risk
In this traditional portfolio, for the lowest risk objective, the results are
exhibited as follows for a 5-year and 10-year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 0.96% weight on stocks (SET50) and 99.04% allocation on bonds (Rating
2-Assets Portfolio (SET50, Corporate Bonds)
129
A- up corporate bonds).
The rate of return for this portfolio is 3.073% and the risk (standard deviation)
of this portfolio is 1.94%. The Sharpe ratio is 0.051. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.27: Efficient frontier and lowest risk of traditional portfolio 2 for 5-year
duration
For the 10-year period, the weight allocations for highest return portfolio
resulted in 0.97% weight on stocks (SET50) and 99.04% allocation on bonds (rating
A- up corporate bonds).
2-Assets Portfolio (SET50, Corporate Bonds)
130
The rate of return for this portfolio is 4.16% and the risk (standard deviation)
of this portfolio is 1.67%. The Sharpe ratio is 0.71.
The efficient frontier on this portfolio is illustrated as follows:
Figure 4.28: Efficient frontier and lowest risk of traditional portfolio 2 for 10-
year duration
3. Traditional Portfolio 3 with the highest risk-adjusted return (Sharpe ratio)
In this traditional portfolio, for the highest risk-adjusted return objective, the
results are exhibited as follows for a 5-year period and 10-year period.
2-Assets Portfolio (SET50, Corporate Bonds)
131
For the 5-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 100% weight on stocks (SET50) and 0% allocation on bonds
(rating A- up corporate bonds).
The rate of return for this portfolio is 38.62% and the risk (standard deviation)
of this Portfolio is 18.60%. The Sharpe ratio is 1.916. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.29: Efficient frontier and highest risk-adjusted return of traditional portfolio
3 for 5-year duration
2-Assets Portfolio (SET50, Corporate Bonds)
132
For the 10-Year Period, the weight allocations for highest risk-adjusted return
portfolio resulted in 40.77% weight on stocks (SET50) and 59.23% allocation on
bonds (rating A- up corporate bonds).
The rate of return for this portfolio is 20.83% and the risk (standard deviation)
of this portfolio is 6.49%. The Sharpe ratio is 2.75. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.30: Efficient frontier and highest risk-adjusted return of traditional portfolio
3 for 10-year duration
2-Assets Portfolio (SET50, Corporate Bonds)
133
4.4. Efficient frontier and optimal portfolio: alternative portfolio with high-risk asset
In this section, the alternative portfolios with high-risk assets will be listed as
follows:
7 types of portfolios can be greatly observed as follows:
1. 3-Assets portfolio with stocks + bonds + physical real-estate
2. 3-Assets portfolio with stocks + bonds + real-estate funds (PF&REIT)
3. 3-Assets portfolio with stocks + bonds + gold
4. 4-Assets portfolio with stocks + bonds + gold + physical real-estate
5. 4-Assets portfolio with stocks + bonds + gold + PF&REIT
6. 5-Assets portfolio with stocks + bonds + gold + physical real-estate + bitcoin
7. 5-Assets portfolio with stocks + bonds + gold + PF&REIT + bitcoin
134
1. 3-Assets Portfolio with stocks + bonds + physical real-estate
In this alternative portfolio, for the highest return objective, the results are
exhibited as follows for a 5-year and 10-year period.
For the 5-year period, the weight allocations for the highest return portfolio
resulted in 100% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 0% in physical real-estate.
The rate of return for this portfolio is 38.62% and the risk (standard deviation)
of this portfolio is 18.6%. The Sharpe ratio is 1.916. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.31: Efficient frontier and highest return of 3-assets portfolio type-3 on 5-year
duration
3-Assets Portfolio (SET50, Corporate Bonds, Physical real-estate)
135
For the 10-year period, the weight allocations for the highest return portfolio
resulted in 100% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 0% allocation on physical real estate.
The rate of return for this portfolio is 45.63% and the risk (standard deviation)
of this portfolio is 15.69%. The Sharpe ratio is 2.72. The efficient frontier on this
Portfolio is illustrated as follows:
Figure 4.32: Efficient frontier and highest return of 3-assets portfolio type-3 on 10-
year duration
3-Assets Portfolio (SET50, Corporate Bonds, Physical real-estate)
136
In this alternative portfolio, for the lowest risk objective, the results are
exhibited as follows for a 5-year period and 10-year period.
For the 5-year period, the weight allocations for lowest risk portfolio resulted
in 0.437% weight on stocks (SET50) and 38.66% allocation on bonds (rating A- up
corporate bonds) and 60.9% in physical real-estate.
The rate of return for this portfolio is 3.157% and the risk (standard deviation)
of this portfolio is 1.221%. The Sharpe ratio is 0.149. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.33: Efficient frontier and lowest risk of 3-assets portfolio type-3 on 5-year
duration
3-Assets Portfolio (SET50, Corporate Bonds, Physical real-estate)
137
For the 10-year period, the weight allocations for lowest risk portfolio resulted
in 0.509% weight on stocks (SET50) and 49.9% allocation on bonds (rating A- up
corporate bonds) and 49.6% in physical real-estate.
The rate of return for this portfolio is 4.07% and the risk (standard deviation) of
this portfolio is 1.19%. The Sharpe ratio is 0.92. The efficient frontier on this portfolio
is illustrated as follows:
Figure 4.34: Efficient frontier and lowest risk of 3-assets portfolio type-3 on 10-year
duration
3-Assets Portfolio (SET50, Corporate Bonds, Physical real-estate)
138
In this alternative portfolio, for the highest risk-adjusted return objective, the
results are exhibited as follows for a 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest risk-adjusted return portfolio
resulted in 51.89% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 48.11% in physical real-estate.
The rate of return for this portfolio is 21.6% and the risk (standard deviation) of
this portfolio is 9.67%. The Sharpe ratio is 1.92. The efficient frontier on this portfolio
is illustrated as follows:
Figure 4.35: Efficient frontier and highest risk-adjusted return of 3-assets
portfolio type-3 on 5-year duration
3-Assets Portfolio (SET50, Corporate Bonds, Physical real-estate)
139
For the 10-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 22.42% weight on stocks (SET50) and 31.9% allocation on bonds
(rating A- up corporate bonds) and 45.622% in physical real-estate.
The rate of return for this portfolio is 13.243% and the risk (standard deviation)
of this Portfolio is 3.65%. The Sharpe ratio is 2.82. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.36: Efficient frontier and highest risk-adjusted return of 3-assets
portfolio type-3 on 10-year duration
3-Assets Portfolio (SET50, Corporate Bonds, Physical real-estate)
140
2. 3-Assets Portfolio with stocks + bonds + real-estate funds (PF&REIT)
In this alternative portfolio, for the highest return objective, the results are
exhibited as follows for the 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 0% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 100% in real-estate funds (PF&REIT).
The rate of return for this portfolio is 71.5% and the risk (standard deviation)
of this portfolio is 17.2%. The Sharpe ratio is 3.98. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.37: Efficient frontier and highest return of 3-assets portfolio type-2 on
5-year duration
3-Assets Portfolio (SET50, Corporate Bonds, real-estate funds)
141
For the 10-year period, the weight allocations for highest return portfolio
resulted in 0% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 100% in real-estate funds (PF&REIT).
The rate of return for this portfolio is 75.9% and the risk (standard deviation)
of this portfolio is 13.3%. The Sharpe ratio is 5.49. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.38: Efficient frontier and highest return of 3-assets portfolio type-2 on
10-year duration
3-Assets Portfolio (SET50, Corporate Bonds, real-estate funds)
142
In this alternative portfolio, for the lowest risk objective, the results are
exhibited as follows for 5-year period and 10-year period.
For the 5-year period, the weight allocations for lowest risk portfolio resulted
in 0.9% weight on stocks (SET50) and 98.13% allocation on bonds (rating A- up
corporate bonds) and 0.98% in real-estate funds (PF&REIT).
The rate of return for this portfolio is 3.7% and the risk (standard deviation) of
this Portfolio is 1.94%. The Sharpe ratio is 0.39. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.39: Efficient frontier and lowest risk of 3-assets portfolio type-2 on 5-year
duration
3-Assets Portfolio (SET50, Corporate Bonds, real-estate funds)
143
For the 10-year period, the weight allocations for lowest risk portfolio resulted
in 0.9% weight on stocks (SET50) and 97.9% allocation on bonds (rating A- up
corporate bonds) and 1.245% in real-estate funds (PF&REIT).
The rate of return for this portfolio is 5.03% and the risk (standard deviation)
of this portfolio is 167%. The Sharpe ratio is 1.24. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.40: Efficient frontier and lowest risk of 3-assets portfolio type-2 on 10-year
duration
3-Assets Portfolio (SET50, Corporate Bonds, real-estate funds)
144
In this alternative portfolio, for the highest risk-adjusted return objective, the
results are exhibited as follows for a 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 28.67% weight on stocks (SET50) and 0% allocation on bonds (rating A-
up corporate bonds) and 71.3% in real-estate funds (PF&REIT).
The rate of return for this portfolio is 62% and the risk (standard deviation) of
this portfolio is 13.7%. The Sharpe ratio is 4.32. The efficient frontier on this portfolio
is illustrated as follows:
Figure 4.41: Efficient frontier and highest risk-adjusted return of 3-assets portfolio
type-2 on 5-year duration
3-Assets Portfolio (SET50, Corporate Bonds, real-estate funds)
145
For the 10-Year Period, the weight allocations for highest return portfolio
resulted in 20.83% weight on stocks (SET50) and 24.84% allocation on bonds (rating
A- up corporate bonds) and 54.33% in real-estate funds (PF&REIT).
The rate of return for this portfolio is 51.65% and the risk (standard deviation)
of this Portfolio is 8.1%. The Sharpe ratio is 6.01. The efficient frontier on this portfolio
is illustrated as follows:
Figure 4.42: Efficient frontier and highest risk-adjusted return of 3-assets portfolio
type-2 on 10-year duration
3-Assets Portfolio (SET50, Corporate Bonds, real-estate funds)
146
3. 3-Assets Portfolio with stocks + bonds + gold
In this alternative portfolio, for the highest return objective, the results are
exhibited as follows for a 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 100% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 0 % in 96.5% gold.
The rate of return for this portfolio is 38.62% and the risk (standard deviation)
of this portfolio is 18.6 %. The Sharpe ratio is 1.92. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.43: Efficient frontier and highest return of 3-assets portfolio type-1 on 5-year
duration
3-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold)
147
For the 10-year period, the weight allocations for highest return portfolio
resulted in 40.8 % weight on stocks (SET50) and 59.2 % allocation on bonds (rating
A- up corporate bonds) and 0% in 96.5% gold.
The rate of return for this portfolio is 20.8 % and the risk (standard deviation)
of this Portfolio is 6.49 %. The Sharpe ratio is 2.75. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.44: Efficient frontier and highest return of 3-assets portfolio type-1 on 10-
year duration
3-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold)
148
In this alternative portfolio, for the lowest risk objective, the results are
exhibited as follows for 5-year period and 10-year period.
For the 5-year period, the weight allocations for lowest risk portfolio resulted
in 0.95% weight on stocks (SET50) and 94.86 % allocation on bonds (rating A- up
corporate bonds) and 4.19 % in 96.5% gold.
The rate of return for this portfolio is 3.3 % and the risk (standard deviation)
of this portfolio is 1.9 %. The Sharpe ratio is 0.19. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.45: Efficient frontier and lowest risk of 3-assets portfolio type-1 on 5-
year duration
3-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold)
149
For the 10-year period, the weight allocations for lowest risk portfolio resulted
in 0.95% weight on stocks (SET50) and 96.27% allocation on bonds (rating A- up
corporate bonds) and 2.77% in 96.5% gold.
The rate of return for this portfolio is 4.12 % and the risk (standard deviation)
of this portfolio is 1.65 %. The Sharpe ratio is 0.69. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.46: Efficient frontier and lowest risk of 3-assets portfolio type-1 on 10-year
duration
3-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold)
150
In this alternative portfolio, for the highest risk-adjusted return objective, the
results are exhibited as follows for 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 57.9% weight on stocks (SET50) and 0% allocation on bonds
(rating A- up corporate bonds) and 42.14% in 96.5% gold.
The rate of return for this portfolio is 26.15% and the risk (standard deviation)
of this portfolio is 11.38%. The Sharpe ratio is 2.04. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.47: Efficient frontier and highest risk-adjusted return of 3-assets
portfolio type-1 on 5-year duration
3-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold)
151
For the 10-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 40.77% weight on stocks (SET50) and 59.23% allocation on
bonds (rating A- up corporate bonds) and 0% in 96.5% gold.
The rate of return for this portfolio is 20.83% and the risk (standard deviation)
of this portfolio is 6.49%. The Sharpe ratio is 2.75. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.48: Efficient frontier and highest risk-adjusted return of 3-assets
portfolio type-1 on 10-year duration
3-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold)
152
4. 4-Assets Portfolio with stocks + bonds + gold + physical real-estate
In this alternative portfolio, for the highest return objective, the results are
exhibited as follows for 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 100% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds), 0% in 96.5% gold, and 0% in physical real-estate.
The rate of return for this portfolio is 38.62% and the risk (standard deviation)
of this portfolio is 18.6%. The Sharpe ratio is 1.92. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.49: Efficient frontier and highest return of 4-assets portfolio type-5 on
5-year duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, Physical real-estate)
153
For the 10-year period, the weight allocations for highest return portfolio
resulted in 100% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds), 0% in 96.5% gold, and 0% in physical real-estate.
The rate of return for this portfolio is 45.6% and the risk (standard deviation)
of this portfolio is 15.7%. The Sharpe ratio is 2.72. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.50: Efficient frontier and highest return of 4-assets portfolio type-5 on
10-year duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, Physical real-estate)
154
For the 5-year period, the weight allocations for lowest risk portfolio resulted
in 0.44% weight on stocks (SET50) and 37.9% allocation on bonds (rating A- up
corporate bonds), 1.72% in 96.5% gold, and 59.9% in physical real-estate.
The rate of return for this portfolio is 3.3% and the risk (standard deviation) of
this portfolio is 1.21%. The Sharpe ratio is 0.238. The efficient frontier on this
Portfolio is illustrated as follows:
Figure 4.51: Efficient frontier and lowest risk of 4-assets portfolio type-5 on 5-year
duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, Physical real-estate)
155
For the 10-year period, the weight allocations for lowest risk portfolio resulted
in 0.50% weight on stocks (SET50) and 49% allocation on bonds (rating A- up
corporate bonds), 1.56% in 96.5% gold, and 48.9% in physical real-estate.
The rate of return for this portfolio is 4.05% and the risk (standard deviation)
of this portfolio is 1.2%. The Sharpe ratio is 0.91. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.52: Efficient frontier and lowest risk of 4-assets portfolio type-5 on 10-year
duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, Physical real-estate)
156
For the 5-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 37.6% weight on stocks (SET50) and 0% allocation on bonds
(rating A- up corporate bonds), 27.4% in 96.5% gold, and 35.1% in physical real-
estate.
The rate of return for this portfolio is 18.1% and the risk (standard deviation)
of this portfolio is 7.4%. The Sharpe ratio is 2.04. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.53: Efficient frontier and highest risk-adjusted return of 4-assets portfolio
type-5 on 5-year duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, Physical real-estate)
157
For the 10-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 22.4% weight on stocks (SET50) and 31.9% allocation on bonds
(rating A- up corporate bonds), 0% in 96.5% gold, and 45.6% in physical real-estate.
The rate of return for this portfolio is 13.2% and the risk (standard deviation) of
this portfolio is 3.65%. The Sharpe ratio is 2.82. The efficient frontier on this portfolio
is illustrated as follows:
Figure 4.54: Efficient frontier and highest risk-adjusted return of 4-assets
portfolio type-5 on 10-year duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, Physical real-estate)
158
5. 4-Assets Portfolio with stocks + bonds + gold + PF&REIT
In this alternative portfolio, for the highest return objective, the results are
exhibited as follows for the 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 0% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds), 0% in gold, and 100% in PF&REIT (real-estate funds).
The rate of return for this portfolio is 71.5% and the risk (standard deviation)
of this portfolio is 17.2%. The Sharpe ratio is 3.98. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.55: Efficient frontier and highest return of 4-assets portfolio type-4 on 5-year
duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, real-estate funds)
159
For the 10-year period, the weight allocations for highest return portfolio
resulted in 0% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds), 0% in gold, and 100% in PF&REIT (real-estate funds).
The rate of return for this portfolio is 75.9% and the risk (standard deviation)
of this portfolio is 13.3%. The Sharpe ratio is 5.5. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.56: Efficient frontier and highest return of 4-assets portfolio type-4 on
10-year duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, real-estate funds)
160
In this alternative portfolio, for the lowest risk objective, the results are
exhibited as follows for the 5-year period and 10-year period.
For the 5-year period, the weight allocations for lowest risk portfolio resulted
in 0.9% weight on stocks (SET50) and 94% allocation on bonds (rating A- up
corporate bonds), 4.2% in gold, and 0.93% in PF&REIT (real-estate funds).
The rate of return for this portfolio is 3.95% and the risk (standard deviation)
of this portfolio is 1.9%. The Sharpe ratio is 0.52. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.57: Efficient frontier and lowest risk of 4-assets portfolio type-4 on 5-year
duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, real-estate funds)
161
For the 10-year period, the weight allocations for lowest risk portfolio resulted
in 0.9% weight on stocks (SET50) and 95.2% allocation on bonds (rating A- up
corporate bonds), 2.74% in gold, and 1.2% in PF&REIT (real-estate funds).
The rate of return for this portfolio is 4.96% and the risk (standard deviation)
of this portfolio is 1.6%. The Sharpe ratio is 1.2. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.58: Efficient frontier and lowest risk of 4-assets portfolio type-4 on 5-year
duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, real-estate funds)
162
In this alternative portfolio, for the highest risk-adjusted return objective, the
results are exhibited as follows for 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 23.3% weight on stocks (SET50) and 0% allocation on bonds
(rating A- up corporate bonds), 19% in gold, and 57.7% in PF&REIT (real-estate
funds).
The rate of return for this portfolio is 51.9% and the risk (standard deviation)
of this portfolio is 11.2%. The Sharpe ratio is 4.37. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.59: Efficient frontier and highest risk-adjusted return of 4-assets
portfolio type-4 on 5-year duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, real-estate funds)
163
For the 10-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 20.8% weight on stocks (SET50) and 24.8% allocation on bonds
(rating A- up corporate bonds), 0% in gold, and 54.3% in PF&REIT (real-estate
funds).
The rate of return for this portfolio is 51.6% and the risk (standard deviation)
of this portfolio is 8.1%. The Sharpe ratio is 6.01. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.60: Efficient frontier and highest risk-adjusted return of 4-assets
portfolio type-4 on 10-year duration
4-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold, real-estate funds)
164
6. 5-Assets Portfolio with stocks + bonds + gold + physical real estate + bitcoin
In this alternative portfolio, for the highest return objective, the results are
exhibited as follows for 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 100% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 0% in 96.5% gold, 0% in physical real-estate and 0% in bitcoin.
The rate of return for this portfolio is 38.62% and the risk (standard deviation)
of this portfolio is 18.6%. The Sharpe ratio is 1.92. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.61: Efficient frontier and highest return of 5-assets portfolio type-7 on
5-year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
physical real-estate, Bitcoin)
165
For the 10-year period, the weight allocations for highest return portfolio
resulted in 0% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 0% in 96.5% gold, 0% in physical real-estate and 100% in
bitcoin.
The rate of return for this portfolio is 307.9% and the risk (standard deviation)
of this portfolio is 176.1%. The Sharpe ratio is 1.73. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.62: Efficient frontier and highest return of 5-assets portfolio type-7 on
10-year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
physical real-estate, Bitcoin)
166
In this alternative portfolio, for the lowest risk objective, the results are
exhibited as follows for 5-Year Period and 10-Year Period.
For the 5-year period, the weight allocations for lowest risk portfolio resulted
in 0.44% weight on stocks (SET50) and 37.9% allocation on bonds (rating A- up
corporate bonds) and 1.7% in 96.5% gold, 59.9% in Physical real-estate and 0.03% in
Bitcoin.
The rate of return for this portfolio is 3.3% and the risk (standard deviation) of
this portfolio is 1.21%. The Sharpe ratio is 0.25. The efficient frontier on this portfolio
is illustrated as follows:
Figure 4.63: Efficient frontier and lowest risk of 5-assets portfolio type-7 on
5-year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
physical real-estate, Bitcoin)
167
For the 10-year period, the weight allocations for lowest risk portfolio resulted
in 0.51% weight on stocks (SET50) and 49.9% allocation on bonds (rating A- up
corporate bonds) and 0.003% in 96.5% gold, 49.6% in physical real-estate and 0% in
bitcoin.
The rate of return for this portfolio is 4.1% and the risk (standard deviation) of
this portfolio is 1.2%. The Sharpe ratio is 0.92. The efficient frontier on this portfolio
is illustrated as follows:
Figure 4.64: Efficient frontier and lowest risk of 5-assets portfolio type-7 on 10-year
duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
physical real-estate, Bitcoin)
168
In this alternative portfolio, for the highest risk-adjusted return objective, the
results are exhibited as follows for a 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 36.4% weight on Stocks (SET50) and 0% allocation on bonds
(rating A- up corporate bonds) and 26.7% in 96.5% gold, 35.1% in physical real-
estate and 1.8% in bitcoin.
The rate of return for this portfolio is 18.2% and the risk (standard deviation)
of this portfolio is 7.3%. The Sharpe ratio is 2.1. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.65: Efficient frontier and highest risk-adjusted return of 5-assets portfolio
type-7 on 5-year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
physical real-estate, Bitcoin)
169
For the 10-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 33.8% weight on stocks (SET50) and 26.5% allocation on bonds
(rating A- up corporate bonds) and 0.002% in 96.5% gold, 37.8% in physical real-
estate and 1.87% in bitcoin.
The rate of return for this portfolio is 23.7% and the risk (standard deviation)
of this portfolio is 6.3%. The Sharpe ratio is 3.28. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.66: Efficient frontier and highest risk-adjusted return of 5-assets
portfolio type-7 on 10-year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
physical real-estate, Bitcoin)
170
7. 5-Assets Portfolio with stocks + bonds + gold + PF&REIT + bitcoin
In this alternative portfolio, for the highest return objective, the results are
exhibited as follows for 5-year period and 10-Year period.
For the 5-year period, the weight allocations for highest return portfolio
resulted in 0% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 0% in 96.5% gold, 0% in PF&REIT (real-estate funds) and 0%
in bitcoin.
The rate of return for this portfolio is 71.5% and the risk (standard deviation)
of this portfolio is 17.2%. The Sharpe ratio is 3.9. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.67: Efficient frontier and highest return of 5-assets portfolio type-6 on
5-year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
Real-estate funds, Bitcoin)
171
For the 10-year period, the weight allocations for highest return portfolio
resulted in 0% weight on stocks (SET50) and 0% allocation on bonds (rating A- up
corporate bonds) and 0% in 96.5% gold, 0% in PF&REIT (real-estate funds) and
100% in bitcoin.
The rate of return for this portfolio is 307.9% and the risk (standard deviation)
of this portfolio is 176.1%. The Sharpe ratio is 1.73. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.68: Efficient frontier and highest return of 5-assets portfolio type-6 on
10-year duration
In this alternative portfolio, for the lowest risk objective, the results are
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
Real-estate funds, Bitcoin)
172
exhibited as follows for 5-year period and 10-year period.
For the 5-year period, the weight allocations for lowest risk portfolio resulted
in 0.9% weight on stocks (SET50) and 94% allocation on bonds (rating A- up
corporate bonds) and 4.2% in 96.5% gold, 0.93% in PF&REIT (real-estate funds) and
0.03% in bitcoin.
The rate of return for this portfolio is 3.96% and the risk (standard deviation)
of this portfolio is 1.9%. The Sharpe ratio is 0.52. The efficient frontier on this
portfolio is illustrated as follows:
Figure 4.69: Efficient frontier and lowest risk of 5-assets portfolio type-6 on 5-
year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
Real-estate funds, Bitcoin)
173
For the 10-year period, the weight allocations for lowest risk portfolio resulted
in 0.9% weight on stocks (SET50) and 95.1% allocation on bonds (rating A- up
corporate bonds) and 2.75% in 96.5% gold, 1.2% in PF&REIT (real-estate funds) and
0.03% in bitcoin.
The rate of return for this portfolio is 4.9% and the risk (standard deviation) of
this portfolio is 1.6%. The Sharpe ratio is 1.2. The efficient frontier on this portfolio is
illustrated as follows:
Figure 4.70: Efficient frontier and lowest risk of 5-assets portfolio type-6 on 10-
year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
Real-estate funds, Bitcoin)
174
In this alternative portfolio, for the highest risk-adjusted return objective, the
results are exhibited as follows for 5-year period and 10-year period.
For the 5-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 23% weight on stocks (SET50) and 0% allocation on bonds
(rating A- up corporate bonds) and 18.9% in 96.5% gold, 57.1% in PF&REIT (real-
estate funds) and 0.92% in bitcoin.
The rate of return for this portfolio is 52% and the risk (standard deviation) of
this portfolio is 11.1%. The Sharpe ratio is 4.4. The efficient frontier on this portfolio
is illustrated as follows:
Figure 4.71: Efficient frontier and highest risk-adjusted return of 5-assets
portfolio type-6 on 5-year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
Real-estate funds, Bitcoin)
175
For the 10-year period, the weight allocations for highest risk-adjusted return
portfolio resulted in 20.6% weight on stocks (SET50) and 24.4% allocation on bonds
(rating A- up corporate bonds) and 0% in 96.5% gold, 53.73% in PF&REIT (real-estate
Funds) and 1.3% in bitcoin.
The rate of return for this portfolio is 55% and the risk (standard deviation) of
this portfolio is 8.3%. The Sharpe ratio is 6.3. The efficient frontier on this portfolio is
illustrated as follows:
Figure 4.72: Efficient frontier and highest risk-adjusted return of 5-assets portfolio
type-6 on 10-year duration
5-Assets Portfolio (SET50, Corporate Bonds, 96.5% Gold,
Real-estate funds, Bitcoin)
176
4.5. Comparison of the optimal portfolios (Total 16 Portfolios)
The following diagram demonstrates the optimum assets allocations on
proposed each of the 16 total portfolios, as per thesis scope.
The investment budget is set as THB 10 million value, from the start of the
investment period.
177
Table 4.9: Performance Results of Optimal Portfolios
178
Based on the above optimum results and Sharpe ratios, comparisons between
different portfolios are conducted, to identify the best portfolio, based on the
investment time interval and type of assets combinations. This is conducted based on
the previous Chapter 3 Methodology “Step 6: Compare Efficient Frontier and the
optimal combination of assets in portfolio”. The results of these evaluations are
revealed as follows:
Referring to the above Table 4.9 results, several types of portfolios
constructed for both 5-year and 10-year are as follows:
Table 4.10: Portfolio types of this study
Portfolio Type Investment Assets
Traditional 2-Assets Portfolio SET50 + Corporate Bonds
Alternative 3-Assets Portfolio Type-1 SET50 + Corporate Bonds + 96.5% Gold
Alternative 3-Assets Portfolio Type-2
SET50 + Corporate Bonds +
Real-estate funds (PF&REIT)
Alternative 3-Assets Portfolio Type-3
SET50 + Corporate Bonds +
Physical real-estate
Alternative 4-Assets Portfolio Type-4
SET50 + Corporate Bonds +
Thai Gold + Real-estate funds (PF&REIT)
179
Table 4.10 (Continued): Portfolio types of this study
Portfolio Type Investment Assets
Alternative 4-Assets Portfolio Type-5
SET50 + Corporate Bonds +
Thai Gold + Physical real-estate
Alternative 5-Assets Portfolio Type-6
SET50 + Corporate Bonds +
Thai Gold + Real-estate funds (PF&REIT)
+ Bitcoin
Alternative 5-Assets Portfolio Type-7
SET50 + Corporate Bonds +
Thai Gold + Physical real-estate + Bitcoin
Comparisons with 5-year and 10-year intervals on all 16 possible portfolios
Referring to the above table 4.9 results, the 5-assets portfolio type-6 (SET50,
corporate bonds, gold, PF&REIT, bitcoin) based on 10-year interval achieved the
highest Sharpe ratio of all possible portfolios constructed, with the value of 6.245
(Sharpe ratio).
Best optimum portfolio: 10-year Interval 5-assets portfolio type-6:
Expected return E(Rp) = 55.01%
Standard deviation σ = 8.33%
Sharpe ratio = 6.245
Weighted allocations:
180
Table 4.11: Best optimum portfolio of all 16 total portfolios
SET50 Corporate
Bonds Thai Gold Real-estate
funds Bitcoin
20.6% 24.4% 0% 53.7% 1.3%
Inferior portfolios: All other portfolios
Comparisons with 5-year and 10-year intervals on 5-assets portfolios
Referring to above table 4.9 results, the 10-year interval 5-assets portfolio
achieve the higher risk-adjusted returns than others. Thus,
Best optimum portfolio: 10-year interval 5-assets portfolio type-6:
Expected return E(Rp) = 55.01%
Standard deviation σ = 8.33%
Sharpe ratio = 6.245
Weighted allocations:
Table 4.12: Best optimum portfolio of all 5-assets portfolios
SET50 Corporate
Bonds Thai Gold Real-estate
funds Bitcoin
20.6% 24.4% 0% 53.7% 1.3%
Inferior Portfolios: All other 5-assets portfolios
181
Comparisons with 5-year and 10-year intervals on 4-assets portfolios
Referring to the above table 4.9 results, the 10-year interval 4-assets portfolio
type-4 (SET50, corporate bonds, gold, real-estate funds) achieved the highest risk-
adjusted return than all other 4-assets portfolios: with the Sharpe ratio of 6.013. Thus,
Best Optimum Portfolio: 10-year interval 4-assets portfolio type-4:
Expected Return E(Rp) = 51.65%,
Standard deviation σ = 8.10%,
Sharpe ratio = 6.013
Weighted allocations:
Table 4.13: Best optimum portfolio of all 4-assets portfolios
SET50 Corporate bonds Thai Gold Real-estate funds
20.9% 24.8% 0% 54.3%
Inferior Portfolio: All other 4-assets portfolios
Comparisons with 5-year and 10-year intervals on 3-assets portfolios
Referring to the above table 4.9 results, the 10-year interval 3-assets portfolio
type-2 (SET50, Corporate Bonds, PF&REIT) achieved the highest risk-adjusted
returns than all other 3-assets portfolios: with the Sharpe ratio of 6.01. Thus,
Best Optimum Portfolio: 10-year interval 3-assets portfolio type-2:
Expected return E(Rp) = 51.65%,
182
Standard deviation σ = 8.10%,
Sharpe ratio = 6.01
Weighted allocations:
Table 4.14: Best optimum portfolio of all 3-assets portfolios
SET50 Corporate bonds Real-estate funds
20.8% 24.8% 54.3%
Inferior Portfolio: All other 3-assets portfolios
Comparisons with 5-year and 10-year intervals on 2-assets portfolios
Referring to the above table 4.9 results, the 2-assets portfolio with 10-year
interval, should be chosen, even though it results in negative Sharpe ratio of -0.045.
Best Optimum Portfolio: 10-year interval 2-assets portfolio:
Expected return E(Rp) = 20.83%,
Standard deviation σ = 6.49%,
Sharpe ratio = 2.75
Weighted allocations: 40.8% in SET50, 59.2% in corporate bonds
Table 4.15: Best optimum portfolio of all 2-assets portfolios
SET50 Corporate bonds
40.8% 59.2%
Inferior Portfolio: All other 2-asset portfolios
183
The best performing portfolio in 5-year interval period
Referring to the above table 4.9 results, within the 5-year interval periods, the
5-assets portfolio type-6 achieved the highest risk-adjusted returns than all other 5-year
interval portfolios.
Best optimum portfolio: 5-year interval 5-assets portfolio type-6:
Expected return E(Rp) = 51.77%,
Standard deviation σ = 11.14%,
Sharpe ratio = 4.38
Weighted allocations:
Table 4.16: Best optimum portfolio within 5-year period
SET50 Corporate
Bonds Thai Gold Real-estate
funds Bitcoin
23% 0% 18.9% 57.1% 0.9%
Inferior portfolio: All other 5-year interval portfolios
The best performing portfolio in 10-year interval period
Referring to the above table 4.9 results, within the 10-year interval periods, the
5-assets portfolio type-6 achieved the highest risk-adjusted returns than all other 10-
year interval portfolios.
Best optimum portfolio: 10-year interval 5-assets portfolio type-6:
Expected return E(Rp) = 55.01%,
184
Standard deviation σ = 8.33%,
Sharpe ratio = 6.245
Weighted allocations:
Table 4.17: Best optimum portfolio within 10-year period
SET50 Corporate
Bonds Thai Gold Real-estate
funds Bitcoin
20.6% 24.4% 0% 53.7% 1.3%
Inferior portfolio: All other 10-year interval-based portfolios.
4.6. Hypothesis conclusions
4 research hypotheses are stated in the previous chapter 3 methodology section
as follows:
1. Traditional portfolio with only stocks and bonds exhibits the higher return
and lower risk than the active portfolios with alternative assets.
2. Portfolio with the most assets exhibit the highest risk adjusted rate of
return as measured by Sharpe ratio.
3. Return and risk from a 5-year portfolio are higher than those from 10-year
portfolio.
4. The inclusion of bitcoin does not offer higher risk-adjusted rate of return
as measured by Sharpe ratio in the portfolios
185
The above hypothesis statements are null hypothesis (H0), in which these are
the statements that the researcher wants to reject. If null hypothesis is rejected, the
alternative hypothesis (Ha) is accepted.
Verifying the Null Hypothesis (H0) “Traditional portfolio with ONLY stocks and
bonds exhibits the higher return and lower risk than the Active portfolios with
alternative assets”.
Based on table 4.9 results, the traditional portfolio refers to 2-asset portfolios:
SET50 and corporate bonds, which reveals only the Sharpe ratio of 1.916 (5-year
period) and 2.750 (10-year period). Compared with other 3-assets, 4-assets and 5-
assets portfolios, the Traditional portfolios achieved the lowest Sharpe ratio. Thus,
this Null Hypothesis is REJECTED, and the Alternative Hypothesis is ACCEPTED.
Verifying the Null Hypothesis (H0) “Portfolio with the most assets exhibits the
highest risk adjusted rate of return as measured by Sharpe ratio”.
Referencing the table 4.9 results, the Sharpe ratios of all the portfolios are
stated as below:
186
Table 4.18: Sharpe ratio results of 5-year period portfolios
5-Year Period Sharpe
Ratio Investment Assets
2-Asset Portfolio 1.916 SET50 and Corporate Bonds
3-Assets Portfolio
Type-1 2.036 SET50 and Corporate Bonds and 96.5%
Gold
3-Assets Portfolio
Type-2 4.318 SET50 and Corporate Bonds and
Real-estate funds (PF&REIT)
3-Assets Portfolio
Type-3 1.922 SET50 and Corporate Bonds and
Physical real-estate
4-Assets Portfolio
Type-4 4.371 SET50 and Corporate Bonds and
96.5% Gold and Real-estate funds
(PF&REIT)
4-Assets Portfolio
Type-5 2.042 SET50 and Corporate Bonds and
96.5% Gold and Physical real-estate
5-Assets Portfolio
Type-6 4.38 SET50 and Corporate Bonds and
96.5% Gold and Real-estate funds
(PF&REIT) and Bitcoin
5-Assets Portfolio
Type-7 2.079 SET50 and Corporate Bonds and
96.5% Gold and Physical real-estate and
Bitcoin
187
Table 4.19: Sharpe ratio results of 10-year period portfolios
10-Year Period Sharpe
Ratio Investment Assets
2-Asset Portfolio 2.75 SET50 and Corporate Bonds
3-Assets Portfolio
Type-1 2.752 SET50 and Corporate Bonds and 96.5%
Gold
3-Assets Portfolio
Type-2 6.01 SET50 and Corporate Bonds and
Real-estate funds (PF&REIT)
3-Assets Portfolio
Type-3 2.82 SET50 and Corporate Bonds and
Physical real-estate
4-Assets Portfolio
Type-4 6.013 SET50 and Corporate Bonds and
96.5% Gold and Real-estate funds
(PF&REIT)
4-Assets Portfolio
Type-5 2.816 SET50 and Corporate Bonds and
96.5% Gold and Physical real-estate
5-Assets Portfolio
Type-6 6.245 SET50 and Corporate Bonds and
96.5% Gold and Real-estate funds
(PF&REIT) and Bitcoin
5-Assets Portfolio
Type-7
3.282
SET50 and Corporate Bonds and
96.5% Gold and Physical real-estate and
Bitcoin
188
The results revealed the just by adding more assets does not ensure that the
corresponding portfolio would achieve the highest risk-adjusted rate of return (Sharpe
ratio). In fact, the factors depend on the investment period, in which the longer the
investment period, the better the performance of the Sharpe ratio. Also, the
performance of the Sharpe ratio depends on the different combinations of assets.
Therefore, this Null Hypothesis is REJECTED. Thus, the Alternative Hypothesis (Ha)
is accepted.
Verifying the Null Hypothesis (H0) “Return and risk from 5-year portfolio are higher
than those from 10-year portfolio”.
Based on the table 4.9 results, the 5-year and 10-year portfolios are extracted
briefly as follows:
Table 4.20: Sharpe ratio results of 5-year period portfolios
5-Year Period
Sharpe
Ratio
Expected
Return
Risk
2-Asset Portfolio
(SET50 and Corporate Bonds) 1.916 39% 19%
3-Assets Portfolio Type-1
(SET50 and Corporate Bonds and 96.5%
Gold)
2.036 26% 11%
3-Assets Portfolio Type-2
(SET50 and Corporate Bonds and Real-estate
funds)
4.318 62% 14%
189
Table 4.20 (Continued): Sharpe ratio results of 5-year period portfolios
5-Year Period
Sharpe
Ratio
Expected
Return
Risk
3-Assets Portfolio Type-3
(SET50 and Corporate Bonds and Physical real-
estate)
1.922 22% 10%
4-Assets Portfolio Type-4
(SET50 and Corporate Bonds and Physical real-
estate)
4.371 52% 11%
4-Assets Portfolio Type-5
(SET50 and Corporate Bonds and
96.5% Gold and Physical real-estate)
2.042 18% 7%
5-Assets Portfolio Type-6
(SET50 and Corporate Bonds and
96.5% Gold and Real-estate funds (PF&REIT)
and Bitcoin)
4.38 52% 11%
5-Assets Portfolio Type-7
(SET50 and Corporate Bonds and
96.5% Gold and Physical real-estate and Bitcoin)
2.079 18% 7%
Table 4.21: Sharpe ratio results of 10-year period portfolios
10-Year Period Sharpe
Ratio
Expected
Return Risk
2-Asset Portfolio
(SET50 and Corporate Bonds) 2.75 21% 6%
3-Assets Portfolio Type-1
(SET50 and Corporate Bonds and 96.5%
Gold)
2.752 21% 6%
3-Assets Portfolio Type-2
(SET50 and Corporate Bonds and Real-estate
funds)
6.01 52% 8%
3-Assets Portfolio Type-3
(SET50 and Corporate Bonds and Physical
real-estate)
2.82 13% 4%
190
Table 4.21 (Continued): Sharpe ratio results of 10-year period portfolios
10-Year Period Sharpe
Ratio
Expected
Return Risk
4-Assets Portfolio Type-4
(SET50 and Corporate Bonds and Physical
real-estate)
6.013 52% 8%
4-Assets Portfolio Type-5
(SET50 and Corporate Bonds and
96.5% Gold and Physical real-estate)
2.816 13% 4%
5-Assets Portfolio Type-6
(SET50 and Corporate Bonds and
96.5% Gold and Real-estate funds (PF&REIT)
and Bitcoin)
6.245 55% 8%
5-Assets Portfolio Type-7
(SET50 and Corporate Bonds and
96.5% Gold and Physical real-estate and
Bitcoin)
3.282 24% 6%
Comparing the above 2 tables, the results revealed that the same combination
of assets with a 10-year period performs much better than 5-year period, based on the
Sharpe ratio. In fact, the total risk of portfolio is low, ranging from 4%-8% in 10-year
portfolios, compared with 5-year portfolios, ranging from 7%-19%.
Therefore, this Null Hypothesis is REJECTED. Instead, the Alternative
Hypothesis is ACCEPTED.
191
Verifying the Null Hypothesis (H0) “The inclusion of Bitcoin does not offer Higher
Risk-Adjusted Rate of Return as measured by Sharpe Ratio in the Portfolios”.
According to table 4.9 data, the inclusion of bitcoin with other portfolio assets
even achieved higher Sharpe ratio, risk-adjusted returns, as in 5-assets portfolio Type-
6 and 5-assets portfolio Type-7.
Thus, the Null Hypothesis is REJECTED. The Alternative Hypothesis is
ACCEPTED.
Summary of Hypothesis Testing
The results of this thesis study found that Markowitz’s modern portfolio
theory proved to achieve better results on long investment timeframe, low correlations
assets with each other. Indeed, the mix of portfolios proved to achieve better
performance results than the traditional portfolios. However, rebalancing strategies
are not considered, as the investment style is assumed to be passive strategy (Buy and
Hold).
192
CHAPTER 5
DISCUSSION
The purpose of this thesis study is to explore the construction of optimal
portfolios using a combination of traditional assets and alternative assets, as well as
comparing the performance of those portfolios to investigate useful insights for
investors. Both 5-year and 10-year time intervals are tested and discussed, utilizing
the modern-portfolio approach.
In this research study, the author developed a total of 16 optimal portfolios
that sought to discover the asset weight allocations for highest return, lowest risk, and
highest risk-adjusted return objectives. A total of 5 investment types have been
considered in this study which includes stocks (SET50), bonds (A-rating corporate
bonds), real-estate (real-estate funds, physical real-estate), gold, and bitcoin.
A total of 4 null hypotheses have been constructed, and all are rejected, thus
alternative hypotheses are accepted.
5.1. Significant Findings
In this research study, significant findings are revealed as follows:
1. Over the long-term investment horizon, typically 10-year period, alternative
assets tend to exhibit higher returns than the mid-term 5-year period. This is
evident in the comparison of table 4.1 and table 4.2 results respectively.
Among them, Bitcoin exhibits 11.72% average monthly returns (10-year
timeframe), which in fact 9.1% higher than the 5-year mid-timeframe.
193
2. On both 5-year and 10-year duration, SET50 stocks exhibit negative
correlation with Thai gold and physical real-estate, while strong positively
correlated with real-estate funds and bitcoin. The highest positively correlated
asset with SET50 is real-estate funds, which are 0.7238 for 5-year duration
and 0.6456 for 10-year duration.
3. For traditional portfolios (SET50 + corporate bonds), the optimal asset
allocations for the 3 observations (portfolio with highest return, portfolio with
lowest risk, and portfolio with highest risk-adjusted return) are revealed as
follows:
Table 5.1: Traditional portfolios’ weight allocation observations
Traditional Portfolios Term SET50 Corporate bonds
Highest return 5-year 100% 0%
Highest return 10-year 100% 0%
Lowest risk 5-year 0.96% 99.04%
Lowest risk 10-year 0.97% 99.04%
Highest risk-adjusted
return 5-year 100% 0%
Highest risk-adjusted
return 10-year 40.77% 59.23%
The results revealed that to achieve the highest returns, investing only
in stocks fulfils the objective of achieving the highest return. Meanwhile, to
achieve the lowest risk of the traditional portfolio, corporate bonds are
194
considered as a safe-haven asset than stocks, based on the empirical evidence
stated above in table 5.1.
However, there is a significant difference between asset weight
allocations on achieving the highest risk-adjusted return, which are the optimal
portfolios. For 5-year duration, investing in stocks (SET50) only proved to be
the solution for optimal portfolio while 40.77% in stocks and 59.23% in
corporate bonds proved to be the solution for optimal portfolio for longer 5-
year duration.
4. Meanwhile, for alternative assets portfolios, the significant observations are as
follows:
Table 5.2: Alternative portfolio type-1 weight allocation observations
Portfolio
Type-1 Term SET50 Corporate
bonds
Physical real-
estate
Highest return 5-year 100% 0% 0%
Highest return 10-year 100% 0% 0%
Lowest risk 5-year 0.437% 38.66% 60.9%
Lowest risk 10-year 0.509% 49.9% 49.6%
Highest risk-
adjusted
return
5-year 51.89% 0% 48.11%
Highest risk-
adjusted
return
10-year 22.42% 31.9% 45.62%
195
According to table 5.2, for the highest return, like traditional
portfolios, stocks (SET50) proved to be the best solution. However, for the
lowest risk, concentrated allocations in physical real-estate and corporate
bonds proved to be the optimal solution for achieving the lowest risk.
However, for the highest risk-adjusted return, the significant findings
are as follows. For the long-term 10-year duration, physical real-estate proved
to be the largest weight allocation for the optimal portfolios, followed by the
corporate bonds, and SET50. However, for 5-year duration, SET50 achieved
the highest allocated asset, followed by physical real-estate.
Table 5.3: Alternative portfolio type-2 weight allocation observations
Portfolio
Type-2 Term SET50 Corporate
bonds Real-estate funds
Highest return 5-year 0% 0% 100%
Highest return 10-year 0% 0% 100%
Lowest risk 5-year 0.9% 98.13% 0.98%
Lowest risk 10-year 0.9% 97.9% 1.245%
Highest risk-
adjusted
return
5-year 28.67% 0% 71.3%
Highest risk-
adjusted
return
10-year 20.83% 24.84% 54.33%
According to table 5.3, for the portfolio type-2, real-estate funds
196
(PF&REIT shares) proved to be the best solution. However, for the lowest
risk, concentrated allocations in corporate bonds with a few minor allocations
to stocks and real-estate funds proved to be the best solution.
However, for the highest risk-adjusted return, the significant findings
are as follows. For a long-term 10-year duration, real-estate funds proved to be
the highest allocated of 54.33%, followed by corporate bonds and stocks
(SET50). For a 5-year duration, real-estate funds will be the highest allocated,
followed by the stocks (SET50).
Table 5.4: Alternative portfolio type-3 weight allocation observations
Portfolio
Type-3 Term SET50 Corporate
bonds Gold
Highest return 5-year 100% 0% 0%
Highest return 10-year 40.8% 59.2% 0%
Lowest risk 5-year 0.95% 94.86% 4.19%
Lowest risk 10-year 0.95% 96.27% 2.77%
Highest risk-
adjusted
return
5-year 57.9% 0% 42.14%
Highest risk-
adjusted
return
10-year 40.77% 59.23% 0%
According to table 5.4, for the portfolio type-3, stocks (SET50) and
corporate bonds proved to be the best solution for highest return. However, for
the lowest risk, concentrated allocations in corporate bonds resulted in the
197
lowest risk for this portfolio type.
However, for the highest risk-adjusted return, the significant findings
are as follows. For a long-term 10-year duration, stocks (SET50) and
corporate bonds constitute a major significant allocation of the optimal
portfolios while stocks (SET50) and gold constitute a major allocation in 5-
year duration.
Table 5.5: Alternative portfolio type-4 weight allocation observations
Portfolio
Type-4 Term SET50 Corporate
bonds Gold Physical real-
estate
Highest
return 5-year 100% 0% 0% 0%
Highest
return 10-year 100% 0% 0% 0%
Lowest risk 5-year 0.44% 37.9% 1.72% 59.9%
Lowest risk 10-year 0.5% 49% 1.56% 48.9%
Highest
risk-
adjusted
return
5-year 37.6% 0% 27.4% 35.1%
Highest
risk-
adjusted
return
10-year 22.4% 31.9% 0% 45.6%
According to table 5.5, for the portfolio type-4, stocks (SET50) proved
to be the only asset to achieve the highest return. However, physical real-estate
becomes the major asset to be allocated when considered for the lowest risk
and highest risk-adjusted return.
198
Table 5.6: Alternative portfolio type-5 weight allocation observations
Portfolio
Type-5 Term SET50 Corporate
bonds Gold Real-estate
funds
Highest
return 5-year 0% 0% 0% 100%
Highest
return 10-year 0% 0% 0% 100%
Lowest risk 5-year 0.9% 94% 4.2% 0.93%
Lowest risk 10-year 0.9% 95.2% 2.74% 1.2%
Highest
risk-
adjusted
return
5-year 23.3% 0% 19% 57.7%
Highest
risk-
adjusted
return
10-year 20.8% 24.8% 0% 54.3%
According to table 5.6, for the portfolio type-5 involving assets, real-
estate funds (PF&REIT shares) are considered as the leading asset for
achieving the highest return. However, for the lowest risk portfolio, corporate
bonds become the major asset while achieving the highest risk-adjusted return
requires the major allocation of real-estate funds (PF&REIT).
Table 5.7: Alternative portfolio type-6 weight allocation observations
Portfolio
Type-6 Term SET50 Corporate
bonds Gold
Physical
real-
estate
Bitcoin
Highest
return 5-year 100% 0% 0% 0% 0%
199
Table 5.7 (Continued): Alternative portfolio type-6 weight allocation
observations
Portfolio
Type-6 Term SET50 Corporate
bonds Gold
Physical
real-
estate
Bitcoin
Highest
return 10-year 0% 0% 0% 0% 100%
Lowest
risk 5-year 0.44% 37.9% 1.7% 59.9% 0.03%
Lowest
risk 10-year 0.51% 49.9% 0.003% 49.6% 0%
Highest
risk-
adjusted
return
5-year 36.4% 0% 26.7% 35.1% 1.8%
Highest
risk-
adjusted
return
10-year 33.8% 26.5% 0.002% 37.8% 1.87%
According to table 5.7, for the portfolio type-6 involving assets,
SET50 proved to be the major asset in mid-term 5-year duration. However,
bitcoin outperformed all other assets in the long-term 10-year duration for the
highest return. However, for lowest risk portfolio, corporate bonds are still
considered as safe-haven assets due to its low volatility and stable returns.
Furthermore, for highest risk-adjusted return, it is evident that a small
allocation of bitcoin improves the overall portfolio performance compared
with other portfolios without bitcoin. According to table 5.5, and table 5.7., the
allocation of additional bitcoin asset, proved to be 0.14% higher than the same
portfolio without bitcoin for 5-year duration, while 10.45% higher for 10-year
duration.
200
Table 5.8: Alternative portfolio type-7 weight allocation observations
Portfolio
Type-7 Term SET50 Corporate
bonds Gold
Real-
estate
funds
Bitcoin
Highest
return 5-year 0% 0% 0% 100% 0%
Highest
return 10-year 0% 0% 0% 0% 100%
Lowest
risk 5-year 0.9% 94% 4.2% 0.93% 0.03%
Lowest
risk 10-year 0.9% 95.1% 2.75% 1.2% 0.03%
Highest
risk-
adjusted
return
5-year 23% 0% 18.9% 57.1% 0.92%
Highest
risk-
adjusted
return
10-year 20.6% 24.4% 0% 53.73% 1.3%
According to table 5.8, for this portfolio type-7 involving assets, for
mid-term 5-year, real-estate funds proved to be the highest return generating
asset. However, bitcoin still outperforms in this portfolio for the long-term 10
year. While corporate bonds are still the safe-heaven asset in this portfolio, the
inclusion of bitcoin significantly improves the risk-adjusted return, with a
major allocation of stocks (SET50) and real-estate funds.
5. Moreover, the effect of adding bitcoin impacts the performance comparison of
the portfolios. Significant findings are as follows:
• The best performing portfolio in a 5-year duration accounts to 5-assets
portfolio type-6 which includes 23% stocks (SET50), 0% bonds,
201
18.9% gold, 57.1% real-estate funds and 0.9% bitcoin.
• Meanwhile, for a 10-year duration, the best performing portfolio
accounts to the same portfolio type-6 with a different amount of asset
allocations: 20.6% stocks, 24.4% bonds, 0% gold, 53.7% real-estate
funds, and 1.3% bitcoin.
6. Furthermore, the risk-adjusted returns are improved when considered for a
longer investment horizon. This is significant in the comparison of all total 16
portfolios, in which 5-assets portfolio type-6 (10-year duration) achieved the
highest Sharpe ratio of 6.245.
5.2. Research Discussion
According to Choi & Mukherji (2010), the previous study on construction of
optimal portfolios results in a higher allocation of riskier assets (equity stocks) as the
holding period time increases. In fact, 3 different holding periods are considered,
namely 1, 5, and 10 years. For just 1-year, treasury bills are allocated 96% while the
rest 4.24% to the small company stocks, meanwhile 88.42% to the treasury bills and
11.58% to the small company stocks for 5-year. However, for 10-year returns, the
allocation to the riskier assets becomes bigger in size, as 19.44% is now allocated to
the small company stocks, while the rest 80.56% to treasury bills. Moreover, another
previous study also proved that as investment term increases from 1 to 10 years,
efficient portfolios have been allocated with the rising stock allocations and declining
bonds (Hansson & Persson, 2000).
Indeed, the current study of optimal portfolios construction also complied
202
with the previous studies’ results and recommendations. In this research study,
according to the alternative assets are allocated more in every portfolio as the holding
period time increases. In fact, according to table 4.9, in 5-assets portfolio type-6, the
riskiest asset, bitcoin is allocated 0.5% more in 10-year term than the 5-year term,
while the traditional asset, stocks (SET50) is allocated less 2.4% in 10-year term than
the 5-year term.
Moreover, the previous studies showed that there is only 11% probability in
stocks underperforming against bonds for 10-year returns, and the results are quite
significant in over 20-year returns, where there is only 5% probability of stocks being
underperformed against the bonds (Butler & Domian, 1991). Indeed, this is evident
and in compliance with current research findings. According to table 4.1 and table
4.2, stocks (SET50) and other alternative assets completely outperform the corporate
bonds by 2x to 10x, in terms of returns. However, corporate bonds proved as the safe-
haven assets to be included since their risk (standard deviation) only ranges between
0.49% - 0.56%.
Moreover, according to (Kyriazis, 2022), during the global conflicts (e.g.,
Russian-Ukrainian conflict), Chinese yuan, gold, soybeans, sugar, corn and bitcoin
are considered as safe-haven assets while Japanese yen, wheat, natural gas, and
combination of Bitcoin and Ethereum are regarded as suitable assets for risk-seekers
for profit opportunities. This in fact, proved that adding bitcoin into the alternative
assets’ portfolio will provide profit opportunities for risk-seekers while proving as a
safe-haven asset during the uncertainties of global conflicts.
Indeed, in this research study, the current findings and previous studies are
complying with each other. In fact, by adding bitcoin into the alternative 5-assets
203
portfolio type-6 and type-7, the Sharpe ratio has greatly improved in those portfolio
types than without bitcoin. In addition, on 10-year investment period, the inclusion of
bitcoin in 5-assets portfolio type-6 results in Sharpe ratio of 6.245 while the same
portfolio without bitcoin only results in 6.103 only.
Moreover, according to the previous study, (Zhang, 2022) revealed the
construction of optimal portfolios using ten stocks of S&P500 index, results in two
models of portfolio: Markowitz model and the Single Index model. The results
revealed that Markowitz’s modern portfolio theory proved to achieve higher returns
than the traditional single index model if investing is done by following the long-term
strategy. Therefore, this current study’s findings also complement the previous
study’s findings.
Compared with traditional portfolios (stocks and bonds only), alternative
assets portfolios proved to be more efficient and higher risk-adjusted returns if the
long-term strategy is followed in accordance with Markowitz’s modern portfolio
theory. In fact, this current study implies the other previous study’s results.
According to (Bary, 2023), top US endowment funds now allocated
alternative assets up to 50% of total portfolio. Indeed, alternative portfolios
outperforms against 60:40 equity/bond traditional portfolios in a significant factor,
over the longer-term horizon, namely 20-year horizon, where top 5 endowments
achieve 11.2 % annualized returns, while the traditional portfolios only accounts for
6.0%.
Moreover, according to (Achudume & Ugbebor, 2021) of the previous study,
they constructed the optimal portfolios using MATLAB to identify investors’ varying
rates of return. The results revealed that diversification, investment policies and
204
management are important factors in portfolio optimization. This in fact,
complements the current thesis study. However, the current thesis study only
highlights the diversification effects and the implementation of Markowitz’s theory
while ignoring the investment policies, firm’s management style and rebalancing
techniques. Thus, these will still be the research gaps and further research trends for
this study.
Another previous study revealed that there are significant gaps and
misalignments between academic theories and the actual asset management practices
(Lee & Eid, 2018). The study was on firms’ management in a developing country of
South America, such that investment managers did not widely use the quantitative
method of portfolio optimization. Rather, a simple rule of maximum limit on each
single asset is more preferred and widely used. This in fact, contradicts and does not
imply with the current thesis study since current study relies quantitative methods on
portfolio optimization using the modern portfolio theory. This could be in fact, due to
insufficient knowledge of investment education and skills of investment managers
while the lack of regulatory compliance on such investment firms.
Moreover, another previous study on real-estate focused revealed the
importance of real-estate assets in improving the overall diversification of the
portfolios (Janta, 2014). The study was on direct real-estate assets and indirect real-
estate assets over the period of 2009 to 2014, which revealed that the inclusion of
real-estate assets proved to exhibit very low correlations with traditional financial
assets such as stocks and bonds. This in fact, complements the current thesis study. In
fact, the current thesis study revealed that the addition of physical real-estate to the
traditional portfolio results in reduction of total portfolio risk by 2.84% for 10-year
205
interval portfolios and 8.93% for the 5-year portfolio.
In addition, another previous study revealed that the inclusion of Bitcoin
greatly improves the risk-adjusted return factors of multi-asset portfolios
(Onniwattananon, 2023). This amplifies both the Sharpe ratios and Treynor ratios as
well. In fact, the previous study focused on the period of 2018 to 2022. This in fact,
complements the current thesis as well. The current thesis revealed that the addition of
Bitcoin in multiple-assets portfolios resulted in 0.466 increment of Sharpe ratio,
compared with the same portfolio assets without Bitcoin in 10-year interval period.
5.3. Recommendation
5.3.1 Investment Recommendation
The investment recommendations are as follows:
Individual investors: for individual investors, the variety of portfolios constructed will
enable them to choose a selection of different market exposure. However, based on
the previous empirical results, the highest Sharpe ratio results derived from the
portfolio comprised of stocks (SET50), corporate bonds, real-estate funds and a few
allocations of bitcoin. Thus, investors are advised to strongly have exposure to these
above markets except cryptocurrencies where extreme caution should be made due to
its uncertain nature and extreme volatility. Nevertheless, based on previous empirical
results, Markowitz’s theory proved that alternative asset portfolios outperform better
in the longer-term horizon, especially from 10-years. Thus, investors are advised to
consider for long-term duration on investment horizon, using the research insights
from the optimal portfolios constructed.
Institutional Investors: regarding institutional investors, the empirical results of this
206
study will provide a benchmark line for optimal portfolios in Thai capital markets,
with the combination of other prominent alternative assets, including gold, real-estate,
and bitcoin. Moreover, institutional investors who conduct portfolio recommendations
and construction based on this study are advised to adjust accordingly with their
clients’ investment objectives and risk-tolerance. Indeed, institutional firms can use
these findings to conduct back-testing on performance of Thai equity markets with
other special commodities included in this study. Nevertheless, investment knowledge
of clients is also essential such that institutional firms are also strongly advised to
educate their respective clients and stakeholders, the possible benefits and
consequences of the market nature and irrationality of investment decision-making.
5.3.2 Managerial Recommendation
The managerial recommendations are as follows. To utilize the current study
results in practice, the application of behavioral finance should be greatly considered.
The emotional biases and cognitive biases of investors could nevertheless impact on
the actual returns’ performance of those portfolios. Therefore, investment results
should not only be relied on this research insights but also educate investors about the
possibly of irrational decision making.
Indeed, the related firms included in the investment recommendations of the
portfolios should also be improved more. According to (SET, 2023), rebalancing is
conducted every 6 months, on the constituents of SET50 index, based on their
performance, market capitalization, and other factors. However, previous results
showed that SET50 index does not outperform against other alternative assets and
commodities, as well as firms being strongly correlated with each other. Therefore,
207
individual investors and institutional firms should conduct further due diligence
approach on SET50 index, on up-to-date information and identify any possible
impacts on the strategic implementation of those major firms.
5.3.3. Policy Recommendations and Implications
Policy makers, especially the SET and other related agencies, should
encourage the further development of Foreign Direct Investment (FDI) since foreign
investors are still concerned about the performance of SET, and related indices. In
fact, the education of investing in SET and Thai capital markets should be further
easily and widely accessible to foreign investors, with a strategic plan of promotions
and marketing. Nevertheless, since the recovery of tourism in 2023, Thai economy
has been regrowth, in which SET exhibits positive index in the short term. Moreover,
the current empirical results would enforce the SET to further consider the suitable
portfolio asset allocations for investors, as well as further conduct publishing
investing insights on including SET stocks with other possible alternative assets.
5.3.4. Recommendations for Further Research
This current thesis study greatly covers the mid-term and long-term horizon
of investing in Thai equities with a combination of other alternative assets. A total of
16 optimal portfolios have been constructed so far, and investors could utilize these
insights as a baseline to further consider investment decisions. However, the study
still doesn’t cover the comparison of Markowitz’s modern portfolio theory with the
latest development trends: post-modern portfolio theory and behavioral finance
theory. Therefore, further research should be focused on considering the new theory
208
approaches and improving the performance of optimal portfolios for investors.
Meanwhile, investor’s utility functions should also be considered so that efficient
portfolios could be further determined based on each investor type’s characteristics.
5.4. Limitation of the Study
Due to the thesis scope, only Modern Portfolio Theory has been considered
respective to thesis study. Indeed, the behavioral factors of investors are not studied
and included in this thesis research. Instead, investors are considered rational, and will
continue to invest in a rational manner. However, the reality suggests that investors
are irrational, and emotional, in which behavioral finance theory needs to be highly
considered on such investment analysis studies. Moreover, apart from other
alternative assets, the investing of cryptocurrencies should be highly cautious to new
investors. Due to its nature of extreme volatility, there is a high chance that the
current research insights of portfolios could not predict the future market volatilities
of the cryptocurrency market.
209
BIBLIOGRAPHY
Abdelmalak, Y. (2017). Application of Portfolio Theory On Selected Markets.
Retrieved from https://is.muni.cz/th/n16k1/Thesis252.pdf
Achudume, C., & Ugbebor, O. O. (2021, January 1). Optimal portfolio of an investor
in a financial market. Journal of Physics: Conference Series, 1734(1), 012050.
https://doi.org/10.1088/1742-6596/1734/1/012050
Ajanapanya, N. (2022, September 28). Gold maintains its glitter for Thai retail
investors as safe-haven asset. Retrieved from
https://www.nationthailand.com/blogs/business/trading-investment/40020493
Ajanapanya, N. (2022, September 28). Thailand third highest buyer of gold in
Southeast Asia in 2022. Retrieved from
https://www.nationthailand.com/business/trading-investment/40024561
Bank of Thailand. (2024). Bank of Thailand International Reserves. Retrieved from
https://app.bot.or.th/BTWS_STAT/statistics/BOTWEBSTAT.aspx?reportID=
94&language=ENG
Bank of Thailand. (n.d.). Lessons learnt from the Asian Financial Crisis. Retrieved
from https://www.bot.or.th/en/our-roles/special-measures/Tom-Yum-Kung-
lesson.html
210
Bary, A. (2023, October). Yale’s Endowment Gains 1.8%, But Badly Lags S&P 500.
Retrieved from https://www.barrons.com/articles/yales-endowment-gains-1-8-
but-badly-lags-s-p-500-9b80143b
Butler, K. C., & Domian, D. L. (1991, April 30). Risk, diversification, and the
investment horizon. The Journal of Portfolio Management, 17(3), 41–47.
https://doi.org/10.3905/jpm.1991.409334
CFA Institute. (2023). Introduction to Alternative Investments. Retrieved from
https://www.cfainstitute.org/en/membership/professional-
development/refresher-readings/introduction-alternative-investments
CFI Team. (2024, February 13). Capital Asset Pricing Model (CAPM). Retrieved
from https://corporatefinanceinstitute.com/resources/valuation/what-is-capm-
formula/
CFI Team. (2024, March 4). Sharpe Ratio. Retrieved from
https://corporatefinanceinstitute.com/resources/career-map/sell-side/risk-
management/sharpe-ratio-definition-formula/
Chladek, N. (2020, May 7). The 7 Alternative Investments You Should Know | HBS
Online. Retrieved from https://online.hbs.edu/blog/post/types-of-alternative-
investments
Choi, B. P., & Mukherji, S. (2010). Optimal Portfolios For Different Holding Periods.
Journal of Business & Economics Research (JBER), 8(10).
https://doi.org/10.19030/jber.v8i10.768
211
De Souza, I. (2023, November 24). Top 10 Luxury Collectible Assets: Alternative
Investments for Collectors. Retrieved from
https://www.myartbroker.com/investing/articles/top-10-luxury-collectible-
investments
Fabozzi, F. J., & Markowitz, H. M. (2011, April 18). The Theory and Practice of
Investment Management. Retrieved from
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118267028
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical
Work. The Journal of Finance, 25(2), 383–417.
https://doi.org/10.2307/2325486
Gai, P., & Vause, N. (2006). Measuring Investors’ Risk Appetite. International
Journal of Central Banking, 2(1). Retrieved from
https://www.ijcb.org/journal/ijcb06q1a5.pdf
Gold Traders Association of Thailand. (n.d.). Gold Traders Association :
- . Retrieved from
https://www.goldtraders.or.th/
Hansson, B., & Persson, M. (2000, September). Time Diversification and Estimation
Risk. Financial Analysts Journal, 56(5), 55–62.
https://doi.org/10.2469/faj.v56.n5.2390
Horstmeyer, D. (2022, April 12). Portfolio Diversification: Harder Than It Used to
Be? Retrieved from
212
https://blogs.cfainstitute.org/investor/2022/04/11/portfolio-diversification-
harder-than-it-used-to-be/
Hübner, G. (2005, January 1). The Generalized Treynor Ratio. Review of Finance,
9(3), 415–435. https://doi.org/10.1007/s10679-005-2265-x
Inci, A. C., & Lagasse, R. (2019, November 11). Cryptocurrencies: applications and
investment opportunities. Journal of Capital Markets Studies, 3(2), 98–112.
https://doi.org/10.1108/jcms-05-2019-0032
Irfanullah, A. (2022, July 13). Portfolio Risk and Return Part II | IFT World.
Retrieved from https://ift.world/booklets/portfolio-management-portfolio-risk-
and-return-part-ii-part1/
Janta, N. (2014). Comparison between Direct and Indirect Real Estate Investment in
the Multi-Assets Portfolio. Thammasat University.
Jitteemet, S. (2016). Assessing Return and Risk in Market for Alternative Investment
(MAI) Using Fama–French Three-Factor Model. Retrieved from
http://dspace.bu.ac.th/jspui/handle/123456789/2203
Kanoria, S., & Muzaffar, H. (2017, February 6). Understanding real estate as an
investment class. Retrieved from https://www.mckinsey.com/industries/real-
estate/our-insights/understanding-real-estate-as-an-investment-class
213
Kierkegaard, K., Lejon, C., & Persson, J. (2006, December). Practical Application of
Modern Portfolio Theory. Retrieved from http://www.diva-
portal.org/smash/get/diva2%3A4384/FULLTEXT01.pdf
Kong-ied, A. (2017, December). Return from Mutual Fund Portfolio Management in
Thailand. Retrieved from http://dspace.bu.ac.th/jspui/handle/123456789/2762
Krulický, T., & Horák, J. (2019). Real estate as an investment asset. SHS Web of
Conferences, 61, 01011. https://doi.org/10.1051/shsconf/20196101011
Kyriazis, N. A. (2022, September 1). Optimal Portfolios of National Currencies,
Commodities and Fuel, Agricultural Commodities and Cryptocurrencies
during the Russian-Ukrainian Conflict. International Journal of Financial
Studies, 10(3), 75. https://doi.org/10.3390/ijfs10030075
Lee, S. C., & Eid, W. (2018, June 22). Portfolio construction and risk management:
theory versus practice. Retrieved from https://doi.org/10.1108/rausp-04-
2018-009
Leković, M. (2021). Historical development of portfolio theory. Tehnika, 76(2), 220–
227. https://doi.org/10.5937/tehnika2102220l
Levy, H., & Spector, Y. (1996, April 30). Cross-Asset versus Time Diversification.
The Journal of Portfolio Management, 22(3), 24–34.
https://doi.org/10.3905/jpm.1996.409551
214
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.
https://doi.org/10.2307/2975974
Minter, M. (2024, January 30). Modern Portfolio Theory vs. Behavioral Finance.
Retrieved from https://www.finsyn.com/modern-portfolio-theory-vs-
behavioral-finance/
Nakamoto, S. (2009). Bitcoin: A Peer-to-Peer Electronic Cash System. Retrieved
from https://bitcoin.org/bitcoin.pdf
Onniwattananon, N. (2023). Study in Risk and Return of Consistent Growth Portfolio
by SETTRADE Stock Screener with Diversification Using Cryptocurrency
(BITCOIN). Master’s Thesis, Mahidol University.
Radović, M., Radukić, S., & Njegomir, V. (2018, April 1). The Application of the
Markowitz’s Model in Efficient Portfolio Forming on the Capital Market in
the Republic of Serbia. Economic Themes, 56(1), 17–34.
https://doi.org/10.2478/ethemes-2018-0002
RBC Wealth Management. (2023, November 1). What are fixed-income securities? -
RBC Wealth Management. Retrieved from
https://www.rbcwealthmanagement.com/en-ca/insights/what-are-fixed-
income-securities
Rhoads, D. (2021, November). Using Modern Portfolio Theory To Create Efficient
Portfolios From The S&P500. Honors College Thesis Murray State
University, 40. https://digitalcommons.murraystate.edu/honorstheses/99/
215
Rodnikorn, P. (2021). The inflation hedging ability of real estate investment in
Thailand. Retrieved from https://digital.car.chula.ac.th/chulaetd/7702
S&P 500®. (2023). Retrieved from
https://www.spglobal.com/spdji/en/indices/equity/sp-500/
Sae-ue, R. (2017, December). The Analysis on Portfolio Performance Consisting of
Several Asset Classes and Different Portfolio Rebalancing Policies.
Retrieved from http://dspace.bu.ac.th/jspui/handle/123456789/2819
SET. (2023). SET Annual Report 2022. Retrieved from
https://media.set.or.th/set/Documents/2023/May/SET_Annual_Report_2022_
ENG.pdf
SET. (2023). SET50 Monthly Report. Retrieved from
https://weblink.set.or.th/dat/report/product/SET50_MonthlyReport_20231031
.pdf
SET. (2024). Market Overview - The Stock Exchange of Thailand. Retrieved from
https://www.set.or.th/en/market
Snopek, L. (2012, January 2). The Complete Guide to Portfolio Construction and
Management. Retrieved from https://doi.org/10.1002/9781118467220
Sriram, S. (2021, March). Bitcoin Becomes Best Performing Asset Of The Decade,
Returning Ten Times More Than Nasdaq 100. Retrieved from
216
https://finance.yahoo.com/news/bitcoin-becomes-best-performing-asset-
132208120.html
Sungkarat, T. (2016, November). Expected Returns and Systematic Risks of
Infrastructure funds. Retrieved from
http://dspace.bu.ac.th/jspui/handle/123456789/2147
Tamplin, T. (2023, May 23). Treynor Ratio | Definition, Components, Calculation,
Applications. Retrieved from https://www.financestrategists.com/wealth-
management/financial-ratios/treynor-ratio/
Thai BMA. (2023). The Thai Bond Market Association. Retrieved from
https://www.thaibma.or.th
Thai Bond Market Association. (2019, September 27). Thai Bond Market
Development. Retrieved from
https://asianbondsonline.adb.org/events/2019/workshop-bond-market-
development-emerging-east-asia/thailands-corp-bond-development-
tiranaprakij-thaibma.pdf
ThaiBMA. (2023, October 15). ThaiBMA iBond Manual. Retrieved from
https://www.thaibma.or.th/document/ibond/8.iBond_Fact%20Figures_EN_Fi
nal.pdf
Thailand Revenue Department. (2023). Jewel Business. Retrieved from
https://rd.go.th/fileadmin/user_upload/kormor/7_Jewel_Business.pdf
217
Yang, Z. (2021). Analysis on CAPM and Sharpe Ratio in Market Investment.
Proceedings of the 6th International Conference on Financial Innovation and
Economic Development (ICFIED 2021).
https://doi.org/10.2991/aebmr.k.210319.002
Yu, J., & Zhang, J. (2023, March 2). A Comprehensive Analysis of The Modern
Portfolio Theory. BCP Business & Management, 38, 2111–2114.
https://doi.org/10.54691/bcpbm.v38i.4046
Zhang, Z. (2022). The Optical Portfolio of the Markowitz Model and the Index
Model. Proceedings of the 2022 7th International Conference on Financial
Innovation and Economic Development (ICFIED 2022).
https://doi.org/10.2991/aebmr.k.220307.248
218
BIODATA
First Name - Last Name Thu Ta Naing
Email thutanaing@artificalab.com
Educational Background Level 4 Diploma in Computing
(NCC Education UK)
Level 5 Diploma in Computing
(NCC Education UK)
First Class Honours, Bachelor of
Science in Business Information
Technology
(University of Greenwich UK)
CFA Level 1 Candidate (CFA Institute)
Working Experience 5 years of experience in Business
Analytics, Data Science, Cloud and AI
CFA Institute Research Challenge
2023-2024 Semi-Finalist (Thailand)
