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Presented to the Department of Financial Management
De La Salle University-Manila
Term 1, A.Y. 2022-2023
A Comparative Assessment of Benjamin Graham's Stock Selection Criteria as Quantitative
Investment Strategy in ASEAN-5 Markets Post Global Financial Crisis: An Examination of
the Defensive and Enterprising Investor Approach
In Fulfillment for the Degree in
Bachelor of Science in Management of Financial Institutions
Submitted by:
Lim, Geoffrey James O.
Malamug, Jose Rafael M.
Panugayan, Ethen Aldrich P.
Submitted to:
Ms. Michelle Brendy Tan
ACKNOWLEDGEMENT
We want to take this space to express our thanks, firstly, to our Almighty God without
Him, this feat would be impossible. We express our greatest gratitude to Mr. John Paul Tanyag,
our beloved thesis adviser who gave us his constant candor and guidance through every step of
making this thesis and pushing us to do better in every step of the way. We express our sincerest
thanks to Dr. Robert Ramos, Ms. Nissa Toledo and Mr. Roderick Pangindian, who served as our
panelists, for their extremely valuable insights and contributions to the improvement of the
thesis. We also express our gratitude to Mr. Tyrone Chan Pao whose work served as a starting
point for this research and to Benjamin Graham, whose endless contribution to the pursuit to
illuminate the workings of the financial markets has served as the bedrock of this thesis. We
would also like to thank our thesis coordinator Ms. Michelle Ocampo Tan for her efforts to make
sure every step of the thesis making process went without hurdles. Finally we are immeasurably
grateful to our friends and family for their unshakeable belief in our abilities, and for their
endless support through the making of this thesis.
ABSTRACT
The main purpose of this research is to examine whether or not Benjamin Graham’s
Stock Selection Criteria is deemed as a strong quantitative investment strategy using the Sharpe
ratio, Jensen's Alpha, Treynor ratio, the beta, Value at Risk and expected returns as performance
metrics that will be computed from the values provided by the method of stationary
bootstrapping. In addition, the study also compares the performance between two of Benjamin
Graham’s stock selection criteria, namely the Defensive and Enterprising Investor from his book,
The Intelligent Investor. The research results show that Benjamin Graham’s Stock Selection
Criteria is not a strong quantitative investment strategy in all of the ASEAN Countries. This is
attributed to the fact that due to the stringent quality of the criteria prescribed by Graham,
creating portfolios based on it only leads to fewer securities. Thus, each portfolio lacks
diversification. Furthermore, based on the performance metrics, it seems that the Defensive
Approach slightly edges out the Enterprising Approach in most of the countries involved in this
study. However, it is worth noting that the performances of the Enterprising and Defensive
portfolios remain uneven in terms of risk and reward as well as in comparisons to the
performances of one anoth
Table of Contents
Chapter I. Introduction
1.1 Background of the Study
1.2 Objectives of the Study
1.3 Statement of the Problem
1.4 Statement of the Hypothesis
1.5 Significance of the Study
1.6 Scope and Limitations
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Chapter II. Review of Related Literature
2.1 Value Investing
2.2 Benjamin Graham’s Investing Strategies
2.3 Defensive Investor Approach
2.4 Enterprising Investor Approach
2.5 Impact of the Global Financial Crisis in the ASEAN-5
2.6 Research Gap
2.7 Literature Map
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Figure 1. Literature Map of Related Literature
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Chapter III. Research Framework
3.1 Theoretical Framework
3.2 Conceptual Framework
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Chapter IV. Methodology
4.1 Research Design
4.2 Sampling Design
4.3 Data Collection
4.4 Method of Data Analysis
4.5 Methodological Limitations
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Chapter V. Results and Discussion
5.1 Descriptive Statistics
5.2 Investor Returns
5.3 Bootstrapped Portfolio Performance
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Chapter VI - Conclusion and Recommendation
6.1 Conclusion
6.2 Recommendation
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References
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Appendix
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List of Figures
Figure 1: Literature Map of Related Literature
Figure 2: Conceptual Framework
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List of Tables
Table 1: Benjamin Graham’s Set of Criteria
Table 2: Summary of Descriptive Statistics, BURSA
Table 3: Summary of Descriptive Statistics, LQ45
Table 4: Summary of Descriptive Statistics, PSEi
Table 5: Summary of Descriptive Statistics, SET50
Table 6: Summary of Descriptive Statistics, STI
Table 7: Summary of Investor Returns Under Weekly Rebalancing
Table 8: Summary of Investor Returns Under Monthly Rebalancing
Table 9: Summary of Investor Returns Under Yearly Rebalancing
Table 10: Summary of Bootstrapped Portfolio, BURSA
Table 11: Summary of Bootstrapped Portfolio, LQ45
Table 12: Summary of Bootstrapped Portfolio, PSEi
Table 13: Summary of Bootstrapped Portfolio, SET50
Table 14: Summary of Bootstrapped Portfolio, STI
Table 15: Summary of Bootstrapped Portfolio Performance, ASEAN-5 Market
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Chapter I. Introduction
1.1 Background of the Study
Warren Buffett is one of the most successful investors of all time. As of May 13, 2022,
Buffett is the world's fifth wealthiest man having a net worth of almost $113 billion (Forbes).
Buffett credits most of his success to his mentor Benjamin Graham, deeming him one of the
most important people in his life and second only to his father.
Being one of Benjamin Graham’s disciples, he was an early adopter of the investing
philosophy that would be known as “Value Investing”. This is an investment strategy that
focuses on purchasing stocks at a lesser price than for what it is actually worth. Warren Buffett
was not alone as an early adopter of this investment strategy. Buffett (1984) wrote an article
detailing the remarkable long term success and consistency in beating the market of his value
investing co-disciples Bill Ruane, Charlie Munger, and Walter Schloss all of whom studied and
practiced under Graham deeming them “the Superinvestors of Graham and Doddsville”.
A study from Everhart (2018) details this further in observing their portfolios through
modern performance metrics and seeing significant success in all the Superinvestors’ portfolios.
The origin of this strategy dates back to the 1920s, and was heavily researched by Benjamin
Graham, along with his colleague David Dodd. In the stock market crash of 1929, all of
Graham’s investments took a hit and it led him to some observations that inspired him to
research and write a book that laid out the fundamental groundwork of value investing, called
“Security Analysis”. The concept of intrinsic value and margin of safety, which were originally
articulated in "Security Analysis," cleared the way for a fundamental analysis of stocks that are
free of speculation at a period when the stock market was understood to be a speculative market
(Kagan, 2019).
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Graham, setting his sights on the individual, wrote The Intelligent Investor in order to
guide those who wish to start their journey in the stock market and equip them with knowledge
in order to avoid becoming speculators. The Intelligent Investor describes two kinds of investors,
the Enterprising Investor and the Defensive Investor, and in turn explores the advantages and
disadvantages of each approach as well as outlining certain principles to adhere to in creating
and managing a portfolio.
To further bolster these principles, Graham outlines specific criteria that a stock must
meet in order to be included in the portfolio of the Defensive or Enterprising Investor. It is
Graham’s belief that ”The rate of return sought should be dependent, rather, on the amount of
intelligent effort the investor is willing and able to bring to bear on his task.” The Intelligent
Investor, dubbed as A Book of Practical Counsel, remains to this day as a foundational work for
practitioners of Value Investing. Almost 50 years after the publication of the fourth and final
edition, there is no doubt about the extent of influence his work has had in modern day investing.
However, there are limits to his work, this final edition paints a market that is a far cry
from today’s more globalized and more integrated markets. Moreover, his work had been
limited to more established markets namely the US. Notably, the current most widely available
edition of the book with commentary by Zweig (2006) is also limited to the US markets and
more importantly, a world not yet ravaged by the 2008 Global Financial Crisis.
To address these limitations, the researchers will refer to the ASEAN-5 region and their
emerging economies. This study puts into practice both of Graham’s stock selection criteria in
creating portfolios made up of the stocks in the markets of the ASEAN-5 as filtered by both the
Defensive and Enterprising Criteria and pitting their returns against that of the index. It will also
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examine if such strategies can be considered as strong quantitative strategies through portfolio
performance measurement.
1.2 Objectives of the Study
● To determine whether the portfolio of stock that meets Benjamin Graham’s Defensive
Stock Selection Criteria in each respective country’s stock exchange within the
ASEAN-5 generates significant positive excess returns compared to the market returns
from 2009-2019.
● To determine whether the portfolio of stock that meets Benjamin Graham’s Enterprising
Stock Selection Criteria in each respective country’s stock exchange within the
ASEAN-5 generates significant positive excess returns compared to the market returns
from 2009-2019.
● To compare both strategies and examine which is more effective as a quantitative
investment strategy.
1.3 Statement of the Problem
There are numerous studies conducted wherein Benjamin Graham’s Strategies were
applied to different stock markets, specifically in the United States and Europe. However, there
are limited studies related specifically to the Defensive and Enterprising Stock Selection Criteria
as suggested by Benjamin Graham that are being applied to the Asian Market. This study aims to
assess Graham's stock selection approach in testing the viability of his stock selection criteria as
a quantitative investment strategy using portfolio performance measurements in each respective
stock exchange of the ASEAN-5.
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1.4 Statement of the Hypothesis
In this study, the following hypotheses will be tested:
Defensive Investor Portfolio
Ho1: The computed Sharpe ratio of a defensive investor portfolio does not significantly
outperform the Sharpe ratio of the Enterprising Investor in each of the ASEAN-5 Markets.
Ha1: The computed Sharpe ratio of a defensive investor portfolio significantly outperforms the
Sharpe ratio of the Enterprising Investor each of the ASEAN-5 Markets.
Ho2: The computed Treynor Ratio of a defensive investor portfolio does not significantly
outperform the treynor ratio of the Enterprising Investor in each of the ASEAN-5 Markets.
Ha2: The computed Treynor Ratio of a defensive investor portfolio significantly outperforms the
treynor ratio of the Enterprising Investor in the ASEAN-5 Markets
Ho3: The computed Jensen's Alpha of a defensive investor portfolio does not significantly
outperform the Jensen alpha of the Enterprising Investor in each of the ASEAN-5 Markets.
Ha3: The computed Jensen's Alpha of a defensive investor portfolio significantly outperforms the
jensen alpha of the Enterprising Investor in each of the ASEAN-5 Markets
Ho4: The computed 95% Value at Risk of a defensive investor portfolio does not significantly
outperform the 95% Value at Risk of the market index of each of the ASEAN-5 Markets.
Ha4: The computed Value at Risk of a defensive investor portfolio significantly outperforms the
95% Value at Risk of the market index of each of the ASEAN-5 Markets
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Ho5: The computed beta coefficient of a defensive investor portfolio does not significantly
outperform the beta coefficient of the Enterprising Investor in each of the ASEAN-5 Markets.
Ha5: The computed beta coefficient of a defensive investor portfolio significantly outperforms the
beta coefficient of the Enterprising Investor in each of the ASEAN-5 Markets
Ho6: The computed expected returns of a defensive investor portfolio does not significantly
outperform the expected returns of the market index of each of the ASEAN-5 Markets.
Ha6: The computed expected returns of a defensive investor portfolio significantly outperforms
the expected returns of the market index of each of the ASEAN-5 Markets
Enterprising Investor Portfolio
Ho1: The computed sharpe ratio of an enterprising investor portfolio does not significantly
outperform the sharpe ratio of the Defensive Investor in each of the ASEAN-5 Markets.
Ha1: The computed sharpe ratio of a enterprising investor portfolio significantly outperforms the
sharpe ratio of the Defensive Investor in each of the ASEAN-5 Markets
Ho2: The computed treynor ratio of an enterprising investor portfolio does not significantly
outperform the treynor ratio of the Defensive Investor in each of the ASEAN-5 Markets.
Ha2: The computed treynor ratio of a enterprising investor portfolio significantly outperforms the
treynor ratio of the Defensive Investor in each of the ASEAN-5 Markets
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Ho3: The computed jensen alpha of an enterprising investor portfolio does not significantly
outperform the jensen alpha of the Defensive Investor in each of the ASEAN-5 Markets.
Ha3: The computed jensen alpha of a enterprising investor portfolio significantly outperforms the
jensen alpha of the Defensive Investor in each the ASEAN-5 Markets
Ho4: The computed 95% Value at Risk of an enterprising investor portfolio does not significantly
outperform the 95% Value at Risk of the market index of each of the ASEAN-5 Markets.
Ha4: The computed 95% Value at Risk of a enterprising investor portfolio significantly
outperforms the 95% Value at Risk of the market index of each of the ASEAN-5 Markets
Ho5: The computed beta coefficient of an enterprising investor portfolio does not significantly
outperform the beta coefficient of the Defensive Investor in each of the ASEAN-5 Markets.
Ha5: The computed beta coefficient of a enterprising investor portfolio significantly outperforms
the beta coefficient of the Defensive Investor in each of the ASEAN-5 Markets
Ho6: The computed expected returns of an enterprising investor portfolio does not significantly
outperform the expected returns of the market index of each of the ASEAN-5 Markets.
Ha6: The computed expected returns of an enterprising investor portfolio significantly
outperforms the expected returns of the market index of each of the ASEAN-5 Markets
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1.5 Significance of the Study
For individual stock investors in the ASEAN
Following in the steps of Benjamin Graham to make investing more accessible to the
laymen, this study will help investors in the ASEAN to determine if the Graham Stock Selection
Criteria would be an appropriate basis for their portfolio management. The results of this
research might also shed light on Benjamin Graham’s stock selection criteria as an effective
quantitative investment strategy.
For investing firms and fund managers
Firms and managers may use the results of the study as a basis for their investing policy
and incorporate aspects of Graham’s stock selection criteria into their portfolio building
strategies as well as discern which countries in the ASEAN-5 may be receptive to the strategy.
Furthermore, the results of this study might add to their strategies.
For future researchers
As Graham developed the tenets of Value Investing, his approach was mainly focused in
the United States since this is where he taught and practiced his craft until his death in 1976, in
line with this fact this study aims to apply his work to a more Globalized approach by using
multiple Asian markets as research locales thus adding to the body of knowledge in ASEAN
Value Investing. This study will add to the body of knowledge regarding value investing in Asian
markets as well as emerging markets. Furthermore, findings from this study may add significant
information for future researchers who decide to conduct studies relating to Benjamin Graham’s
work.
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1.6 Scope and Limitations
In this increasingly globalized market, it is pertinent to reframe Graham’s work through
the lens of emerging markets in the Global South, specifically for this work, the ASEAN-5. With
this, the study focuses on assessing the performance of Graham’s Defensive Investor Stock
Selection Criteria and Graham’s Enterprising Investor Stock Selection Criteria on the composite
indices of the ASEAN-5 stock exchanges namely, Indonesia Stock Exchange (LQ45) , Bursa
Malaysia (BURSA), Philippine Stock Exchange (PSE), Singapore Exchange (STI) and The
Stock Exchange of Thailand (SET50). In order to do so, the returns for the stocks of the major
indices in the ASEAN-5 markets, their respective financial information corresponding to the
Graham's stock selection criteria and the returns of the composite indices will be gathered for the
study coming from various market aggregators such as the Refinitiv Eikon Database.
The researchers limited the research locale to the ASEAN-5 due to their emerging
economies serving as new grounds complementary to Graham’s work in significantly more
established markets in the form of the Dow Jones in the US as observed in The Intelligent
Investor. Thus, this research will not cover the other countries outside of the ASEAN-5. The
researchers chose to limit the study period to a 10-year span starting from 2009 to 2019 because
this period will serve as a test for Graham’s recommendations mimicking the after crisis
conditions. Furthermore, the selected period is based on prior research conducting backtests and
research recommendations to expand the period.
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Chapter II. Review of Related Literature
2.1 Value Investing
Value Investing is a longstanding tradition and philosophy of investing that dates back to
the Great Depression, and was founded by Benjamin Graham, who is considered to be the
“Father of Value Investing”. The strategy uses fundamental analysis, wherein one examines the
financial ratios of a company, as a foundation in recognizing stocks that are considered to be
undervalued. This investing philosophy was expanded further by Warren Buffett, Charlie
Munger and a new generation of successful investors. However, due to its longevity, it begs the
question whether or not this philosophy is still a viable investment strategy in modern markets.
An article written by Michael Mauboussin (2020), states that Value Investing is still alive
and well in today’s market. Value investing has become synonymous with buying stocks with
low valuation multiples and selling those with high multiples in recent decades. However, value
investing should not be confused with just buying stocks with low multiples. One reason for this
confusion comes from the capital asset pricing model. Developed in the 1960s, the model
suggests that there is a positive correlation between risk and reward, wherein, the more risk
investors take, the more they anticipate to be compensated, on average, by an efficient market.
Risk, symbolized by the Greek letter beta, is defined by academics as how much a stock
fluctuates in relation to market fluctuations. A stock with a beta of one will move in lockstep
with the market on average, whereas a beta below one indicates smaller movements and a beta
over one indicates larger changes. The predicted total return of a stock is the reward. Although
the model is appealing in principle, it does not work when applied in practice. Researchers that
put it to the test discovered that average returns for low-risk equities were greater than expected,
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while those for high-risk firms were lower. French and Fama (1992) wrote a paper that added
measure of size and value to the beta in order to right the relationship between risk and reward.
The size factor revealed that small-cap stocks outperform large-cap ones. Stocks with low
multiples performed better than those with high multiples, according to the value factor, which is
defined as a multiple of price-to-book value per share. Sadly, many investors and market
observers still confuse value investing with the value factor years later. The value factor is a
fictitious measure of price-value disparities. Furthermore, the value factor's importance is
diminishing. With this in mind, Fundamental value investors should look for price-to-value
discrepancies in specific assets. The source of value is the present value of future cash flows, not
deceptive multiples. "All good investment is value investing," says Charlie Munger, Warren
Buffett's Berkshire Hathaway partner. The value element may be waning, but value investing
remains as essential and effective as ever (Mauboussin, 2020).
Various studies were also conducted related to Value Investing as an investing strategy.
Most of these studies used financial ratios or fundamental analysis in determining the viability of
this philosophy that was started by Benjamin Graham. In one study, Drevelius and Sorensen
(2018) discovered that previous research has demonstrated the existence of a value premium.
However, their study focused on how to capitalize on this premium in the Swedish stock market.
The study investigated the possible benefits and risks of value investment strategies in the
Swedish stock market from 2006 to 2016, using dividend yield, price-to-earnings (P/E), and
price-to-book (P/B) ratios. The results demonstrated that the value portfolios had unusual returns
within the time period studied. Furthermore, value stocks beat growth stocks when dividend
yield and P/B ratio are used as a criteria in screening stocks. However, as high P/E ratios tended
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to work better than low P/E ratios, the same influence on the P/E ratio could not be established.
Out of all the ratios that were used in their study, companies with the lowest P/B ratios had the
highest risk-adjusted returns. According to the findings of this article, using more ratio-based
criteria in an investing strategy does not result in greater risk-adjusted returns.
Using cross-sectional predictions, Li and Mohanram (2019) merged quality-based
fundamental analysis tactics like the F SCORE from Piotroski (2000) and the GSCORE from
Mohanram (2005) with value-based strategies like the V/P ratio from Frankel and Lee (1998)
and the PEG ratio. While all four techniques produce considerable hedging returns, combining
quality-driven and value-driven approaches increases fundamental analysis' effectiveness
significantly. Their two-dimensional technique is simple and may be applied to a wide range of
equities, outperforming traditional practitioner approaches that require a long time period of data.
The gains in hedge returns are consistent across partitions and are unaffected by risk variables or
other stock return drivers.
Chan Pao (2016) back-tested the strategy of investing in firms with a low
price-to-earnings ratio on the Taiwan Stock Exchange (TWSE), using stock prices from May
2006 to May 2016. From 2006 to 2016, the stock prices of all TWSE listed businesses were
ranked based on this ratio, and the current year's stock prices were compared to the next year's
stock prices. The percentage change in stock prices from year to year was then calculated. The
stocks were then divided into equal-weighted portfolio deciles, with the lowest price-to-earnings
ratios in the first decile and the highest price-to-earnings ratios in the tenth decile. After
backtesting the data, results showed that investing in stocks with a low price-to-earnings ratio
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outperforms the Taiwan Stock Exchange Capitalization Weighted Stock Index on average
(TAIEX). Tortoriello's test for finding a solid quantitative investing strategy was then applied to
the strategy. It met just a handful of Tortoriello's criteria, leading the researcher to conclude that
the price to earnings ratio is not a good quantitative investing approach on its own. Perhaps it can
be linked with a fundamental element or the price to book ratio to develop a more solid
quantitative investment approach.
2.2 Benjamin Graham’s Investing Strategies
Terzi (2016) conducted a study that applied one of Benjamin Graham’s Approaches for
stock selection in the Istanbul Stock Exchange, which is situated in Turkey. The study found that
investors that utilize Benjamin Graham’s Stock Selection Criteria in creating portfolios provided
superior returns than the BIST-100 , which is the stock market index of the Istanbul Stock
Exchange, during the period of 2004 to 2015, excluding the period of the Global Financial Crisis.
The performance and risk of both the Graham stock portfolio and the index was calculated by
using analytical tools, namely the Sharpe Ratio, Treynor Ratio, Jensen Alpha, Beta, and Standard
Deviation. The Sharpe Ratio measures an investment’s risk adjusted return, which means that the
higher the Sharpe Ratio of a portfolio the better its risk-adjusted performance. Results of the
study showed that the Sharpe Ratio of the Graham Stock Portfolio is greater than the
performance of the market index, with the Graham Stock Portfolio managing to achieve a Sharpe
Ratio of 0.07 compared to the BIST-100, which achieved -0.10. As for the Treynor ratio, which
measures how much excess return can be gained for the risk being taken by a portfolio, the
Graham Stock Portfolio has a Treynor Ratio of 0.97. This value is much higher than the Treynor
Ratio of the BIST-100, which is at a negative value of -0.87. Meanwhile, the Jensen Alpha
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represents the average return on a stock portfolio. The portfolio is earning excess returns when
the value of the Jensen Alpha is positive. The Graham Stock Portfolio managed to have a Jensen
Alpha of 1.23, which means that the portfolio gained positive excess returns. The volatility of a
portfolio is measured by beta. A beta of one indicates that the security's price moves in lockstep
with the market. A beta greater than 1 indicates increased volatility, whereas a beta less than one
1 lesser volatility. In financial models that use volatility and risk to predict anticipated returns,
beta is a key component. The beta of the Graham stock portfolio is 0.64, indicating that it is less
risky than the typical market. The standard deviation is a measure of investment volatility. The
standard deviation, also known as historical volatility, is used by investors to assess the degree of
expected volatility. A fund with a high return and a low standard deviation is often appealing.
Because of the lack of diversity, the portfolio standard deviation was larger than the market.
Overall, despite the standard deviation of the Graham Stock Portfolio being higher than the
standard deviation of the index, which is attributed to the lack of diversification, it seems that
using Graham’s Stock Selection Criteria is a viable option when creating a portfolio that
generates better returns than the market.
Rachmatullah and Faturohman (2016) assessed Benjamin Graham’s stock selection
criteria from the book “Security Analysis”, wherein he listed 10 criteria which an investor who
practices value investing can use in selecting stocks. The study applied this stock selection
criteria in order to assess whether or not using Graham’s Criteria generate positive returns in the
Indonesian Stock Exchange. The study used Independent sample t-test, Sharpe Ratio, Treynor
Ratio, and the Capital Asset Pricing Model to examine the different combinations of the 10
Benjamin Graham Criteria and the minimum number of criteria to be fulfilled by a stock in order
for it to be considered as a security that can gain positive returns. Except for the combination of
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discount to net current asset value (NCAV) and steady prior earnings growth, the data reveal that
practically all of Benjamin Graham's risk-reward combinations may be employed by investors to
generate excess profits. Additionally, stocks that match at least two of Graham’s prescribed
criteria can provide investors with excess returns if held for a period of 24 months. Furthermore,
the more Graham criteria a stock meets, the more probable it is to provide a positive excess
return to the investor at least in the Indonesian Stock Exchange.
S. Agarwal and M. Agarwal (2020) used Graham’s Stock Selection Criteria in identifying
undervalued stocks in the NIFTY 500 situated in India. The study applied Graham’s Criteria over
a period of 10 years, from 2010 to 2020, and compared the returns of the portfolio to the
benchmark index of the market which is the NSE 50. Moreover, the study also utilized the
Treynor ratio in comparing both returns of the portfolio and the benchmark index. Using specific
filters that were prescribed by Benjamin Graham in selecting stocks, the results showed that the
returns from the Graham Stock Portfolio beat the benchmark index of the NSE 50 eight out of
the eleven years. Meanwhile, after computing for the Treynor Ratio, the portfolio returns beat the
benchmark index six years out of eleven. Ben Graham's portfolios beat the index in the Indian
Stock Exchange, according to the studies findings. However, the investment holding duration has
an impact on its outcomes. This demonstrates that active actors such as Institutions, Mutual
Funds, and Investment Advisors may employ Graham filters. Over time, passively holding
Graham's portfolios would not provide completely positive returns. Three of the eleven years had
negative returns, demonstrating that Graham's filters can help create an optimum portfolio, but
that negative returns can occur in some years. The beta of portfolios formed utilizing Graham's
criteria was similarly greater. This clearly illustrates that portfolios using Graham's filters have
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higher risk. Portfolios based on Graham's filters outperform portfolios based on Treynor ratio in
six out of eleven years.
Palazzo et al. (2018) evaluated a value investing method for the Brazilian market, picking
stocks based on Benjamin Graham's criterion to avoid lower-quality firms with risks not
represented by existing risk models. An investigation of the Graham filters' applicability in the
domestic market and the criteria at which they should be developed was first carried out using
the Economatica® system database. Palazzos’s study had numerous filters such as for Turnover,
Current Ratio, Uninterrupted Profits, Historical Dividend Payment, Annual Growth of Earnings
per Share, Price/Earnings (P/E), Price/Book Value (P/B) and lastly, Liquidity. The study found
that Filters 2 (current ratio higher than 1.22) and 3 (only profits in the last five years) were found
to be the most important in Graham's stock selection process. Similarly, when considering which
stock selection criteria may be disregarded, it can be inferred that filters 1 (large size) and 6 (P/E
lower than or equal to 7) fall into this category, since when they were removed from a selection
with the other filters, the portfolio composition did not change. At the same time, since filters 4
(uninterrupted dividends in the last five years), 5 (10-year earnings growth of 30%), and 7 (P/E x
P/B lower than or equal to 7) were removed from the selection with all the filters, better
portfolios in terms of risk adjusted return were generated, it can be concluded that a model
without these filters produces superior results to a classification that uses them in the stock
selection. Furthermore, the stock selection model proposed by Graham can be confirmed to be
valid in the current Brazilian market, as portfolios constructed in accordance with this
methodology were able to present a higher risk adjusted return (Sharpe ratio) than the market, as
well as a positive alpha and exposure to systemic risk (beta) lower than 1.00, demonstrating the
validity of value investing as a methodology for picking stocks and thus answering the question.
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Using Graham's selection criterion, Zacharia and Hashim (2017) attempted to give
insight into the value of stock portfolios listed in Saudi Arabia. It looked at how well the stocks
met Graham's main and secondary selection criteria, as well as the potential of BHAR. Two
indices, the EWI and the VWI, were used to improve the accuracy of portfolio measurement. The
time series data used in this research covered a 10-year span from 2000 to 2011. According to
the research, 23 firms out of 160 chosen from the Saudi Arabia stock market met the key
criterion in 2000, however this number rapidly declined to 4 companies in 2011.The findings
show that the NCAV/MV portfolios that were compared to the SAS-EWI market benchmark
outperformed expectations on average by +83.47 percent during a three-year holding period.
When the NCAV/MV portfolios were compared to the SAS-VWI market benchmark, they
likewise had a positive and significant market-adjusted BHAR of +49.02 percent over the
three-year period, while the percentage return was marginally lower. During the period of the
study, this condition suggested that smaller firms outperformed bigger firms on the Saudi Arabia
Stock Exchange.
The applicability of specific combinations of Benjamin Graham's stock selection criterion
on industrial shares trading on the JSE was investigated by Klerck and Maritz (1997). The
information was screened to identify whether businesses met the various requirements. The
returns on the portfolio were then assessed using Jensen's technique of analysis. The findings of
this study revealed that between 1977 and 1994, an investor who used a mix of Graham's criteria
to build a portfolio outperformed the industrial index. It was also shown that not all individual
investments were profitable, and that the total outcomes were sometimes negative. However, at
the ten percent threshold of significance, all of the portfolios analyzed delivered risk adjusted
returns that were much higher than what the asset pricing model predicted.
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2.3 Defensive Investor Approach
The Defensive Investor is characterized by Graham (1973) as primarily focused in
avoiding serious mistakes and losses and secondly as aiming to be free from exerting effort and
making frequent decisions. Zweig (2006) in his commentary of the Intelligent Investor refers to
the Defensive Investor as the “passive” investor whose portfolio is able to run on “autopilot”.
Further describing the Defensive Investor’s portfolio Graham prescribes that the
Defensive Investor place at least a minimum of 25% to a maximum 75% of their allocated funds
to either common Stock or Bonds and the remaining 75% maximum or 25% minimum must be
placed in either Stock or Bonds. To simplify Graham says to begin with a 50-50 split either Stock
to Bond or Bond to Stock and adjust and rebalance the portfolio accordingly. Graham
recommends adjustments be made during times of either protracted bear markets leaning towards
more common stock since they appear as “bargain prices” or when markets are dangerously high
leaning towards bonds. However in times of financial crisis it is recommended that a strict 50-50
approach to the portfolio. The general approach of the Defensive Investor is built on restraint.
Graham dedicates a chapter on how the Defensive Investor benefits from a Common
Stock component and outlines general guidelines for stock selection if the Defensive Investor
were to choose not to simply follow an index or opt for an index fund. Graham explains that the
Common Stock component is meant to shield the portfolio from the effects of inflation on the
bond yields. Furthermore, he presents data that shows higher average returns for common stock.
Graham outlines general guidelines for the common stock portion of the Defensive Investor’s
portfolio
1. A minimum of 10 up to a maximum of 30 different common stocks
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2. Generally, large, prominent and conservatively financed companies
3. Must have had 20 years of continuous dividend payments
4. A limit on the price of 25x the average earning and 20x of the last 12 months
The first rule puts a focus on diversification as a tactic for the Defensive Investor. The second
rule Graham addresses as being vague however he employs a measure for so called “financially
conservative” companies Graham suggests that at book value the common stock must reflect
50% of its total capitalization including its bank debt, for a company to be considered “large” he
suggests 50 million in assets or 50 million in market capitalization. Zweig commented that in
2006 for a firm to be considered large it must have at least 10 billion in market capitalization.
Prominence is measured by Graham as ranking as first quarter or first third in size in their
respective industry. The last rule is meant to eliminate “Growth Stocks” as an option for the
Defensive Investor. Graham is averse to these since they have shown a history of losing market
value in a short amount of time thus not a good fit for the Defensive Investor who wishes to be
passive in their portfolio management.
The following methods for stock selection is outlined by Graham in The Intelligent
Investor in order to put into practice the base principles of the Defensive Investor approach to
portfolio building:
1. Adequate Size of the Enterprise
2. A Sufficiently Strong Financial Condition
3. Earnings Stability
4. Dividend Record
5. Earnings Growth
6. Moderate Price/Earnings Ratio
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7. Moderate Ratio of Price to Assets
The first criterion sees that during the process of selecting common stocks, one must
consider the size of the company. The main focus is to exclude companies which are deemed as
small and put more emphasis on companies that possess assets amounting to 50 million and
achieve annual sales figures of at least 100 million. The second criterion focuses on the financial
condition of the company, it suggests that current assets should at least double more than current
liabilities to be deemed its financial condition as sufficiently strong. Furthermore, long term debt
should not exceed its net current assets. The third criterion focuses on the company's earning
stability, wherein there should be positive earnings in its common stock in each of the past ten
years. The fourth criterion demands that one should also look into the company’s dividend
payments, wherein it should be uninterrupted for at least the past 20 years. The fifth criterion
sees that there is a minimum increase of at least one-third in per-share earnings in the past 10
years. The sixth criterion demands that the current price should not be more than 15 times
average earnings. The seventh criterion means that current price should not be more than 1½
times the book value. Graham provides as a rule of thumb, that the product of the multiplier
times the ratio of price to book value should not exceed 22.5.
Graham details the rationale behind these criteria as a means to gather stocks that exhibit
a “minimum of quality” in past performance as well as current financial position and also a
“minimum of quantity” in earnings and assets per dollar of price. The first five are measures of
quality which are meant to weed out companies that are too small in size, possess a weak
financial condition, a deficit stigma and those who have not paid out dividends. In short, the first
four criteria are meant to exclude underperformers that may bear a risk to the Defensive
Investor’s portfolio. The last two criteria are Graham’s measure of quantity which are in place to
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reduce the presence of so-called growth stocks in the Defensive Investor’s portfolio. Graham
explains the aversion to growth stocks by saying that there is an “absence of an adequate factor
of safety when too large a portion of the price must depend on the ever increasing earnings in the
future.” In the use of these two measures the Defensive Investor demands that the company has
more assets and more earnings reflected in the price thus proving the company to be more stable.
2.4 Enterprising Investor Approach
The Enterprising Investor is an investor that exerts more effort and is more active and
thus sees relatively exemplary returns compared to the Defensive Investor following Graham’s
belief that “The rate of return sought should be dependent, rather, on the amount of intelligent
effort the investor is willing and able to bring to bear on his task”. The Enterprising Investor is
described by Graham (1973) as the investor willing to “devote time and care to the selection of
securities that are both sound and more attractive than the average.”
Graham splits his discussion of the Enterprising Investor Approach into a positive and a
negative side. The negative side as the name suggests is a process of negation. In this discussion
Graham prohibits the Enterprising Investor from pursuing certain financial instruments. They are
as follows:
1. High grade preferred stocks
2. Inferior bonds unless they can be bought at a bargain
3. Foreign government bonds
4. Newly issued shares
5. Stocks that have excellent earnings confined to the recent past
22
Graham justifies the first security by saying that they are more suited for corporate
buyers. For the second one, bonds priced at least 30% under par, Graham justifies the purchase
of inferior bargain bonds because they usually have severe sinking spells during bad markets
thus resulting in a bargain but a large proportion of these inferior bonds eventually recover their
position when favorable conditions returns however Graham also says that the purchase of good
grade bonds at significant discount may yield the same amount in terms of risk and reward. For
the third security, it is Graham’s concern that in the event the foreign government were to have
turmoil economically or otherwise there would be no way to enforce the investor’s claim.
Graham expresses his concern for the fourth security in saying that new issues are mostly sold in
“favorable market conditions'’ only for the issuer and not the buyer. Graham is also wary that
they have a “special salesmanship” thus sellers are more incentivized to push these new issues to
the investor.
The Positive side is where Graham describes 4 situations in which the Enterprising
Investor is now allowed to buy
1. Buying in low markets and selling in high markets
2. Buying carefully chosen growth stocks
3. Buying bargain issues of various types
4. Buying into special situations
Graham sees the potential of entering the market in a depressed state and exiting it in the
advanced stages of a boom since this is when stocks and bonds are sold at a bargain price.
Graham while apprehensive towards growth stocks in the Defensive Approach Graham sees their
potential when picked wisely, these companies identified as companies with a good record and
“good prospects” as well as “outperforming the averages”, however Graham also warns that the
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investor may only be able to breakeven or worse overpay for the growth stock since he believes
that “unusually rapid growth cannot last forever” and that the growth curve eventually flattens
out or turns downward. In order to identify growth stocks with potential for the Enterprising
Investor Graham gives a limit of a P/E ratio no higher than 20, otherwise the investor may be
overpaying for the company since this is a sign that the prospects of the company have already
been reflected in the price. Graham further recommends that the Enterprising Investor participate
in these three situations as much as possible:
1. Relatively unpopular large companies
2. Purchase of bargain issues
3. Special situations
Graham emphasizes large companies that are going through a period of unpopularity since they
have a double advantage for the Enterprising Investor. Firstly, due to the nature of its size the
company has the capital and brain power to carry them through that period of unpopularity and
secondly, the market has been very responsive to any improvement for that company.
Graham suggests that this issue be accompanied by a low P/E ratio. Graham describes
Bargain Issues as any issue selling well under par, however, to qualify for the Enterprising
Investor he dictates that they must be firstly, undervalued he describes two markers for
undervalued companies, currently disappointing results and unpopularity. Secondly, he details
the optimal bargain issue would belong to a large and prominent company selling below its past
average price and its past average P/E. Graham then identifies a more specific measure for a
bargain issue which is an issue that sells for less than the company’s net working capital. Graham
describes an issue in a Special Situation as issues tied up in complicated legal proceedings. One
of these situations would be having an issue with a company that is about to be acquired by
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another larger company. Graham says that it is almost always necessary to offer a price
considerably above the current level for the share since there is a need to acquire a majority of
the shares of the smaller company.
In order to put into practice these principles Graham outlines a set of criteria that may aid
the Enterprising Investor in selecting the stocks for his portfolio
1. Low multiplier: A P/E ratio of less than 10x
2. Financial Condition: Current assets at least 1.5 times current liabilities and debt not more
than 110% of net current assets
3. Earnings stability: No deficit in the last 5 years
4. Dividend record: Some current dividend
5. Earnings growth: Last year’s earnings more than those of 5 years ago.
6. Price: less than 120% net tangible assets.
Stock Selection Criteria
Although not explicitly stated by Graham (1973) in editions of the Intelligent Investor it
is heavily implied by the central thesis of The Intelligent Investor that, the rate of return sought is
corresponding to the amount of intelligent effort an investor will pursue, that the stock selection
criteria for both the Defensive and Enterprising Investor are to be used separately from one
another. Although the stock selection criteria may have overlaps in terms of what type of
screeners are used, for example, the P/E ratio. The value of the ratio greatly differs from each set
of criteria since the criteria are tailored specifically to the temperament and approach required by
either of The Intelligent Investors. This similarity is a result of Graham's focus on the price
relative to a company's intrinsic value as is the norm with Value Investing. Furthermore, as the
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central thesis once more implies it is of note that the Defensive criteria is meant to show a
minimum of returns while the Enterprising criteria is meant to show more promising returns thus
Graham implies that while not exactly meant to compete the expected returns for either criteria
may be different. However, as will be shown in this review of related literature studies observing
the Defensive criteria have been seen to outperform the market. Zweig (2006) in his commentary
of The Intelligent Investor, mentions that "both approaches are equally intelligent, and you can
be successful in either - but only if you know yourself enough to pick the right one and stick with
it over the course of your investing lifetime" thus he implies that the approaches and
subsequently the criteria must be used separately from each other.
2.5 Impact of the Global Financial Crisis in the ASEAN-5
Impact on Indonesia
Djaja (2009) investigated how the financial crisis and the global economic slowdown
affected the Indonesian economy. As a starting point, the study will use data from 2007. Then, as
much as data is available, look at the first three quarters of 2008, with an emphasis on the third
and fourth quarters. The article indicated that in 2007, the Consumer Confidence Index was
106.1. The CCI then fell in two consecutive quarters, to 95.0 and 93.8 in the first and second
quarters of 2008, due to rising food and energy costs in both the global and local markets. From
July to October, the CCI began to rebound, reaching 102.8 in the third quarter of 2008. The
article also showed that the economy grew by 6.3 percent in 2007, the greatest rate since the
Asian economic crisis rocked Indonesia in 1997. Growth seems to be stable and strong in the
following three quarters of 2008; 6.3 percent in the first quarter, then 6.4 percent and 6.1 percent
in the second and third quarters, respectively. This suggested that despite the negative picture
painted by the asian economic crisis, Indonesia's economic performance was rather good. In the
26
first half of 2008, Indonesia was the only major East Asian economy that did not suffer a growth
slowdown.
Impact on Malaysia
Sharif et al. (2016) assessed the effect on seventy-seven Bursa Malaysia stock market
companies before and during the crisis. The stock prices were obtained from Thomson Reuters'
Data stream. There are 850 firms listed on Bursa Malaysia, however only 77 companies with
market values over RM500 million were considered for this research. The study used two data
sets which were the 2007 and 2008 data. The differences of the covariance structures were tested
with the use of S* statistic which was created for high-dimensional data sets like the stock
market. The test found that the covariance structures of 2007 and 2008 had significant
differences. The findings showed that the HWAN stock is the most decisive element in 2007
while the MRES stock is the most dominant in 2008. The movement of HWAN stock and MRES
stock from the crisis had a significant influence on the stock market's stability structure.
Alp et al. (2012) found that Malaysia was severely affected by the 2008–2009 global
financial crisis. Bank Negara Malaysia (BNM) allowed the currency rate to devalue as money
flowed out and dropped the policy rate by 150 basis points in anticipation of the recession that
would follow the period of significant financial turmoil. This article attempts to calculate how
much more severe the recession would have been if not for the BNM's monetary policy reaction.
Using the most acute year of the crisis as a baseline, counterfactual models imply that growth
would have been –3.4 percent had the BNM not conducted countercyclical and discretionary
interest rate decreases. In addition, models imply that production would have fallen by -5.5
percent during the same four-quarter period if a fixed exchange rate system had been in place. In
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other words, exchange rate flexibility and BNM-implemented interest rate reduction significantly
mitigated the effect of the global financial crisis on the Malaysian economy.
Impact on the Philippines
Yap et al. (2009) examined the effects of the global financial crisis of 2008 and the
subsequent recession in various nations on the performance of the Philippine economy. The
study found that developing country exports plummeted, drawing many of them into the global
economic crisis. The Philippines was not immune to the effects of the crisis, with GDP growth
slowing significantly in the fourth quarter of 2008 and the first half of 2009. Although asset
values fluctuated, the financial sector remained relatively stable, unlike the 1997 East Asian
crisis. Unemployment rose marginally, but it was more noticeable in the industrial sector, which
bore the burden of the recession mostly via exports. Remittances from abroad Filipino workers,
on the other hand, continued to rise, although at a slower pace. Despite the drop in exports and
increased capital outflows, foreign currency reserves continued to rise. The rising fiscal
imbalance is a source of worry, owing to the need to boost government spending to compensate
for decreasing consumption, investment, and exports. The authors even recommended that the
global financial crisis of 2008 has brought two issues to the forefront: fiscal changes, notably
steps to boost resources required to accomplish the Millennium Development Goals, and
measures to restore private investment in the Philippines.
Impact on Singapore
Mah-Hui and Maru (2010) was able to expose fundamental weaknesses in the world
financial system as well as the export oriented growth of many Asian countries. The research
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also pointed out the following elements that allowed Singapore to preserve exchange rate
flexibility while maintaining exchange rate stability. For starters, Singapore's macro and
microeconomic policies were solid and credible, reducing the country's vulnerability to
short-term volatility. The employment of monetary tools in combination with other more direct
approaches to deal with economic difficulties is the next point. Finally, Singapore has a stable
banking system with a low loan-to-deposit ratio, strong capital adequacy, little currency
mismatch risk, low non-performing asset ratio, and a rising capital market to support the banking
sector.
Impact on Thailand
Chandoevwit (2010) examined the impact of the Global Financial Crisis and policy
responses in Thailand. Several factors were considered such as the employment sector,
percentage change in hours of employment (1996-1998 and 2007-2009), number of employees,
average hours of work of employees in manufacturing, overtime income of employees in
manufacturing (by age group and education), unemployment rate (1996-1998 and 2007-2009),
etc. The literature found that its impact on economic growth was low relative to the 1997
financial crisis, and that it had a short-term impact on manufacturing jobs. The social groupings
associated with the industrial industry faced negative consequences. Employees between the ages
of 20 and 29, as well as those with a secondary or technical degree, lost money due to a
reduction in overtime hours worked. Because of the limited coverage of the Social Security Law,
government measures to boost employment may be ineffective. The impact of stimulus
package-I is unclear because of the weak short-term multiplier effect.
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2.6 Research Gap
One of the prevalent research gaps that was identified by the researchers is the limited
studies on Benjamin Graham’s Defensive Stock Selection Criteria that is being applied to the
ASEAN countries. Moreover, there seems to be no studies related to Benjamin Graham’s
Enterprising Stock Selection Criteria. Due to the lack of studies regarding both Benjamin
Graham’s Defensive and Enterprising Stock Selection Criteria, the purpose of this research is to
contribute to the study of assessing the performance of Benjamin Graham’s Strategies,
specifically the Defensive and Enterprising Stock Selection Criteria, in the ASEAN countries.
30
2.7 Literature Map
Figure 1. Literature Map of Related Literature
31
Chapter III. Research Framework
3.1 Theoretical Framework
The study gathered theories which are relevant towards the study. The researchers intend
to use all the theories presented below as an anchor for this study in assessing the viability of
Benjamin Graham’s Defensive and Enterprising Stock Selection Criteria as an Investment
Strategy in the ASEAN-5 countries.
Efficient Market Hypothesis
According to Fama (1970) the Efficient Market Hypothesis is the proposition that all
available information is simultaneously incorporated into the market price of a security. This
information such as financial news and research as well as political, economic and social events
are all assumed to be instantaneously incorporated into the stock price. This hypothesis
developed by Fama (1970) ascribes to the assumption that since all market participants already
have the access to the same information stocks tend to trade at their fair value already thus it
would be impossible for investors to purchase or sell either undervalued or overvalued stocks.
The Efficient Market Hypothesis would render all past information as useless thus neither
technical analysis, financial analysis based on historical price, nor fundamental analysis,
financial analysis based on financial data, would be rendered useless. Under EMH it would be
impossible to achieve abnormal returns thus the optimal portfolio for all investors would be the
market portfolio itself.
Value Investing
Graham (1973) outlines the central principle of Value Investing as the margin of safety,
this is the difference between market price and the intrinsic value of a stock. Graham employs
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methods of fundamental analysis such as observing the P/E ratio and P/B ratio in order to
determine stocks that are undervalued. The concept of undervaluation and intrinsic value is at
odds with the Efficient Market Hypothesis because the existence of these proves that the market
is inefficient thus excess returns are able to be achieved. Furthermore, the constant success of
Graham’s value investing proves counter to this assumption Rachmatullah (2016) cites previous
studies in the field of value investing which detail portfolios outperforming the market average
thus providing evidence of inefficiencies in the market. He cites Oppenheimer (1984) which
proved that Benjamin Graham‘s 10 stock selection criteria generated excess returns compared to
the market. Furthermore, Klerck and Maritz (1997) found similar results of outperformance in
South Africa and Singh and Kaur (2014) in Indian markets. Chan Pao (2016) cites Greenwald,
Kahn, Sonkin and van Biema (2004) in which they observed how value investing using low Price
to Earnings (P/E) ratio or low Price to Book (P/B) ratio produce better results than the market
returns.
Capital Asset Pricing Model
Fama (1970) emphasized that in order to prove the Efficient Market Hypothesis, the
excess returns of a portfolio must be tested, this is because when there is no evidence of excess
return it is an indication that markets are efficient thus proving that the markets are efficient. The
excess return is measured by getting the difference between the expected and actual return.
According to Rachmatullah the expected return is adjusted for risk using the capital asset pricing
model, according to this model the correct measure of risk for a stock is the stock‘s beta – that is,
the extent to which the returns of a stock is correlated with the returns of the market as a whole.
33
Thus in order to further prove the Efficient Market Hypothesis no significant risk adjusted
returns should be found.
Established by William Sharpe (1963), the Capital Asset Pricing Model was created to
assess the relationship between risk and return. This means that the model is based on the
assumption that there is a linear relationship between expected returns and systematic risk of any
financial asset, at least in a market that is deemed to be efficient. Moreover, the model
demonstrates that for a given degree of risk, higher returns than those expected for the amount of
risk taken are not attainable on average.
The equation below shows the CAPM's specification:
𝑅𝑖, 𝑡 − 𝑅𝑓, 𝑡 = α𝑖 + β𝑖(𝑅𝑚, 𝑡 − 𝑅𝑡) + ε𝑖, 𝑡
where:
Ri,t = the return of portfolio i in month t;
Rf,t = the return of the risk-free asset in month t;
RM,t = the return of the market portfolio in month t;
αi = the intercept of the regression equation for portfolio i (or Jensen’s alpha);
βi = the slope of the regression equation for portfolio i (traditionally called beta);
εi,t = the error term (assumed to be a white noise process with normal distribution, zero mean
and constant variance).
The Jensen’s Alpha developed by Jensen (1967) was the first way to measure the
effectiveness of an investment strategy through the use of the CAPM. in measuring the
regression’s intercept, Jensen was able to assess that a statistically significant alpha compared to
expected returns is a sign that the strategy produces excess return.
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3.2 Conceptual Framework
Figure 2. Conceptual Framework of the Study
The independent variables needed for the study will include the financial data to be
collected from the stocks in the major indices of the ASEAN-5. These financial data consisting
of fundamentals outlined in Graham’s Stock Selection Criteria for either Defensive or
Enterprising Investor will be used to sift through the stocks of the major indices and form equally
weighted Defensive and Enterprising portfolios. In order to measure their returns, the daily
closing price of the stocks for the entire period will be gathered. The market will also be
observed as a benchmark for the performance of the portfolios thus its daily closing price will
also be gathered.
Once the portfolios have been established as well as the pertinent data from the market is
gathered, the portfolio returns are calculated for the entire period and the performance metrics
comprising each portfolio and the markets’ Treynor Ratio, Jensen Alpha, Value at Risk, and Beta
will be calculated for the entire period as well. In order to conduct hypothesis testing, the results
35
of the performance metrics for both market and both portfolios will have to undergo stationary
bootstrapping. This will result in the t-test p-values for the performance metrics. Thus, answering
if the Defensive Investor and Enterprising Investor Stock Selection Criteria are able to
consistently outperform the Market and deemed a viable quantitative investment strategy.
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Chapter IV. Methodology
4.1 Research Design
This study examines the performance of the Defensive Investor and Enterprising Investor
Stock Selection Criteria as prescribed by Benjamin Graham in The Intelligent Investor in the
ASEAN-5 through testing its returns against that of the market as represented by the major
indices of the ASEAN-5 through the lens of quantitative research. The study will observe if the
Defensive and Enterprising Portfolios would be able to generate excess returns as well as
observing the quality of returns on performance metrics. Through this quantitative research, it
would determine if the portfolio would be able to significantly outperform the market of the
ASEAN-5 and generate statistically significant excess returns.
The researchers will gather the pertinent financial information from the stocks of the
major ASEAN-5 indices based on the prescriptions made by Graham in reference to the
Defensive and Enterprising Investor stock selection criteria from the periods of 2009 to 2019.
The selected stocks will then be formed into equally weighted portfolios. The researchers will
then be comparing the performance of both portfolios against the corresponding indices of each
of the ASEAN-5 exchanges, based on the following measures:
● Sharpe Ratio
● Treynor Ratio
● Jensen Alpha
● Value at Risk (using the EVT-POT Method)
● Beta
● Portfolio Returns
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In order to facilitate this analysis, the researchers will be conducting the method by Politis and
Romano (1994) on the stationary bootstrap.
4.2 Sampling Design
The study will be employing the use of purposive sampling since the data is limited to
only the returns of the specific stocks in the indices and the returns of the indices themselves thus
the entire market of stocks in the ASEAN-5 is not considered for the study. Furthermore, the
sample to be used must follow a set of definite criteria in order to be included in the study.
Seeing as the data to be collected must first be screened through the Defensive and Enterprising
Stock Selection Criteria the researchers found that the use of a purposive sampling technique in
order to conduct their study would be appropriate. This technique is mainly used in studies that
are selective and particular in the characteristics of the sample data extracted.
4.3 Data Collection
The researchers will gather the pertinent financial data from the stocks in the ASEAN-5
indices based on the prescriptions made by Graham in reference to the stock selection criteria of
either the Enterprising or the Defensive Investor and are as follows:
Investor Stock Selection Criteria
Defensive Investor Stock Selection Criteria
Enterprising Investor Stock Selection Criteria
1.
At least a current ratio of 2 and a
Long-term debt to the net current asset ratio
of less than 1
1.
At least a current ratio of 1.5 and a
debt to the net current asset ratio not more
than 1.1
2.
Some earnings for the common stock
in each of the past 10 years
2.
No earnings deficit for 5 years
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3.
Uninterrupted dividend payments for
at least the past 10 years
3.
Some current dividend
4.
A minimum increase of at least
one-third in per-share earnings in the past 10
years
4.
Last year’s earnings more than
earnings from 5 years ago
5.
P/E ratio should not be more than 15
5.
P/E ratio should not be more than 10
6.
P/B should ratio not be more than 1.5
6.
P/TBV ratio should be less than 1.2
7.
Not less than 100 million of annual
sales.
Table 1. Benjamin Graham’s Set of Criteria
The following indices and the stocks detailed in them will be used for the study: the PSEI
for the Philippine Stock Exchange, the Straits Times Index (STI) for Singapore, the SET50 Index
for Stock Exchange of Thailand, the LQ45, for Indonesia, the FTSE Bursa Malaysia KLCI Index
for Malaysia. The researchers will be obtaining daily portfolio value for the Enterprising and
Defensive Investor. The researchers will also obtain daily prices of the corresponding market
indices After doing the selection criteria, the researchers will begin filtering the stocks and group
them into equally weighted portfolios based on the Defensive Investor Stock Selection Criteria
and Enterprising Investor Stock Selection Criterion. The researchers also decided to make
adjustments to the Uninterrupted dividend payments criteria from the Defensive Approach.
Originally stated that it should be at least 20 years, the researchers decided to reduce the number
of years to 10 due to how stringent it was.
4.4 Method of Data Analysis
Following methodologies from a study conducted by Terzi (2016), the researchers will
employ performance measures to the portfolios based on Graham’s Criteria and market indices.
Moreover, the researchers will also employ the method of statistical bootstrapping by Politis and
39
Romano (1994) in order to determine if the returns of the Graham portfolios are statistically
significant in beating the market.
Performance Metrics
In order to measure the performance of each Portfolio and Index the following formulas
will be used:
Sharpe Ratio
The Sharpe Ratio was created by William F. Sharpe for the purpose of helping investors
comprehend an investment’s return in relation to its risk. The Sharpe ratio is a measure of excess
portfolio return relative to its standard deviation above the risk-free rate (Fernando, 2022). A
higher Sharpe ratio indicates that a fund's returns are better. The formula of the Sharpe Ratio is
shown below:
𝑆𝑅 =
𝑅𝑃− 𝑅𝑓
σ𝑝
Wherein the risk-free rate Rf is subtracted from the return of the portfolio Rp, which then will be
divided by the standard deviation σP of the portfolio’s excess return. The standard deviation is a
measurement of how far the portfolio's return differs from the expected return. The standard
deviation also reveals the volatility of the portfolio (Lo, 2002).
Treynor Ratio
The Treynor ratio is a performance metric used to calculate how much excess return a
portfolio generated for each unit of risk it took on. The Treynor ratio is essentially a risk-adjusted
40
return measurement based on systematic risk. It shows how much money an investment made for
the amount of risk it took on. The formula of the Treynor Ratio is shown below:
𝑇=
𝑟𝑝−𝑟𝑓
β𝑝
The Treynor Ratio, unlike the Sharpe Ratio, uses the Beta Bp as the denominator instead of the
standard deviation of the portfolio. This means that only the market risk that the portfolio will be
exposed to is incorporated into the formula. The higher the Treynor Ratio of a portfolio, the
better risk adjusted return (Kenton, 2020).
Jensen Alpha
Michael C. Jensen invented the term "Jensen's Alpha" in 1968. The capital asset pricing
model uses Jensen's index to compute the needed (excess) return of a stock, investment, or
portfolio. The security market line is used as a benchmark in the Jensen index. This metric was
initially used to evaluate mutual fund managers in the 1970s. This methodology is used to adjust
the degree of beta risk, resulting in greater projected returns for riskier equities. It enables the
investor to determine if their portfolio generated an abnormal return when compared to the total
capital market (Shahid, 2007). The formula for the Jensen Alpha is shown below as represented
in Terzi (2016):
α = 𝑅𝑝 − [𝑅𝑓 + β𝑃(𝑅𝑚 − 𝑅𝑓)]
Wherein:
Rp = The expected return of portfolio
Rf = Risk Free Rate
ßp = Beta of the Portfolio
Rm = Market Return
41
Value at Risk
Linsmeier and Pearson (2000) stated that VAR is a single statistical measure of potential
portfolio losses that is summarized. An entity's VAR is the loss that is predicted to be surpassed
with a likelihood of just x percent over the following t-day holding period, given a probability of
x percent and a holding time of t days. Loosely, it is the loss that is expected to be exceeded
during x percent of t-day holding periods. Var is the upper alpha-level quantile of the generalized
pareto distribution. It’s the worst case possible loss of an investor in a given portfolio. The Value
at Risk is a good indicator of volatility. It measures the worst case possible loss (Singh et. al,
2011).
Beta
The metrics beta is a measure of a security's or portfolio's volatility or systematic risk
relative to the market as a whole, and is largely employed in the capital asset pricing model. A
beta more than one indicates high risk, beta equal to one indicates moderate risk, and beta less
than one indicates low risk (Kenton, 2021). The formula for the beta is shown below as
represented in Terzi (2016):
β=
𝐶𝑜𝑣(𝑟𝑖−𝑟𝑚)
𝑉𝑎𝑟(𝑟𝑚)
Wherein:
Cov = Covariance
Var = Variance
42
ri = Expected Return on Portfolio
rm = Average expected return of the market
Portfolio Returns
The gain or loss generated by an investment portfolio encompassing several kinds of
assets is referred to as portfolio return. Portfolios are designed to provide returns based on the
investment strategy's stated goals as well as the risk tolerance of the portfolio's target clients.
(Chen, 2020). The Expected Return measures the projected amount a portfolio may earn based
on the historical returns of the stocks in a portfolio and their weights. In order to compute it, the
average of the portfolio returns for the entire period will be gathered. The Portfolio Returns are
calculated below as represented in Terzi (2016).
𝑅𝑝 =
𝑅𝑝𝑡−𝑅𝑖(𝑡−1)
𝑅𝑖(𝑡−1)
Wherein:
RP = The expected return of the portfolio
Rpt = Closing Value at t day
Ri(t-1) = Closing Value at (t-1) day
Stationary Bootstrapping
The stationary bootstrap provides a way of producing multiple different observations of a
specific distribution based only on the empirical data available. The bootstrap was initially
proposed by Efron (1979) and later improved for various different applications in computational
statistics. One particular weakness of the original bootstrap is that it considered variables that
43
were assumed to be, in statistical terms, independent and identically distributed. This limits the
application of the bootstrap to mostly cross-sectional data, as it fails to take into account the
possible serial correlations that a time series data would typically have.
Politis and Romano (1994) provide a workaround to this using the stationary bootstrap.
{
}
Given a time series 𝑋𝑡, 𝑡∈𝑍 , we can perform a hypothesis test against a statistic 𝑇𝑁 computed
(
)
over the time series 𝑇𝑁 𝑋1, 𝑋2, …, 𝑋𝑁 by producing 𝐵 different “realizations” of the time series
{𝑋𝑡, 𝑡∈𝑍}, also known as “pseudo time series” that follows the distribution and correlation
{
}
structure of 𝑋𝑡, 𝑡∈𝑍 .
A block of length 𝑏 is constructed from the original time series as
{
}
𝐵𝑖,𝑏 = 𝑋𝑖, 𝑋𝑖+1, ... 𝑋𝑖+𝑏−1
{
*
*
*
}
The stationary bootstrap creates a new time series 𝑋1, 𝑋2, …, 𝑋𝑁 by taking random samples of
𝐵𝑖,𝑏 until the total length reaches 𝑁, the original length of the time series. Thus, 𝐵 different time
series will be constructed, with each being used to estimate the value of the statistic 𝑇𝑁. Thus,
consider the Treynor Ratio, which is estimated from the series using
(
)
𝑇𝑅 = 𝑅𝑝 − 𝑅𝑓 β𝑝
44
where 𝑅𝑝 is the expected return of the portfolio, 𝑅𝑓 is the risk-free rate, and β𝑝 is the beta
coefficient of the portfolio. Through the stationary bootstrap, 𝐵 different values of the Treynor
Ratio will be evaluated on each of the 𝐵 pseudo time series, resulting in the set
{𝑇𝑅1, 𝑇𝑅2, …, 𝑇𝑅𝐵}
For each portfolio. A t-test on the portfolio Treynor Ratios can then be performed using the set of
𝐵 observations. The same will be conducted to test the performance of the other portfolio
metrics.
The overall flow of the analysis will then be as follows:
1. Data Collection
a. Obtain daily portfolio value for the Enterprising and Defensive Investor
b. Obtain daily prices of the corresponding market indices
c. Produce the returns for both the portfolios and the index using the percentage
(
)
change formula: 𝑅𝑡 = 𝑦𝑡 − 𝑦𝑡−1 /𝑦𝑡−1 where 𝑦𝑡 is the price of the
index/portfolio at time 𝑡.
2. Descriptive Statistics
a. Compare the performance of the portfolios based on the proposed metrics on the
present market data
3. Bootstrapping:
a. Produce 𝐵 pseudo time series based on the collected portfolio and index data
b. For each of the pseudo-time series, produce the value of the metrics
45
c. Perform t-test between the portfolios and the indices from the produced 𝐵
realizations of the portfolio metrics.
4.5 Methodological Limitations
Due to the nature of the study, the methodology does not consider external factors such as
the country's economic performance or internal events in the firms covered by the study.
Furthermore, as part of the comparative aspect of the study the researchers look to continuous
periods containing both at least one Defensive Investor and Enterprising Investor security. Thus,
this study does not consider periods where only securities from either Defensive or Enterprising
is available, both approaches should be available.
46
Chapter V. Results and Discussion
This chapter shows the results and discussion regarding the viability of Benjamin
Graham’s Defensive and Enterprising Criteria as a Quantitative Investment Strategy using
historical stock prices from 2009 to 2019 based on the following performance metrics: (1)
Sharpe Ratio, (2) Treynor Ratio, (3) Beta, (4) Alpha, (5) Value at Risk, and (6) Expected
Returns.
The chapter will also include the outcomes of the analysis of the data collected for the
research findings. Prior to performing the analysis, a set of securities were first identified based
on stated criteria for the enterprising and defensive investors as prescribed by Benjamin Graham.
The years that will be included in the comparative assessment are the stocks of STI from 2013 to
2019 and the stocks of BURSA, LQ45, PSEi and SET50 from 2016 to 2019. This is due to the
requirement that in order to be included in the comparative assessment, the Enterprising and
Defensive Stocks must both have at least one stock every year and be in a sequence. This
resulted in a total of six securities for the Malaysian (BURSA) market, eight securities for
Indonesian (LQ45) market, three securities for the Philippine (PSEI) market, five securities for
Thailand (SET50) market, and only two securities for the Singaporean (STI) market.
After gathering the necessary data, the researchers conducted a descriptive analysis and
conducted daily, weekly, and monthly rebalancing for returns of the stock prices; this was done
in order to illustrate how the portfolios of both defensive and enterprising approaches will fare
against the portfolio of the Market Index for each market.
The data will be tested using the stationary bootstrap method, wherein it provides a way
of producing multiple different observations of a specific distribution based only on the empirical
data available. This section will also interpret and explain the values presented in each table and
47
whether or not Benjamin Graham’s Criteria is deemed as a strong Quantitative Investment
Strategy in the ASEAN-5 Market.
5.1 Descriptive Statistics
The descriptive statistics for each of the securities and their corresponding indices are
provided below. The researchers used the stock prices from the period of 2009 to 2019.
5.1.1 Malaysian Market (BURSA)
Type
Market
Asset
BURSA
of
Criteria
Mean
SD
Min
Max
0.000
0.015
-0.081
0.058
Defensive and
GENT
Enterprising
Defensive and
PGAS
Enterprising
0.000
0.013
-0.060
0.067
PEPT
Enterprising
0.000
0.010
-0.200
0.053
HLCB
Defensive
0.000
0.012
-0.054
0.055
HTHB
Enterprising
-0.001
0.040
-0.526
0.077
0.000
0.011
-0.066
0.054
0.000
0.005
-0.033
0.020
Defensive and
PCGB
BURSA
Enterprising
Table 2. Summary of Descriptive Statistics, BURSA
In the Malaysian Market (BURSA), Daily returns are found to be on average 0% for
almost all of the securities, reflecting a known property for stock market returns in high
48
frequency views. However, for HTBH (Hartalega Holdings), it seems that this security resulted
in a negative average of -0.1%., which suggests that this particular stock has seen a slightly
decreasing valuation over the long term. The common averages at around zero makes the
standard deviations comparable at face value in this view. The standard deviation provides a
measure of the volatility observed in the day-to-day returns of these listed securities. In the
Malaysian market, the stock found with the highest trading volatility is HTHB, with a standard
deviation of 0.040. HTHB returns at the minimum may lose -0.526 in one day and at maximum
will gain 0.077. This is considerably a wider variation than the standard deviation of 0.005
observed for the FTSE BURSA Malaysia index. The spread for the BURSA index ranges from
-0.033 to 0.02 only. All individual stocks in the Malaysian market exhibit volatilities larger than
that of the overall index. This is attributed to the fact that only a small number of securities were
able to pass the criteria which led to the portfolio lacking in diversification, which is important
because it reduces the variability that is present in the data. This lack of diversification is also
present for the other countries as the discussion moves further.
5.1.2 Indonesian Market (LQ45)
Type
of
Market
Asset
Criteria
Mean
SD
Min
Max
LQ45
GGRM
Defensive
0.000
0.021
-0.260
0.073
MNCN
Defensive
-0.001
0.030
-0.333
0.137
INTP
Defensive
0.000
0.024
-0.082
0.112
0.000
0.023
-0.077
0.096
Defensive
and
UNTR
Enterprising
49
BSDE
Enterprising
-0.001
0.021
-0.099
0.093
PTBA
Enterprising
0.001
0.028
-0.208
0.123
SSIA
Enterprising
0.000
0.023
-0.078
0.136
ITMG
Enterprising
0.000
0.027
-0.142
0.166
0.000
0.010
-0.055
0.036
LQ45
Table 3. Summary of Descriptive Statistics, LQ45
This same average is observed in the Indonesian market for MNCN (Media Nusantara
Citra) and BSDE (Bumi Serpong Damai). On the other hand, a positive average of 0.1%
observed for PTBA (PT Bukit Asam) suggests that this security has been seeing consistent
positive day-to-day returns in the period covered. Similar to the Malaysian Market, the market
index has the lowest volatility. The volatility for the LQ45 index is only 0.010, with a returns
spread of -0.055 to 0.036. The stocks with the closest level of volatility are GGRM (Gudang
Garam TBK PT) and BSDE, with standard deviations both at 0.021. However, this still doubles
the amount of volatility observed for the overall market index. Returns for GGRM range from
-0.260 to 0.073, while for BSDE the range is from -0.099 to 0.093.
50
5.1.3 Philippine Market (PSEi)
Type
Market
Asset
PSEI
of
Criteria
Mean
SD
Min
Max
0.000
0.018
-0.087
0.073
Defensive
and
AGI
Enterprising
Defensive
and
MEG
Enterprising
0.000
0.022
-0.114
0.077
PGOLD
Enterprising
0.000
0.016
-0.101
0.067
0.000
0.010
-0.046
0.035
PSEI
Table 4. Summary of Descriptive Statistics, PSEi
In the Philippine Market, across the board, daily returns resulted in 0% for all of the
securities. Volatility between some of the securities and market index does not seem to be that far
off. Again, the market index still has the lowest volatility with a standard deviation of 0.010, but
the stocks with the closest level of volatility are AGI (Alliance Global Group) and PGOLD
(Puregold Price Club), with standard deviations of 0.018 and 0.016, respectively. Meanwhile,
returns for AGI range from -0.087 to 0.073, while for PGOLD the range is from -0.101 to 0.067.
It's also worth noting that out of all the stocks, MEG (Megaworld) seems to have the lowest and
highest range, which is from -0.114 to 0.077. This is attributed to the stock having the highest
volatility, which means that it lacks diversification. Thus, minimizing movement of the returns,
meaning that the stock can have higher gains, but also has bigger losses. This is a common
pattern throughout the results showcased.
51
5.1.4 Thailand Market (SET50)
Type
Market
Asset
SET50
of
Criteria
Mean
SD
Min
Max
Defensive
and
AOT
Enterprising
0.001
0.013
-0.067
0.081
BH
Defensive
0.000
0.016
-0.094
0.082
LH
Defensive
0.000
0.014
-0.061
0.067
INTUCH
Enterprising
0.000
0.014
-0.082
0.091
TOA
Enterprising
0.000
0.015
-0.060
0.075
0.000
0.008
-0.037
0.039
SET50
Table 5. Summary of Descriptive Statistics, SET50
In the Thailand Market, same as PTBA (PT Bukit Asam) in the Indonesian Market, AOT
(Airports of Thailand) resulted in a positive average of 0.1%, which means that in the period
covered, the stock has been experiencing positive returns. Furthermore, the second closest level
of volatility towards the Thailand Market Index is also AOT with a standard deviation of 0.013.
Meanwhile, returns for this stock range from -0.067 to 0.081. However, INTUCH (Intouch
Holdings) has returns ranging from -0.082 to 0.091, with this stock having the highest possible
positive return among the selected securities.
52
5.1.5 Singaporean Market (STI)
Type
Market
Asset
of
Criteria
STI
Mean
SD
Min
Max
0.000
0.022
-0.250
0.131
0.000
0.014
-0.185
0.057
0.000
0.007
-0.045
0.026
Defensive
and
YAZG
Enterprising
Defensive
and
CTDM
Enterprising
STI
Table 6. Summary of Descriptive Statistics, STI
In the Singaporean Market, nothing seems to be out of normal and it follows the pattern
that has been illustrated throughout the rest of the markets. All securities seem to average 0%.
Moreover, CTDM (City Developments) has the second closest volatility level to the market
index, with a standard deviation of 0.014. However, this still doubles the amount of volatility
observed for the market index. Furthermore, YAZG (Yangzijiang Shipbuilding Holdings) has the
widest range of return, ranging from a minimum loss of -0.250 to a maximum gain 0.131.
5.2 Investor Returns
5.2.1 Weekly Rebalancing
Defensive
BURSA
Enterprising
Index
Mean
SD
Mean
SD
Mean
SD
-0.001
0.024
-0.001
0.029
0.000
0.012
53
LQ45
0.000
0.049
0.000
0.054
0.001
0.021
PSEI
-0.002
0.041
-0.002
0.040
0.001
0.020
SET50
-0.001
0.026
0.002
0.033
0.001
0.017
STI
-0.001
0.038
-0.002
0.040
0.000
0.016
Table 7. Summary of Investor Returns Under Weekly Rebalancing
The following set of results compares the returns earned by each sort of investor in each
market, as well as the market index's performance. This comparison is performed in the table
displaying the investor returns under weekly rebalancing, assuming that the investor does a
weekly rebalancing of their assets or when returns are realized each week. It should be
emphasized that average returns are poor in this high frequency setup. The average yield
predicted by the defensive investor in the BURSA market is -0.0001 or a loss of 0.1%, which is
the same as the average yield expected by the enterprising investor. In comparison, with a
0.000% projected yield, an investor in the index would have broken even. This observation
carries over in the PSE and the STI markets.
In contrast, breakeven results are anticipated by both defensive and enterprising investors
in the LQ45 market. Investing in the index, on the other hand, would have yielded an average of
0.0001, or a 0.1% increase. It is only in the SET market where either investors can expect to gain
over the market index. While the defensive investor gets an average yield of -0.001, the
enterprising investor sees a return of 0.002, compared to a gain of 0.001 predicted from an index
fund following the overall SET50 performance.
54
The standard deviation of returns for defensive portfolios under weekly rebalancing
ranges from 0.024 to 0.41 across the five markets. This is 0.029 to 0.054 for enterprising
investors. Both are much greater than the market indexes' ranges of 0.012 to 0.21.
5.2.2 Monthly Rebalancing
Defensive
Enterprising
Index
Mean
SD
Mean
SD
Mean
SD
BURSA
-0.005
0.042
-0.006
0.053
-0.001
0.024
LQ45
-0.004
0.108
-0.002
0.115
0.004
0.034
PSEI
-0.005
0.084
-0.006
0.081
0.003
0.036
SET50
-0.004
0.059
0.006
0.062
0.005
0.031
STI
-0.006
0.094
-0.009
0.100
-0.001
0.035
Table 8. Summary of Investor Returns Under Monthly Rebalancing
The outcomes of weekly rebalanced returns don't differ much from those of monthly
rebalancing. Table 3 provides a summary of the outcomes. Defensive investors are still
anticipated to lose an average of 0.004 to 0.005 of their fund value in the BURSA, PSEI, SET50,
and STI markets. Meanwhile, the enterprising investor expects a gain of 0.006 in the SET50
market, whereas a fund tracking the index would have gained somewhat behind at 0.005.
For defensive portfolios subject to monthly rebalancing, the standard deviation of returns
varies from 0.059 to 0.105 across ASEAN markets and from 0.062 to 0.115 for enterprising
investors. Both are substantially higher than the 0.024 to 0.036 ranges of the market indices.
55
5.2.3 Yearly Rebalancing
Defensive
Enterprising
Index
Mean
SD
Mean
SD
Mean
SD
BURSA
0.009
0.142
-0.070
0.180
-0.014
0.086
LQ45
-0.188
0.603
-0.091
0.611
0.038
0.140
PSEI
-0.048
0.263
-0.059
0.244
0.033
0.174
SET50
-0.036
0.096
0.097
0.074
0.029
0.118
STI
-0.027
0.237
-0.079
0.300
-0.003
0.118
Table 9. Summary of Investor Returns Under Yearly Rebalancing
Finally, for the investor returns under yearly rebalancing, the losses are likely to be
significantly bigger with annual rebalancing. The LQ45 index's defensive investor would have
lost an average of 18.8%, but the SET50 market's defensive investor would have lost 3.6%. In
comparison, a broad market indices investor would have earned 3.8% in the LQ45 market and
2.9% in the SET50 market. As with the previous results for weekly and monthly rebalancing, the
enterprising investor would have expected a gain that compares well to the performance of the
market index only in the SET50. The enterprising investor in the Thailand market would have
expected a gain of 9.7%, compared to only 2.9% for the market index.
The standard deviation of returns for defensive portfolios that are subject to monthly
rebalancing differs across the BURSA, LQ45, PSEI, SET50, and STI market from 0.096 to
0.603. For savvy investors, the standard deviation varies from 0.074 to 0.611. Both are much
higher than the market indexes' 0.086 to 0.174 ranges.
56
5.3 Bootstrapped Portfolio Performance
5.3.1 Malaysian Market (BURSA)
Stat
Investor
Mean
sd
lower_95
upper_95
p-value
Returns
Enterprising
-0.001
0.002
-0.005
0.002
0.000
Defensive
-0.001
0.002
-0.004
0.002
0.000
Market
0.000
0.001
-0.002
0.001
Enterprising
-0.047
0.073
-0.184
0.086
Defensive
-0.022
0.066
-0.135
0.116
Enterprising
1.141
0.154
0.872
1.448
Defensive
0.826
0.159
0.472
1.114
Enterprising
-0.001
0.002
-0.004
0.002
Defensive
-0.001
0.002
-0.006
0.002
Enterprising
-0.001
0.002
-0.005
0.003
Defensive
0.000
0.002
-0.004
0.003
Enterprising
0.050
0.005
0.040
0.060
0.000
Defensive
0.039
0.007
0.029
0.056
0.000
Market Index
0.019
0.002
0.016
0.022
Sharpe
Beta
Treynor
Alpha
Value at Risk
0.000
0.000
0.000
0.000
Table 10. Summary of Bootstrapped Portfolio Performance, BURSA
Table 10 details the performances of the Defensive and Enterprising portfolios in the
Malaysian market as represented by stocks from the BURSA as well as comparing these
portfolios to the performance of the market index as represented by the BURSA.
57
In terms of average returns it was observed that both the Enterprising and the Defensive
portfolios exhibited negative returns compared to the breakeven performance as expected from
the market. Both bootstrapped portfolios were found to be significant in testing, indicative that
the returns are significantly lower in performance compared to the market.
Looking at the Sharpe ratio, it is found to be negative for both the bootstrapped
Enterprising and
Defensive portfolios. However upon testing these figures were to be
significantly different against the broad market index. Their negative values are indicative of a
poor risk-adjusted performance against total volatility.
As for the beta of the bootstrapped Defensive and Enterprising portfolios the results saw
the Enterprising portfolio outperforming the Defensive portfolio at a 11.41% expected increase
in returns compared to the Defensive portfolio’s 8.26%% increase for every 10% increase in the
index. These results are indicative of their significantly different results from one another with
the Enterprising portfolio being significantly higher.
Observing the Sharpe ratio, however, returns continue to be negative on excess returns.
The Treynor ratio shows, nevertheless, that the higher excess returns for the enterprising investor
is proportional to a higher systematic risk that the investor must take on.
In line with the higher volatility presented in the previous set of results, the Value at Risk
of both bootstrapped portfolios are significantly different from that of the market. Compared to
both Enterprising and Defensive portfolios the market has a significantly lower loss, with the
maximum loss a portfolio may have at 5% probability being only 1.9%, while the Defensive
portfolio is expected to lose 3.9%, and having the potential highest loss, the Enterprising
portfolio is expected to lose 5.0%.
58
5.3.2 Indonesian Market (LQ45)
Stat
Investor
mean
sd
lower_95
upper_95
p-value
Returns
Enterprising
-0.001
0.004
-0.008
0.007
0.000
Defensive
-0.001
0.003
-0.007
0.006
0.003
Market
0.001
0.001
-0.002
0.004
Enterprising
-0.030
0.072
-0.158
0.123
Defensive
-0.039
0.071
-0.173
0.101
Enterprising
0.954
0.174
0.652
1.299
Defensive
1.135
0.144
0.887
1.378
Enterprising
-0.001
0.004
-0.007
0.007
Defensive
-0.001
0.003
-0.006
0.004
Enterprising
-0.003
0.004
-0.011
0.006
Defensive
-0.003
0.003
-0.009
0.004
Enterprising
0.090
0.008
0.075
0.106
0.000
Defensive
0.082
0.007
0.069
0.096
0.000
Market Index
0.034
0.003
0.027
0.040
Sharpe
Beta
Treynor
Alpha
Value at Risk
0.000
0.000
0.000
0.000
Table 11. Summary of Bootstrapped Portfolio Performance, LQ45
Table 11 details the performances of the Defensive and Enterprising portfolios in the
Indonesian market as represented by stocks from the LQ45 as well as comparing these portfolios
to the performance of the market index as represented by the LQ45.
59
In terms of the average returns of the Defensive and Enterprising portfolios, the
stationary bootstrap has shown that both the Defensive and Enterprising portfolios exhibited
negative results compared to the market. A corresponding bootstrap test found that both of the
Defensive and Enterprising portfolios were significantly different from the returns of the market.
This is indicative that the performance of the Defensive and Enterprising portfolios are
significantly lower compared to the returns of the market.
In terms of the Sharpe Ratio, the bootstrapped Defensive and Enterprising portfolios
showed negative results, however it was found that the Sharpe Ratio of the bootstrapped
portfolios were also found to be significantly different from one another. These results show that
even through a risk-adjusted performance against total volatility the portfolios perform poorly.
As for the beta of the bootstrapped Defensive and Enterprising portfolios the results saw
the Defensive portfolio outperforming the Enterprising portfolio at a 11.35% expected increase
in returns compared to the Enterprising portfolio’s 9.54% increase for every 10% increase in the
index. These results are indicative of their significantly different results from one another with
the Defensive portfolio being significantly higher.
Observing the Jensen’s Alpha of the bootstrapped Defensive and Enterprising portfolios
found the same negative values which is indicative of negative excess returns. Upon testing they
are found to be also significantly different from one another with the Enterprising Investor
having had a higher Jensen’s Alpha. Echoing the results of the Jensen’s Alpha’s for the
bootstrapped portfolios the Treynor Ratio are observed to be the same with the higher excess
returns of the Enterprising Investor portfolios representing a higher Treynor ratio as well.
60
Further bolstering the volatility present in the previous set of results, it was found that
upon observation of the Value at Risk the market had significantly lower losses compared to both
the bootstrapped Defensive and Enterprising portfolio. Indicative of the maximum loss a
portfolio may have at 5% probability, the market portfolio may only lose 3.4% while the
Defensive Investor may lose 8.2% with the Enterprising Investor having the potential highest
loss at 9%.
5.3.3 Philippine Market (PSEi)
Stat
Investor
mean
sd
lower_95
upper_95
p-value
Returns
Enterprising
-0.002
0.003
-0.007
0.004
0.000
Defensive
-0.001
0.003
-0.007
0.005
0.000
Market
0.001
0.001
-0.002
0.003
Enterprising
-0.074
0.076
-0.219
0.076
Defensive
-0.069
0.074
-0.195
0.082
Enterprising
1.227
0.159
0.950
1.565
Defensive
1.259
0.155
1.029
1.598
Enterprising
-0.002
0.002
-0.006
0.002
Defensive
-0.002
0.002
-0.005
0.002
Enterprising
-0.003
0.003
-0.008
0.001
Defensive
-0.003
0.003
-0.008
0.001
Enterprising
0.066
0.007
0.054
0.080
Sharpe
Beta
Treynor
Alpha
Value at Risk
0.000
0.000
0.000
0.000
0.000
61
Defensive
0.068
0.007
0.055
0.082
Market Index
0.031
0.002
0.027
0.036
0.000
Table 12. Summary of Bootstrapped Portfolio Performance, PSEi
Table 12 presents the performance of the Defensive and Enterprising portfolios in the
Philippine market as represented by stocks from the PSEi as well as comparing these portfolios
to the performance of the market index as represented by the PSEi.
Reflecting the previous countries, the stationary bootstrap has shown that both the
Defensive and Enterprising portfolios exhibited negative results compared to the market. Further
similarities arise in the corresponding bootstrap test that found once more that both of the
Defensive and Enterprising portfolios were significantly different from the returns of the market.
Indicating that in terms of average returns the performance of the bootstrapped portfolios had
been significantly lower compared to the returns of the market.
In terms of the Sharpe Ratio, the bootstrapped Defensive and Enterprising portfolios in
line with results from the previous countries had seen negative results, furthermore it was found
that the Sharpe Ratio of the bootstrapped portfolios were also found to be significantly different
from one another. These results show that even through a risk-adjusted performance against total
volatility both portfolios perform poorly however the Enterprising portfolio performs relatively
worse.
As for the beta of the bootstrapped Defensive and Enterprising portfolios the results saw
the Defensive portfolio outperforming the Enterprising portfolio at a 12.59% expected increase
in returns compared to the Enterprising portfolio’s 12.27% increase for every 10% gain in the
62
index. These results are indicative of their significantly different results from one another with
the Defensive portfolio being higher, reflecting once more the results from previous countries.
The Treynor ratios of the bootstrapped Defensive and Enterprising portfolios found the
same negative values which is indicative of negative excess returns. Upon testing they are found
to be also significantly different from one another with the Enterprising Investor having had a
higher Treynor’s ratio. Reflecting the results of the Treynor ratio for the bootstrapped portfolios
the Jensen’s Alpha are observed to be the same values as well as being significantly different,
however due to the relative virtue of the Treynor Ratio and the Jensen Alpha it is assumed that
the Enterprising Investor’s Jensen’s Alpha is higher.
Further highlighting the volatility present in the previous observations, it was found that
the Value at Risk of the market had significantly lower losses compared to both the bootstrapped
Defensive and Enterprising portfolio. Indicative of the maximum loss a portfolio may have at 5%
probability, the market portfolio will only lose 3.1% while the Defensive Investor, having the
potential highest loss, may lose 6.8% and the Enterprising Investor who may lose 6.6%.
5.3.4 Thailand Market (SET50)
Stat
Investor
mean
sd
lower_95
upper_95
p-value
Returns
Enterprising
0.002
0.002
-0.002
0.005
0.011
Defensive
-0.001
0.002
-0.004
0.003
0.000
Market
0.001
0.001
-0.001
0.004
Enterprising
0.014
0.067
-0.109
0.148
Sharpe
0.000
63
Beta
Treynor
Alpha
Value at Risk
Defensive
-0.100
0.070
-0.230
0.030
Enterprising
0.816
0.129
0.555
1.050
Defensive
0.846
0.083
0.662
1.004
Enterprising
0.001
0.003
-0.005
0.007
Defensive
-0.003
0.002
-0.006
0.001
Enterprising
-0.001
0.002
-0.005
0.004
Defensive
-0.003
0.002
-0.007
0.001
Enterprising
0.053
0.003
0.046
0.059
0.000
Defensive
0.044
0.004
0.038
0.052
0.000
Market Index
0.027
0.002
0.023
0.031
0.000
0.000
0.000
Table 13. Summary of Bootstrapped Portfolio Performance, SET50
Table 13 presents the performance of the Defensive and Enterprising portfolios in the
Thailand market as represented by stocks from the SET50 as well as comparing these portfolios
to the performance of the market index as represented by the SET50.
Differing slightly from the previous countries, the stationary bootstrap has shown
different results for the Defensive and Enterprising portfolios. The Defensive portfolio exhibited
negative results compared to the market while the Enterprising Portfolio saw a small .01%
increase when compared to the market. Similarities to the previous arise once more in the
corresponding bootstrap test that found that both of the Defensive and Enterprising portfolios
were significantly different from the returns of the market. Indicating that in terms of average
64
returns the performance of the bootstrapped Defensive portfolio had been significantly lower
compared to the returns of the market while the Enterprising portfolio had been higher.
In terms of the Sharpe Ratio, the bootstrapped Defensive and Enterprising portfolios
differ once more with results from the previous countries while reflecting the previous results
with the bootstrapped Enterprising portfolio had garnered a small positive value while the
bootstrapped Defensive portfolio had gathered a negative value. Furthermore, it was found that
the Sharpe Ratio of the bootstrapped portfolios were significantly different from one another.
These results show that even through a risk-adjusted performance against total volatility both
portfolios perform relatively poorly however the Defensive portfolio performs worse.
As for the beta of the bootstrapped Defensive and Enterprising portfolios, the results saw
the Defensive portfolio outperforming the Enterprising portfolio at a 8.46% expected increase in
returns compared to the Enterprising portfolio’s 8.16% increase for every 10% gain in the index.
These results are indicative of their significantly different results from one another with the
Defensive portfolio being higher, reflecting once more the results from previous countries.
Looking at the Jensen’s Alpha of the bootstrapped Defensive and Enterprising portfolios,
it was observed that both portfolios present negative values which is indicative of negative
excess returns. Upon testing, they are found to be also significantly different from one another
with the Enterprising Investor having had a higher Jensen’s Alpha. Echoing the results of the
Jensen’s Alpha’s for the bootstrapped portfolios, the Treynor Ratio are observed to be the same
with the higher excess returns of the Enterprising Investor portfolios which represent a higher
Treynor ratio as well in line with previous results from the previous countries.
65
Measuring the Value at Risk for the bootstrapped Defensive and Enterprising portfolios,
the volatilities presented in the previous set of metrics are further highlighted. In testing, it was
found that the Value at Risk of the market had significantly lower losses compared to both the
bootstrapped Defensive and Enterprising portfolio. Indicative of the maximum loss a portfolio
may have at 5% probability. If an investor were to invest in the market index, the worst losses to
be incurred will only be limited to 2.7% while the Defensive Investor may lose 4.4% with the
Enterprising Investor having the potential highest loss at 5.3%.
5.3.5 Singaporean Market (STI)
Stat
Investor
mean
sd
lower_95
upper_95
p-value
Returns
Enterprising
-0.002
0.002
-0.006
0.002
0.000
Defensive
-0.002
0.002
-0.005
0.002
0.000
Market
0.000
0.001
-0.002
0.001
Enterprising
-0.055
0.049
-0.145
0.035
Defensive
-0.043
0.051
-0.126
0.055
Enterprising
1.176
0.125
0.927
1.415
Defensive
1.181
0.092
1.005
1.376
Enterprising
-0.002
0.001
-0.004
0.001
Defensive
-0.001
0.001
-0.004
0.001
Sharpe
Beta
Treynor
0.000
0.000
0.000
66
Alpha
Value at Risk
Enterprising
-0.002
0.002
-0.006
0.002
0.000
Defensive
-0.001
0.002
-0.005
0.002
Enterprising
0.069
0.007
0.057
0.084
0.000
Defensive
0.064
0.008
0.053
0.079
0.000
Market Index
0.027
0.002
0.024
0.030
Table 14. Summary of Bootstrapped Portfolio Performance, STI
Table X details the performances of the Defensive and Enterprising portfolios in the
Singaporean market as represented by stocks from the STI as well as comparing these portfolios
to the performance of the market index as represented by the STI.
Looking at the average returns of the Defensive and Enterprising portfolios, the
stationary bootstrap has shown that market had outperformed both the Defensive and
Enterprising portfolios seeing as they lose value compared to the breakeven of the market.
Reflecting this, a corresponding bootstrap test found that both of the Defensive and Enterprising
portfolios were significantly different from the returns of the market. This is indicative that the
performance of the Defensive and Enterprising portfolios are significantly lower compared to the
returns of the market.
In terms of the Sharpe Ratio, the bootstrapped Defensive and Enterprising portfolios
showed negative results, however it was found that the Sharpe Ratio of the bootstrapped
portfolios were also found to be significantly different from one another as is with the other
countries. These results show that even through a risk-adjusted performance against total
volatility the portfolios perform poorly with the Enterprising portfolio performing worse.
67
As for the beta of the bootstrapped Defensive and Enterprising portfolios, the results saw
the Defensive portfolio outperforming the Enterprising portfolio at a 11.81% expected increase
in returns compared to the Enterprising portfolio’s 11.76% increase with a 10% gain in the index.
These results are indicative of their significantly different results from one another with the
Defensive portfolio being significantly higher in line with results from previous countries.
The bootstrapped Defensive and Enterprising portfolios’ Jensen’s Alphas were found to
be negative values indicating negative excess returns. In testing they are found to be also
significantly different from one another, in line with earlier results in other countries, with the
Enterprising Investor having had a higher Jensen’s Alpha. Echoing the results of the Jensen’s
Alpha’s for the bootstrapped portfolios the Treynor Ratio are observed to also be negative with
the higher excess returns of the Enterprising Investor portfolios representing a higher Treynor
ratio as well.
Observing the volatility present in the previous set of results, it was found that the Value
at Risk of the market had significantly lower losses compared to both the bootstrapped Defensive
and Enterprising portfolio. Indicative of the maximum loss a portfolio may have at 5%
probability, the market portfolio may only lose 2.7% while the Defensive Investor may lose 6.4%
with the Enterprising Investor having the potential highest loss at 6.9%.
68
Chapter VI - Conclusion and Recommendation
6.1 Conclusion
Graham's contributions to the world of finance are immeasurable, however his teachings
while prevalent in most Western countries and creating some very notable successes when tested
in the Southeast Asian market yielded uneven and poor results. The portfolios resulting in the use
of either set of criteria presented high risk and relatively low rewards across the ASEAN-5
markets.
In terms of performance the risk of either Defensive or Enterprising portfolio varied from
country to country, the Sharpe ratio of the Defensive Investor Portfolios from BURSA, STI and
PSEi were able to significantly outperform the Enterprising investor. This is indicative of the
portfolios having less risk compared to the Enterprising portfolios from these countries. As for
the Enterprising portfolios of the LQ45 and the SET50, their Sharpe ratios were able to
significantly outperform those of the Defensive Investor Portfolios, signaling less risk compared
to the Defensive Investor Portfolios from their respective countries.
Another measure of risk, the Jensen's Alpha of the Defensive Investor Portfolios from
BURSA, LQ45, and
STI were able to significantly outperform the Enterprising Investor
meaning they were able to yield excess returns beating the benchmark of the Enterprising
Investor. As for the Enterprising Investor, the portfolios from the PSEi and SET50 were able to
outperform the Defensive Investor in terms of Jensen's Alpha indicative of their excess returns
beating the Defensive Investor Portfolio from their respective countries.
For the Treynor Ratio of the Defensive Investor Portfolio, LQ45 and PSEI were able to
significantly outperform the Enterprising Investor. This means that they were able to yield excess
69
returns higher than the Enterprising Investor Portfolio. Regarding the Enterprising Investor, the
BURSA, SET50 and STI were able to outperform the Defensive Investor Portfolio which
indicates that they have earned more excess returns than the Defensive Investor Portfolio of the
Malaysian, Thailand and Singaporean Market.
The beta of the Defensive Investor Portfolios from LQ45, PSEi, STI and SET were able
to significantly outperform the Enterprising Investor Portfolios meaning that the returns of these
portfolios produce better returns with regards to movements in the market. As for the
Enterprising Investor Portfolios, only the portfolio from BURSA was able to significantly
outperform the Defensive Investor. This serves to signal that the Defensive portfolio is the better
investment compared to the Enterprising portfolio with regards to returns in these respective
countries. However, the investor must consider the prior risks attached to each kind of Graham
portfolio.
While the performances of the Enterprising and Defensive portfolios remain uneven in
terms of risk and reward as well as in comparisons to the performances of one another.
Compared to the market, it is apparent that both types of portfolios leave a lot to be desired. With
all Graham portfolios, across all the ASEAN-5 markets unable to significantly outperform both
the Value at Risk and the Expected Returns of the market index. With the exception of a .01%
gain for the expected returns of the Thailand Market's Enterprising Investor. This reflects that if
an investor were to use either criteria and build their portfolios on stocks that passed either
criteria the investor would not be able to breakeven their investment compared to the index and
their portfolio would incur losses greater than the market in the worst case.
70
Sharpe
Ratio
Treynor
Ratio
Jensen's
Alpha
Beta
Expected
Returns
VAR
BURSA
DEF
ENT
DEF
ENT
MARKET
MARKET
LQ45
ENT
DEF
DEF
DEF
MARKET
MARKET
PSEI
DEF
DEF
ENT
DEF
MARKET
MARKET
SET50
ENT
ENT
ENT
DEF
ENT
MARKET
STI
DEF
ENT
DEF
DEF
MARKET
MARKET
Table 15. Summary of Bootstrapped Portfolio Performance
The Defensive Investor Portfolio in the Malaysian market’s Treynor Ratio and Beta were
unable to outperform the Enterprising Investor Portfolio thus the researchers accept the null
hypotheses for these metrics. Compared to the market, the Value at Risk and the expected returns
of the Defensive Investor Portfolio in the Malaysian market did not significantly outperform the
market thus the researchers accept the null hypothesis for these metrics. As for the Sharpe Ratio
and the Jensen's Alpha of the Defensive Investor Portfolio in the Malaysian market, they
significantly outperformed that of the Enterprising Investor Portfolio in the Malaysian market,
the researchers reject the null hypotheses for these metrics.
The Enterprising Investor Portfolio in the Malaysian market’s Sharpe ratio and the Jensen
Alpha did not significantly outperform the Defensive Investor Portfolio in the Malaysian market
and thus their respective null hypotheses are accepted. The Value at Risk and the expected
returns of the Enterprising Investor Portfolio in the Malaysian market did not significantly
outperform the market thus the researchers accept the respective null hypothesis. The Treynor
Ratio and the beta
of the Enterprising Investor Portfolio significantly outperformed the
Defensive Investor Portfolio in the Malaysian market, rejecting their respective null hypotheses.
71
The Defensive Investor Portfolio in the Indonesian market’s Sharpe Ratio had been
unable to significantly outperform the Sharpe ratio of the Enterprising Investor Portfolio in the
Indonesian market thus the researchers accept the null hypothesis for this. The Treynor ratio, the
Jensen Alpha and the beta of the Defensive Investor Portfolio in the Indonesian market, however,
were able to significantly outperform the respective metrics of the Enterprising Investor Portfolio
in the Indonesian market, rejecting their respective null hypotheses. The Value at Risk and the
Expected Returns of the Defensive portfolio in the Indonesian market were unable to outperform
that of the Indonesian market portfolio thus the respective null hypotheses for the Defensive
portfolio in the Indonesian market are accepted.
The Enterprising Investor Portfolio in the Indonesian market’s Sharpe ratio had been
able to significantly outperform the Defensive Investor portfolio in the Indonesian market's
Sharpe ratio thus its null hypothesis is rejected. The Treynor ratio, Jensen’s Alpha and beta of the
Enterprising Investor Portfolio in the Indonesian market did not significantly outperform the
respective metrics of the Defensive Investor portfolio in the Indonesian market, thus their
respective null hypotheses are accepted. The Value at Risk and the Expected Returns of the
Enterprising Investor Portfolio in the Indonesian market were unable to significantly outperform
that of the Indonesian market’s Value at Risk and Expected Returns thus the respective null
hypotheses for the Enterprising portfolio in the Indonesian market are accepted
The Defensive Investor portfolio in the Philippine market, the Sharpe ratio, Treynor ratio
and the beta were able to significantly outperform the Enterprising Investor portfolio thus the
respective null hypotheses are rejected. The Defensive portfolio in the Philippine market is
unable to significantly outperform the Value at Risk and the expected returns of the market thus
the respective null hypotheses are accepted. The Defensive portfolio in the Philippine market
72
was also unable to outperform the Jensen Alpha of the Enterprising portfolio thus the respective
null hypothesis is accepted.
The Enterprising portfolio in the Philippine market's Sharpe ratio, Treynor ratio, beta and
Jensen’s alpha were unable to significantly outperform the Defensive portfolio thus the
respective null hypotheses will be accepted. The Value at Risk and expected returns of the
Enterprising portfolio had also been unable to significantly outperform the market thus the
respective null hypotheses are also accepted.
The Defensive Investor Portfolio in the Thailand Market's Sharpe Ratio, Treynor Ratio
and Jensen’s Alpha did not significantly outperform the Enterprising Investor Portfolio therefore
the researchers accept the null hypotheses for the respective metrics. The Defensive Investor
Portfolio in the Thailand market's beta, was able to significantly outperform the beta of the
Enterprising Investor Portfolio in the Thailand market therefore the respective null hypothesis is
rejected. The Value at risk and expected returns of the Defensive Investor Portfolios in the
Thailand market were unable to significantly outperform the market portfolios' Value at risk and
expected returns therefore the respective null hypotheses are accepted.
The Enterprising Investor Profile in the Thailand Market's Beta was unable to
significantly outperform the Defensive Investor Portfolio in the Thailand Market's Beta therefore
the respective null hypothesis is accepted. As for the Sharpe Ratio, Treynor Ratio and Jensen’s
Alpha of the Enterprising Investor Portfolio in the Thailand market were able to significantly
outperform the Defensive Investor Portfolio in the Thailand market's Sharpe ratio, Treynor ratio
and Jensen's Alpha therefore the respective null hypotheses are rejected. The Value at risk of the
Enterprising Investor Portfolio in the Thailand market was unable to outperform the market
73
portfolio's Value at risk thus the respective null hypothesis is accepted. The expected returns of
the Enterprising Investor Portfolio in the Thailand market was able to outperform the market
portfolio's expected returns thus the respective null hypothesis is rejected.
The Defensive Investor Portfolio of the Singaporean Market's Beta, Sharpe Ratio,
Treynor Ratio and Jensen’s Alpha were able to significantly outperformed the Enterprising
Investor Portfolio in the Singaporean Market's Beta, Sharpe Ratio, Treynor Ratio and Jensen’s
Alpha therefore the respective null hypotheses are rejected. The Value at risk and expected return
of the Defensive Investor Portfolio in the Singaporean Market were unable to significantly
outperform the market portfolios' Value at risk and expected returns therefore the respective null
hypotheses are accepted.
The Enterprising Investor Portfolio for the Singaporean Market's Beta, Sharpe Ratio,
Treynor Ratio and Jensen’s Alpha, were unable to significantly outperform the Defensive
Investor Portfolio of the Singaporean Market's Beta, Sharpe Ratio, Treynor Ratio and Jensen’s
Alpha therefore the respective null hypotheses are accepted. The Value at risk and expected
returns of the Enterprising Investor Portfolio for the Singaporean Market were unable to
significantly outperform the market portfolio's Value at risk and expected returns therefore the
respective null hypotheses are accepted.
These results come about through the nature of the amount of stocks in the Graham
portfolios. Each country's Graham portfolios were comprised of, at maximum, two securities in
each period observed. This lack of diversification in the Graham portfolios can only result in
above average risk. Investing in the market index which is highly diversified allows for the
minimization of losses thus it would be the stronger quantitative investment strategy.
74
Furthermore, this lack of diversification is a result of Graham’s criteria being too
restrictive for the ASEAN-5 markets. This may be due to the age of the criteria, being almost
three decades old and that they were originally done using the US markets as represented by the
S&P 500 Index.
As literature from Terzi (2016), Rachmatullah and Faturohman (2016), Agarwal and
Agarwal (2020), Palazzo et, al. (2018) and Zacharia and Hashim (2017) there is precedence of
Graham’s criterias greatly benefiting investors in providing gains in smaller markets.
These literatures however altered Graham’s stock selection criteria to suit their market as
Terzi (2016) did or use other areas of his work as Rachmatullah and Faturohman (2016) did or
pick specific criteria to use as Agarwal and Agarwal (2020) did. The researchers in their pursuit
to keep Graham’s criteria in the Intelligent Investor mostly intact had reflected on the securities
chosen and the performance of the portfolios in the ASEAN-5 making it a weak quantitative
investment strategy.
6.2 Recommendation
6.2.1 For Individual Stock Investors in the ASEAN
For investors in the ASEAN, it is not recommended to use Benjamin Graham’s Stock
Selection Criteria of Defensive and Enterprising based on the result presented in this paper. The
results of the paper showed that using Benjamin Graham’s Criteria is very risky due to some
weaknesses. The researchers find that the weakness of the model used in this study is the
limitation of its data, wherein only companies from the composite index were selected for the
screening, along with the stringent nature of the criteria, this combination immensely diminishes
the number of possible securities to be entered into a portfolio. Thus, leading to portfolios that
75
lack diversification and creating a risky investment strategy. Knowing about the implications of
using this strategy will help investors in their decision making in investing in stocks in the
ASEAN Market. The researchers recommend for investors to not solely rely on this study and
branch out and look for other studies regarding Benjamin Graham that might have different
results. Also, it is recommended that investors look into other quantitative investment strategies
that might aid in their goal of maximizing their returns.
6.2.2 For Investing Firms
For investing firms, it is recommended that they do not use Benjamin Graham’s Stock
Selection Criteria as Quantitative Investment Strategy in screening for securities. As mentioned
previously, similar for the investors, the weakness of the model is the stringent nature of the
criteria and the fact that only companies from the composite index were screened for this study.
Despite other studies showing the positive nature of Benjamin Graham’s Teaching, this study
contradicts the majority of results. Thus, firms should use their own judgment and use the result
of this research as supplementary information to their decision making in regard to their
investments. Investing Firms might focus on other strategies other than value investing, such as
buy and hold and growth investing to mention a few.
6.2.3 For Future Researchers
The results show that Benjamin Graham’s Stock Selection Criteria, whether it is
Defensive or Enterprising, does not provide a strong quantitative investment strategy in the
ASEAN countries specified in this study. This study may serve as a reference for future
researchers that aim to further dive deep into the field of value investing in regard to Benjamin
Graham’s teachings. Also, the researchers recommend for future researchers who plan on
76
conducting a similar study to expand the population of their data. Rather than focusing solely on
the companies listed in the composite index of each country, future researchers could look into
the entire stock market and widen their pool of securities, so as to avoid the problem of creating
portfolios that lack diversification. The researchers also recommend for future researchers to
focus on the pandemic period since the period in this study is only limited to the pre-pandemic
period. Through focusing on the pandemic period, a time where market conditions were
different, new results could be obtained. Furthermore, future researchers could also look into
other strategies such as growth investing or buy and hold investing to mention a few, and use
bootstrapping as their methodology, so as to create more comprehensive results from a new
methodology other than that of backtesting.
77
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81
Appendix
Appendix A : Codes Used
library(readxl)
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
##
date, intersect, setdiff, union
library(gridExtra)
library(ggplotify)
# --- Read Data
bursa.port.ent <"ENT-PORT")
lq45.port.ent <"ENT-PORT")
psei.port.ent <"ENT-PORT")
set50.port.ent <"ENT-PORT")
sti.port.ent
<"ENT-PORT")
read_excel("Data/Reformatted/BURSA.xlsx", sheet =
read_excel("Data/Reformatted/LQ45.xlsx",
sheet =
read_excel("Data/Reformatted/PSEI.xlsx",
sheet =
read_excel("Data/Reformatted/SET50.xlsx", sheet =
read_excel("Data/Reformatted/STI.xlsx",
sheet =
bursa.port.def
"DEF-PORT")
lq45.port.def
"DEF-PORT")
psei.port.def
"DEF-PORT")
set50.port.def
"DEF-PORT")
sti.port.def
"DEF-PORT")
<- read_excel("Data/Reformatted/BURSA.xlsx", sheet =
<- read_excel("Data/Reformatted/STI.xlsx",
sheet =
bursa.price
lq45.price
psei.price
set50.price
sti.price
read_excel("Data/Reformatted/BURSA.xlsx",
read_excel("Data/Reformatted/LQ45.xlsx",
read_excel("Data/Reformatted/PSEI.xlsx",
read_excel("Data/Reformatted/SET50.xlsx",
read_excel("Data/Reformatted/STI.xlsx",
=
=
=
=
=
<<<<<-
<- read_excel("Data/Reformatted/LQ45.xlsx",
sheet =
<- read_excel("Data/Reformatted/PSEI.xlsx",
sheet =
<- read_excel("Data/Reformatted/SET50.xlsx", sheet =
sheet
sheet
sheet
sheet
sheet
"PRICE")
"PRICE")
"PRICE")
"PRICE")
"PRICE")
bursa.price$year <- year(bursa.price$date)
lq45.price$year <- year(lq45.price$date)
psei.price$year <- year(psei.price$date)
82
set50.price$year <- year(set50.price$date)
sti.price$year
<- year(sti.price$date)
bursa.price$week
lq45.price$week
psei.price$week
set50.price$week
sti.price$week
<<<<<-
bursa.price$month
lq45.price$month
psei.price$month
set50.price$month
sti.price$month
week(bursa.price$date)
week(lq45.price$date)
week(psei.price$date)
week(set50.price$date)
week(sti.price$date)
<<<<<-
month(bursa.price$date)
month(lq45.price$date)
month(psei.price$date)
month(set50.price$date)
month(sti.price$date)
bursa.price <- subset(bursa.price, date <= as.Date("2019-12-31"))
lq45.price <- subset(lq45.price, date <= as.Date("2019-12-31"))
psei.price <- subset(psei.price, date <= as.Date("2019-12-31"))
set50.price <- subset(set50.price, date <= as.Date("2019-12-31"))
sti.price <- subset(sti.price, date <= as.Date("2019-12-31"))
# --- Descriptive
bursa.desc <- data.frame(
mean = apply(get_return2(bursa.price[,2:8]), 2, function(x) mean(x, na.rm =
TRUE)),
sd = apply(get_return2(bursa.price[,2:8]), 2, function(x) sd(x, na.rm =
TRUE)),
min = apply(get_return2(bursa.price[,2:8]), 2, function(x) min(x, na.rm =
TRUE)),
max = apply(get_return2(bursa.price[,2:8]), 2, function(x) max(x, na.rm =
TRUE))
)
bursa.desc
##
##
##
##
##
##
##
##
mean
GENT -2.961110e-04
PGAS -3.578621e-04
PEPT
1.427513e-04
HLCB
1.404098e-04
HTHB -5.923864e-04
PCGB -2.051345e-05
BURSA -5.476031e-05
sd
0.014918724
0.012703656
0.010176865
0.011609438
0.039556177
0.011137026
0.005275799
min
-0.08090615
-0.05992949
-0.20000000
-0.05444444
-1.10869565
-0.06590258
-0.03289749
max
0.05757576
0.06695700
0.05310621
0.05492228
0.07714286
0.05361930
0.01994844
lq45.desc <- data.frame(
mean = apply(get_return2(lq45.price[,2:10]), 2, function(x) mean(x, na.rm =
TRUE)),
sd = apply(get_return2(lq45.price[,2:10]), 2, function(x) sd(x, na.rm =
TRUE)),
min = apply(get_return2(lq45.price[,2:10]), 2, function(x) min(x, na.rm =
83
TRUE)),
max = apply(get_return2(lq45.price[,2:10]), 2, function(x) max(x, na.rm =
TRUE))
)
lq45.desc
##
##
##
##
##
##
##
##
##
##
GGRM
MNCN
INTP
UNTR
BSDE
PTBA
SSIA
ITMG
LQ45
mean
-2.256697e-04
-5.316221e-04
-4.068424e-04
4.179825e-05
-5.946539e-04
7.662161e-04
-3.385944e-04
3.396495e-04
2.141154e-04
sd
0.02081205
0.02981965
0.02400260
0.02270006
0.02135311
0.02806257
0.02338183
0.02743627
0.01044973
min
-0.26007326
-0.33333333
-0.08187135
-0.07668232
-0.09947644
-0.20843672
-0.07762557
-0.14234450
-0.05454794
max
0.07282230
0.13716814
0.11212815
0.09606987
0.09253731
0.12300469
0.13636364
0.16602317
0.03591405
psei.desc <- data.frame(
mean = apply(get_return2(psei.price[,2:5]), 2, function(x) mean(x, na.rm =
TRUE)),
sd = apply(get_return2(psei.price[,2:5]), 2, function(x) sd(x, na.rm =
TRUE)),
min = apply(get_return2(psei.price[,2:5]), 2, function(x) min(x, na.rm =
TRUE)),
max = apply(get_return2(psei.price[,2:5]), 2, function(x) max(x, na.rm =
TRUE))
)
psei.desc
##
##
##
##
##
mean
sd
min
max
AGI
-4.828305e-04 0.01787694 -0.08661417 0.07269504
MEG
-2.646558e-04 0.02171346 -0.11356467 0.07731959
PGOLD 3.431758e-05 0.01569005 -0.10063559 0.06672845
PSEI
8.682059e-05 0.01009090 -0.04566764 0.03513012
set50.desc <- data.frame(
mean = apply(get_return2(set50.price[,2:7]), 2, function(x) mean(x, na.rm =
TRUE)),
sd = apply(get_return2(set50.price[,2:7]), 2, function(x) sd(x, na.rm =
TRUE)),
min = apply(get_return2(set50.price[,2:7]), 2, function(x) min(x, na.rm =
TRUE)),
max = apply(get_return2(set50.price[,2:7]), 2, function(x) max(x, na.rm =
TRUE))
)
set50.desc
84
##
##
##
##
##
##
##
mean
sd
min
max
AOT
7.048885e-04 0.01309824 -0.06741573 0.08121827
BH
-4.678873e-04 0.01564768 -0.09375000 0.08196721
LH
-1.809945e-05 0.01371603 -0.06086957 0.06722689
INTUCH 5.202608e-05 0.01392371 -0.08196721 0.09134615
TOA
7.879620e-05 0.01459542 -0.05952381 0.07518797
SET50
2.716526e-04 0.00791746 -0.03711191 0.03928590
sti.desc <- data.frame(
mean = apply(get_return2(sti.price[,2:4]), 2, function(x) mean(x, na.rm =
TRUE)),
sd = apply(get_return2(sti.price[,2:4]), 2, function(x) sd(x, na.rm =
TRUE)),
min = apply(get_return2(sti.price[,2:4]), 2, function(x) min(x, na.rm =
TRUE)),
max = apply(get_return2(sti.price[,2:4]), 2, function(x) max(x, na.rm =
TRUE))
)
sti.desc
##
mean
sd
min
max
## YAZG -1.607541e-04 0.021717323 -0.25000000 0.13131313
## CTDM -1.921179e-04 0.013825721 -0.18498943 0.05729167
## STI -2.153648e-05 0.007118409 -0.04488304 0.02621081
# --- Generate Portfolio
bursa.ret.ent <- get_portfolios(market = "bursa", investor = "ent")
bursa.ret.def <- get_portfolios(market = "bursa", investor = "def")
lq45.ret.ent <- get_portfolios(market = "lq45", investor = "ent")
## Warning in max.default(structure(numeric(0), class = c("POSIXct",
"POSIXt": no
## non-missing arguments to max; returning -Inf
## Warning in max.default(structure(numeric(0), class = c("POSIXct",
"POSIXt": no
## non-missing arguments to max; returning -Inf
lq45.ret.def <- get_portfolios(market = "lq45", investor = "def")
## Warning in max.default(structure(numeric(0), class = c("POSIXct",
"POSIXt": no
## non-missing arguments to max; returning -Inf
## Warning in max.default(structure(numeric(0), class = c("POSIXct",
"POSIXt": no
## non-missing arguments to max; returning -Inf
85
psei.ret.ent <- get_portfolios(market = "psei", investor = "ent")
psei.ret.def <- get_portfolios(market = "psei", investor = "def")
set50.ret.ent <- get_portfolios(market = "set50", investor = "ent")
set50.ret.def <- get_portfolios(market = "set50", investor = "def")
sti.ret.ent <- get_portfolios(market = "sti", investor = "ent")
sti.ret.def <- get_portfolios(market = "sti", investor = "def")
# --- Plot of Returns
grid.arrange(
as.grob(function(){
plot(ret_port ~ date, data = bursa.ret.ent$weekly, type = "l", col =
"dodgerblue4", xlab = "BURSA", ylab = "Value",
ylim = c(-0.2,0.2))
lines(ret_port ~ date, data = bursa.ret.def$weekly, col = "deepskyblue3")
par(new = TRUE)
plot(ret_market ~ date, data = bursa.ret.def$weekly, xlab="", ylab="",
axes=FALSE, type="l", col="darkorange4")
legend("topleft",legend=c("Enterprising","Defensive","BURSA"), cex = 0.8,
lty=1,
text.col=c("dodgerblue4","deepskyblue3","darkorange4"),col=c("dodgerblue4","d
eepskyblue3","darkorange4"))
}),
as.grob(function(){
plot(ret_port ~ date, data = lq45.ret.ent$weekly, type = "l", col =
"dodgerblue4", xlab = "LQ45", ylab = "Value",
ylim = c(-0.2,0.2))
lines(ret_port ~ date, data = lq45.ret.def$weekly, col = "deepskyblue3")
par(new = TRUE)
plot(ret_market ~ date, data = lq45.ret.def$weekly, xlab="", ylab="",
axes=FALSE, type="l", col="darkorange4")
legend("topleft",legend=c("Enterprising","Defensive","LQ45"), cex = 0.8,
lty=1,
text.col=c("dodgerblue4","deepskyblue3","darkorange4"),col=c("dodgerblue4","d
eepskyblue3","darkorange4"))
}),
as.grob(function(){
plot(ret_port ~ date, data = psei.ret.ent$weekly, type = "l", col =
"dodgerblue4", xlab = "PSEI", ylab = "Value",
ylim = c(-0.2,0.2))
lines(ret_port ~ date, data = psei.ret.def$weekly, col = "deepskyblue3")
par(new = TRUE)
plot(ret_market ~ date, data = psei.ret.def$weekly, xlab="", ylab="",
axes=FALSE, type="l", col="darkorange4")
legend("topleft",legend=c("Enterprising","Defensive","PSEI"), cex = 0.8,
lty=1,
86
text.col=c("dodgerblue4","deepskyblue3","darkorange4"),col=c("dodgerblue4","d
eepskyblue3","darkorange4"))
}),
as.grob(function(){
plot(ret_port ~ date, data = set50.ret.ent$weekly, type = "l", col =
"dodgerblue4", xlab = "SET50", ylab = "Value",
ylim = c(-0.2,0.2))
lines(ret_port ~ date, data = set50.ret.def$weekly, col = "deepskyblue3")
par(new = TRUE)
plot(ret_market ~ date, data = set50.ret.def$weekly, xlab="", ylab="",
axes=FALSE, type="l", col="darkorange4")
legend("topleft",legend=c("Enterprising","Defensive","SET50"), cex = 0.8,
lty=1,
text.col=c("dodgerblue4","deepskyblue3","darkorange4"),col=c("dodgerblue4","d
eepskyblue3","darkorange4"))
}),
as.grob(function(){
plot(ret_port ~ date, data = sti.ret.ent$weekly, type = "l", col =
"dodgerblue4", xlab = "STI", ylab = "Value",
ylim = c(-0.3,0.3))
lines(ret_port ~ date, data = sti.ret.def$weekly, col = "deepskyblue3")
par(new = TRUE)
plot(ret_market ~ date, data = sti.ret.def$weekly, xlab="", ylab="",
axes=FALSE, type="l", col="darkorange4")
legend("topleft",legend=c("Enterprising","Defensive","STI"), cex = 0.8,
lty=1,
text.col=c("dodgerblue4","deepskyblue3","darkorange4"),col=c("dodgerblue4","d
eepskyblue3","darkorange4"))
}),
nrow = 3
)
87
# --- Descriptve Statistics
desc.weekly <- data.frame(
market = c("BURSA","LQ45","PSEI","SET50","STI"),
mean.def = c(
mean(bursa.ret.def$weekly$ret_port, na.rm = TRUE),
mean(lq45.ret.def$weekly$ret_port, na.rm = TRUE),
mean(psei.ret.def$weekly$ret_port, na.rm = TRUE),
mean(set50.ret.def$weekly$ret_port, na.rm = TRUE),
mean(sti.ret.def$weekly$ret_port, na.rm = TRUE)
),
sd.def = c(
sd(bursa.ret.def$weekly$ret_port, na.rm = TRUE),
sd(lq45.ret.def$weekly$ret_port, na.rm = TRUE),
sd(psei.ret.def$weekly$ret_port, na.rm = TRUE),
sd(set50.ret.def$weekly$ret_port, na.rm = TRUE),
sd(sti.ret.def$weekly$ret_port, na.rm = TRUE)
),
mean.ent = c(
mean(bursa.ret.ent$weekly$ret_port, na.rm = TRUE),
mean(lq45.ret.ent$weekly$ret_port, na.rm = TRUE),
mean(psei.ret.ent$weekly$ret_port, na.rm = TRUE),
mean(set50.ret.ent$weekly$ret_port, na.rm = TRUE),
mean(sti.ret.ent$weekly$ret_port, na.rm = TRUE)
),
sd.ent = c(
sd(bursa.ret.ent$weekly$ret_port, na.rm = TRUE),
sd(lq45.ret.ent$weekly$ret_port, na.rm = TRUE),
sd(psei.ret.ent$weekly$ret_port, na.rm = TRUE),
sd(set50.ret.ent$weekly$ret_port, na.rm = TRUE),
sd(sti.ret.ent$weekly$ret_port, na.rm = TRUE)
),
mean.market = c(
mean(bursa.ret.def$weekly$ret_market, na.rm = TRUE),
mean(lq45.ret.def$weekly$ret_market, na.rm = TRUE),
mean(psei.ret.def$weekly$ret_market, na.rm = TRUE),
mean(set50.ret.def$weekly$ret_market, na.rm = TRUE),
mean(sti.ret.def$weekly$ret_market, na.rm = TRUE)
),
sd.market = c(
sd(bursa.ret.def$weekly$ret_market, na.rm = TRUE),
sd(lq45.ret.def$weekly$ret_market, na.rm = TRUE),
sd(psei.ret.def$weekly$ret_market, na.rm = TRUE),
sd(set50.ret.def$weekly$ret_market, na.rm = TRUE),
sd(sti.ret.def$weekly$ret_market, na.rm = TRUE)
)
)
desc.weekly
88
##
##
##
##
##
##
##
##
##
##
##
##
1
2
3
4
5
1
2
3
4
5
market
mean.def
BURSA -0.0008317085
LQ45 -0.0004910179
PSEI -0.0016963182
SET50 -0.0006929851
STI -0.0014765600
sd.market
0.01161841
0.02142594
0.01965127
0.01727231
0.01647606
sd.def
mean.ent
sd.ent
mean.market
0.02406791 -1.499965e-03 0.02935898 -0.0001836320
0.04942359 1.188733e-06 0.05433163 0.0010187409
0.04066642 -1.823777e-03 0.03980776 0.0006096680
0.02641003 1.732999e-03 0.03309633 0.0014852716
0.03797047 -2.056219e-03 0.04045815 -0.0001314588
desc.monthly <- data.frame(
market = c("BURSA","LQ45","PSEI","SET50","STI"),
mean.def = c(
mean(bursa.ret.def$monthly$ret_port, na.rm = TRUE),
mean(lq45.ret.def$monthly$ret_port, na.rm = TRUE),
mean(psei.ret.def$monthly$ret_port, na.rm = TRUE),
mean(set50.ret.def$monthly$ret_port, na.rm = TRUE),
mean(sti.ret.def$monthly$ret_port, na.rm = TRUE)
),
sd.def = c(
sd(bursa.ret.def$monthly$ret_port, na.rm = TRUE),
sd(lq45.ret.def$monthly$ret_port, na.rm = TRUE),
sd(psei.ret.def$monthly$ret_port, na.rm = TRUE),
sd(set50.ret.def$monthly$ret_port, na.rm = TRUE),
sd(sti.ret.def$monthly$ret_port, na.rm = TRUE)
),
mean.ent = c(
mean(bursa.ret.ent$monthly$ret_port, na.rm = TRUE),
mean(lq45.ret.ent$monthly$ret_port, na.rm = TRUE),
mean(psei.ret.ent$monthly$ret_port, na.rm = TRUE),
mean(set50.ret.ent$monthly$ret_port, na.rm = TRUE),
mean(sti.ret.ent$monthly$ret_port, na.rm = TRUE)
),
sd.ent = c(
sd(bursa.ret.ent$monthly$ret_port, na.rm = TRUE),
sd(lq45.ret.ent$monthly$ret_port, na.rm = TRUE),
sd(psei.ret.ent$monthly$ret_port, na.rm = TRUE),
sd(set50.ret.ent$monthly$ret_port, na.rm = TRUE),
sd(sti.ret.ent$monthly$ret_port, na.rm = TRUE)
),
mean.market = c(
mean(bursa.ret.def$monthly$ret_market, na.rm = TRUE),
mean(lq45.ret.def$monthly$ret_market, na.rm = TRUE),
mean(psei.ret.def$monthly$ret_market, na.rm = TRUE),
mean(set50.ret.def$monthly$ret_market, na.rm = TRUE),
mean(sti.ret.def$monthly$ret_market, na.rm = TRUE)
),
89
sd.market = c(
sd(bursa.ret.def$monthly$ret_market, na.rm = TRUE),
sd(lq45.ret.def$monthly$ret_market, na.rm = TRUE),
sd(psei.ret.def$monthly$ret_market, na.rm = TRUE),
sd(set50.ret.def$monthly$ret_market, na.rm = TRUE),
sd(sti.ret.def$monthly$ret_market, na.rm = TRUE)
)
)
desc.monthly
##
##
##
##
##
##
##
##
##
##
##
##
1
2
3
4
5
1
2
3
4
5
market
mean.def
BURSA -0.004719354
LQ45 -0.003888328
PSEI -0.005007984
SET50 -0.004270817
STI -0.006344700
sd.market
0.02412228
0.03412612
0.03641947
0.03137927
0.03546880
sd.def
0.04178185
0.10842102
0.08400092
0.05888696
0.09408910
mean.ent
-0.005684149
-0.001544446
-0.005611764
0.005790028
-0.009303953
sd.ent
mean.market
0.05339312 -0.0013164236
0.11513720 0.0044753404
0.08124548 0.0026626921
0.06217221 0.0049733041
0.10003241 -0.0008342944
desc.yearly <- data.frame(
market = c("BURSA","LQ45","PSEI","SET50","STI"),
mean.def = c(
mean(bursa.ret.def$yearly$ret_port, na.rm = TRUE),
mean(lq45.ret.def$yearly$ret_port, na.rm = TRUE),
mean(psei.ret.def$yearly$ret_port, na.rm = TRUE),
mean(set50.ret.def$yearly$ret_port, na.rm = TRUE),
mean(sti.ret.def$yearly$ret_port, na.rm = TRUE)
),
sd.def = c(
sd(bursa.ret.def$yearly$ret_port, na.rm = TRUE),
sd(lq45.ret.def$yearly$ret_port, na.rm = TRUE),
sd(psei.ret.def$yearly$ret_port, na.rm = TRUE),
sd(set50.ret.def$yearly$ret_port, na.rm = TRUE),
sd(sti.ret.def$yearly$ret_port, na.rm = TRUE)
),
mean.ent = c(
mean(bursa.ret.ent$yearly$ret_port, na.rm = TRUE),
mean(lq45.ret.ent$yearly$ret_port, na.rm = TRUE),
mean(psei.ret.ent$yearly$ret_port, na.rm = TRUE),
mean(set50.ret.ent$yearly$ret_port, na.rm = TRUE),
mean(sti.ret.ent$yearly$ret_port, na.rm = TRUE)
),
sd.ent = c(
sd(bursa.ret.ent$yearly$ret_port, na.rm = TRUE),
sd(lq45.ret.ent$yearly$ret_port, na.rm = TRUE),
90
sd(psei.ret.ent$yearly$ret_port, na.rm = TRUE),
sd(set50.ret.ent$yearly$ret_port, na.rm = TRUE),
sd(sti.ret.ent$yearly$ret_port, na.rm = TRUE)
),
mean.market = c(
mean(bursa.ret.def$yearly$ret_market, na.rm = TRUE),
mean(lq45.ret.def$yearly$ret_market, na.rm = TRUE),
mean(psei.ret.def$yearly$ret_market, na.rm = TRUE),
mean(set50.ret.def$yearly$ret_market, na.rm = TRUE),
mean(sti.ret.def$yearly$ret_market, na.rm = TRUE)
),
sd.market = c(
sd(bursa.ret.def$yearly$ret_market, na.rm = TRUE),
sd(lq45.ret.def$yearly$ret_market, na.rm = TRUE),
sd(psei.ret.def$yearly$ret_market, na.rm = TRUE),
sd(set50.ret.def$yearly$ret_market, na.rm = TRUE),
sd(sti.ret.def$yearly$ret_market, na.rm = TRUE)
)
)
desc.yearly
##
market
sd.market
## 1 BURSA
0.08647235
## 2
LQ45
0.13950648
## 3
PSEI
0.17380781
## 4 SET50
0.11831818
## 5
STI
0.11755704
mean.def
sd.def
mean.ent
sd.ent
mean.market
0.008809935 0.14199691 -0.06956009 0.17989378 -0.013538542
-0.188161637 0.60267136 -0.09144487 0.61102159
0.037791904
-0.048423563 0.26296209 -0.05935515 0.24406324
0.033027657
-0.036176367 0.09575395
0.028584110
0.09664758 0.07387959
-0.026934304 0.23730174 -0.07890526 0.29982288 -0.002819561
# --- Perform Bootstraps
set.seed(923)
bursa.boots <- get_bootstrap(ent = bursa.ret.ent$weekly$ret_port, def =
bursa.ret.def$weekly$ret_port,
mart = bursa.ret.def$weekly$ret_market)
lq45.boots <- get_bootstrap(ent = lq45.ret.ent$weekly$ret_port, def =
lq45.ret.def$weekly$ret_port,
mart = lq45.ret.def$weekly$ret_market)
psei.boots <- get_bootstrap(ent = psei.ret.ent$weekly$ret_port, def =
psei.ret.def$weekly$ret_port,
mart = psei.ret.def$weekly$ret_market)
set50.boots <- get_bootstrap(ent = set50.ret.ent$weekly$ret_port, def =
set50.ret.def$weekly$ret_port,
mart = set50.ret.def$weekly$ret_market)
sti.boots <- get_bootstrap(ent = sti.ret.ent$weekly$ret_port, def =
91
sti.ret.def$weekly$ret_port,
mart = sti.ret.def$weekly$ret_market)
bursa.boots$summary
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
stat
mean
sd
lower_95
upper_95
return_ent
return_ent -0.0014553219 0.002146633 -0.004874385 0.002342920
return_def
return_def -0.0007627034 0.001635254 -0.003982309 0.002469461
return_mart return_mart -0.0001711314 0.000869300 -0.001795819 0.001258901
sharpe_ent
sharpe_ent -0.0471154695 0.073182314 -0.184166859 0.086393837
sharpe_def
sharpe_def -0.0218496780 0.066029242 -0.134821364 0.116347611
beta_ent
beta_ent 1.1411953516 0.153537872 0.872033838 1.448447610
beta_def
beta_def 0.8262810848 0.159351366 0.472341183 1.114059930
treynor_ent treynor_ent -0.0011361791 0.001743050 -0.004413858 0.002087800
treynor_def treynor_def -0.0008881732 0.002042432 -0.005806296 0.002129214
alpha_ent
alpha_ent -0.0011024074 0.002124764 -0.004868674 0.002712139
alpha_def
alpha_def -0.0004883945 0.001743665 -0.004290165 0.002555158
var_ent
var_ent 0.0496216793 0.005147011 0.040165683 0.059931423
var_def
var_def 0.0393745230 0.007126271 0.028711263 0.055876019
var_mart
var_mart 0.0191166330 0.001541336 0.016340731 0.021863297
pvals
return_ent
1.118856e-13
return_def
3.726207e-10
return_mart
NA
sharpe_ent
3.267909e-04
sharpe_def
NA
beta_ent
1.274825e-62
beta_def
NA
treynor_ent 1.922502e-01
treynor_def
NA
alpha_ent
1.707603e-03
alpha_def
NA
var_ent
1.600616e-172
var_def
8.916714e-101
var_mart
NA
lq45.boots$summary
##
##
##
##
##
##
##
##
##
##
##
stat
mean
sd
lower_95
upper_95
return_ent
return_ent -0.0005653966 0.003590560 -0.007849831 0.006902392
return_def
return_def -0.0007438032 0.003441759 -0.006796823 0.005934984
return_mart return_mart 0.0010026745 0.001476124 -0.001619416 0.003831795
sharpe_ent
sharpe_ent -0.0300898188 0.071766588 -0.158300399 0.123441196
sharpe_def
sharpe_def -0.0391180263 0.070617510 -0.172589728 0.101074142
beta_ent
beta_ent 0.9538808956 0.174305895 0.652076546 1.298985279
beta_def
beta_def 1.1350965276 0.144218576 0.887206636 1.378043666
treynor_ent treynor_ent -0.0014494822 0.003886876 -0.007306696 0.007397913
treynor_def treynor_def -0.0014688970 0.002723354 -0.006367104 0.004040575
alpha_ent
alpha_ent -0.0025399875 0.004210100 -0.011061309 0.005632424
92
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
alpha_def
var_ent
var_def
var_mart
alpha_def -0.0028370919
var_ent 0.0897155611
var_def 0.0824230230
var_mart 0.0338816016
pvals
return_ent
2.995095e-08
return_def
2.548551e-03
return_mart
NA
sharpe_ent
2.055012e-01
sharpe_def
NA
beta_ent
7.269227e-26
beta_def
NA
treynor_ent 9.538985e-01
treynor_def
NA
alpha_ent
4.417350e-01
alpha_def
NA
var_ent
2.800263e-195
var_def
4.709021e-197
var_mart
NA
0.003470748 -0.009301158 0.003552124
0.008230326 0.075033207 0.106431642
0.007383366 0.069018309 0.095598972
0.003145435 0.027432303 0.039649111
psei.boots$summary
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
return_ent
return_def
return_mart
sharpe_ent
sharpe_def
beta_ent
beta_def
treynor_ent
treynor_def
alpha_ent
alpha_def
var_ent
var_def
var_mart
return_ent
return_def
return_mart
sharpe_ent
sharpe_def
beta_ent
beta_def
treynor_ent
treynor_def
alpha_ent
alpha_def
var_ent
stat
mean
return_ent -0.0015513672
return_def -0.0014501299
return_mart 0.0008001018
sharpe_ent -0.0735953122
sharpe_def -0.0689433534
beta_ent 1.2266178897
beta_def 1.2590681197
treynor_ent -0.0019214143
treynor_def -0.0017819859
alpha_ent -0.0033055582
alpha_def -0.0032340494
var_ent 0.0661515962
var_def 0.0675224956
var_mart 0.0312961743
pvals
1.504825e-19
1.758749e-10
NA
5.357680e-01
NA
3.992218e-02
NA
4.833305e-01
NA
7.841407e-01
NA
6.587282e-162
sd
0.003103982
0.003046789
0.001400508
0.076227488
0.073874350
0.159403522
0.155420723
0.002033722
0.001939666
0.002588769
0.002628564
0.006773796
0.006579589
0.002287668
lower_95
-0.007108006
-0.006932871
-0.001812133
-0.219093593
-0.195078987
0.949626813
1.028628665
-0.005613019
-0.005360244
-0.008045846
-0.008309033
0.053690904
0.055021444
0.027366603
upper_95
0.004346349
0.004744136
0.003397017
0.075782058
0.081855596
1.564964265
1.597851085
0.001980635
0.001965926
0.001479461
0.001401241
0.079794393
0.081503822
0.035525361
93
## var_def
## var_mart
1.126819e-169
NA
set50.boots$summary
##
upper_95
## return_ent
0.0054675071
## return_def
0.0027567840
## return_mart
0.0036488364
## sharpe_ent
0.1484139218
## sharpe_def
0.0298655856
## beta_ent
1.0499079616
## beta_def
1.0038420626
## treynor_ent
0.0065758180
## treynor_def
0.0008067914
## alpha_ent
0.0040002798
## alpha_def
0.0005435466
## var_ent
0.0594921809
## var_def
0.0520587974
## var_mart
0.0313348612
##
## return_ent
## return_def
## return_mart
## sharpe_ent
## sharpe_def
## beta_ent
## beta_def
## treynor_ent
## treynor_def
## alpha_ent
## alpha_def
## var_ent
## var_def
## var_mart
stat
return_ent
mean
sd
lower_95
0.0018425353 0.002112373 -0.001972873
return_def -0.0007953500 0.001813896 -0.004111731
return_mart
0.0014047457 0.001194886 -0.001243183
sharpe_ent
0.0138205232 0.067004174 -0.108596585
sharpe_def -0.0998963064 0.070082476 -0.229590051
beta_ent
0.8160633245 0.129269833
0.555469247
beta_def
0.8463182698 0.083155592
0.661874192
treynor_ent
0.0006204702 0.002650411 -0.004560812
treynor_def -0.0025742553 0.001827065 -0.005694075
alpha_ent -0.0006987278 0.002446195 -0.005433731
alpha_def -0.0033860386 0.001991780 -0.007176194
var_ent
0.0527430376 0.003480080
0.045948921
var_def
0.0440595132 0.003595865
0.037849256
var_mart
0.0269803528 0.002277290
0.022771137
pvals
1.121152e-02
3.182644e-09
NA
2.529679e-47
NA
5.675392e-03
NA
7.151230e-36
NA
1.480586e-28
NA
7.863703e-237
2.272856e-174
NA
94
sti.boots$summary
##
upper_95
## return_ent
0.0021806384
## return_def
0.0019780298
## return_mart
0.0014762932
## sharpe_ent
0.0349746498
## sharpe_def
0.0550529584
## beta_ent
1.4147070285
## beta_def
1.3757313683
## treynor_ent
0.0008850946
## treynor_def
0.0014079153
## alpha_ent
0.0016993897
## alpha_def
0.0018972567
## var_ent
0.0841760676
## var_def
0.0787472539
## var_mart
0.0304213446
##
## return_ent
## return_def
## return_mart
## sharpe_ent
## sharpe_def
## beta_ent
## beta_def
## treynor_ent
## treynor_def
## alpha_ent
## alpha_def
## var_ent
## var_def
## var_mart
stat
mean
sd
lower_95
return_ent -0.0021571216 0.0021321850 -0.006125683
return_def -0.0016308979 0.0019970377 -0.005025382
return_mart -0.0001249744 0.0008811457 -0.001800616
sharpe_ent -0.0554734677 0.0490045528 -0.145449633
sharpe_def -0.0433900679 0.0506496285 -0.126385668
beta_ent
1.1764331298 0.1250167083
0.926668574
beta_def
1.1805161859 0.0924924850
1.004756752
treynor_ent -0.0017051441 0.0014928512 -0.004447164
treynor_def -0.0012551332 0.0014350282 -0.003612118
alpha_ent -0.0018767516 0.0019429863 -0.005524940
alpha_def -0.0013607368 0.0018965627 -0.004926089
var_ent
0.0687330663 0.0074457696
0.056686492
var_def
0.0640570653 0.0076492938
0.052923262
var_mart
0.0272478919 0.0016622008
0.023911796
pvals
2.390664e-28
6.018211e-24
NA
1.576436e-02
NA
7.106216e-01
NA
2.262472e-03
NA
7.496724e-03
NA
2.390723e-160
1.239323e-146
NA
grid.arrange(
as.grob(function(){
95
boxplot(list(
Enterprising = bursa.boots$boots$return_ent,
Defensive = bursa.boots$boots$return_def,
Market = bursa.boots$boots$return_mart
), xlab = "BURSA")
}),
as.grob(function(){
boxplot(list(
Enterprising = lq45.boots$boots$return_ent,
Defensive = lq45.boots$boots$return_def,
Market = lq45.boots$boots$return_mart
), xlab = "LQ45")
}),
as.grob(function(){
boxplot(list(
Enterprising = psei.boots$boots$return_ent,
Defensive = psei.boots$boots$return_def,
Market = psei.boots$boots$return_mart
), xlab = "PSEI")
}),
as.grob(function(){
boxplot(list(
Enterprising = set50.boots$boots$return_ent,
Defensive = set50.boots$boots$return_def,
Market = set50.boots$boots$return_mart
), xlab = "SET50")
}),
as.grob(function(){
boxplot(list(
Enterprising = sti.boots$boots$return_ent,
Defensive = sti.boots$boots$return_def,
Market = sti.boots$boots$return_mart
), xlab = "STI")
}),
nrow = 2
)
96
grid.arrange(
as.grob(function(){
boxplot(list(
Enterprising = bursa.boots$boots$var_ent,
Defensive = bursa.boots$boots$var_def,
Market = bursa.boots$boots$var_mart
), xlab = "BURSA")
}),
as.grob(function(){
boxplot(list(
Enterprising = lq45.boots$boots$var_ent,
Defensive = lq45.boots$boots$var_def,
Market = lq45.boots$boots$var_mart
), xlab = "LQ45")
}),
as.grob(function(){
boxplot(list(
Enterprising = psei.boots$boots$var_ent,
Defensive = psei.boots$boots$var_def,
Market = psei.boots$boots$var_mart
), xlab = "PSEI")
}),
as.grob(function(){
boxplot(list(
Enterprising = set50.boots$boots$var_ent,
Defensive = set50.boots$boots$var_def,
Market = set50.boots$boots$var_mart
), xlab = "SET50")
}),
as.grob(function(){
boxplot(list(
Enterprising = sti.boots$boots$var_ent,
Defensive = sti.boots$boots$var_def,
Market = sti.boots$boots$var_mart
97
), xlab = "STI")
}),
nrow = 2
)
98
Appendix B : Figures
Figure 1. Distributions of bootstrapped returns of the enterprising and defensive investors,
compared against market index performance
99
Figure 2. Distributions of bootstrapped values at risk of the enterprising and defensive investors,
compared against market index performance
100
Turnitin Similarity Index
101
102
103
104
105
106