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CHAPTER 2

LITERATURE REVIEW

2.1 Theoretical Foundation

This section summarizes the available literature on the field of value investing, value

premium, capital asset pricing model, modern portfolio theory and behavioral

finance. Additionally, this section also provide a brief comparison of previous

researches in the field of value investing from around the globe from the past 3

decades.

2.1.1 Investment

Investment is defined as the current commitment of money or other resources in the

expectation of reaping future benefits (Bodie, Kane, Marcus, & Jain, 2014). Reilly

and Brown, on a similar note, defined investment as the current commitment of

money for a period of time in order to derive future payments that will compensate

the investor for: (1) the time the funds are committed, (2) the expected rate of

inflation, and (3) the uncertainty of the future payments. The “investor” mentioned

here may refer to an individual, a government, a pension fund, or a corporation

(Reilly & Brown, 2011).

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An investor may decide to invest his money in real assets of the economy or financial

assets. Real assets are assets which generate goods, services and net income to the

economy. Examples of real assets include, but not limited to land, buildings,

machines,

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consumer durables and inventories of goods. Financial assets on the other hand are

assets which do not contribute directly to the productive capacity of the economy, but

rather act as means for individuals to hold claims on real assets. Examples of

financial assets include, but not limited to, fixed-income securities or debt securities

such as bonds, common stocks or equities, and derivative securities (Bodie, Kane,

Marcus, & Jain, 2014). This study focuses on the study of stocks.

2.1.2 Stocks Classification

One of the clearest mechanisms of human thought is classification, the grouping of

objects into categories based on some similarity among them. This process allows

human to categorize similar entities in order to better understand the entities (Rosch

& Lloyd, 1978; Wilson & Keil, 1999).

The principle of classification also exists in the world of investments. For instance, in

Indonesia, the Indonesian Stock Exchange (hereby referred to as IDX) classifies

stocks into 9 distinct sectors, they are: 1) Agriculture, 2) Mining, 3) Basic Industry

and Chemicals, 4) Miscellaneous Industry, 5) Consumer Goods Industry, 6) Property,

Real Estate and Building Construction, 7) Infrastructure, Utilities and Transportation,

8) Finance, and 9) Trade, Services and Investment. Within these sectors, stocks

whose businesses operate in the same line of business are further categorized to

separate classes. For example, stocks categorized in the mining sector can be further

classified as operating in a) Coal Mining, b) Crude Petroleum and Natural Gas

Production, c) Metal and Mineral Mining, d) Land/Stone Quarrying, e) Others. This

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method of classification is called the sectorial classification system and the sectorial

classification system used to categorize companies listed at the Indonesia Stock

Exchange is the Jakarta Stock Industrial Classification (JASICA) (Indonesia Stock

Exchange, 2015).

Individual and institutional investors, however, not only classify stocks based on the

sector in which the company is operating, but also the size of the company or market

capitalization of the company, the company‟s value, or in some circumstances, the

level of return that is expected from the company. Some popular stocks classifications

are: a) Blue-chip stocks, b) Growth stocks, c) Value stocks, d) Large-cap stocks, e)

Mid-cap stocks, f) Small-cap stocks, g) Defensive stocks, h) Cyclical stocks, and i)

Income stocks (Thomas, 2006).

Blue chip stocks represent the largest companies in the equity market. These

companies usually have very high earnings year after year, and have a reputation of

stability and exceptional corporate management. Defensive stocks are stocks of

companies that are generally stable all year around as they are companies that provide

important goods and services that are used in good as well as bad economic times

such as tobacco companies, healthcare company, and food and beverages companies.

Income stocks are generally some which offer above average dividend payments to

their shareholders. Income stocks companies are usually very stable, and have gained

a large market share, as they can afford to heavily reward their shareholders. Cyclical

stocks are stocks of companies whose sales and profits generally fluctuate depending

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on the business cycle or condition of the economy such as mining companies

(Thomas, 2006).

When determining whether a stock is a large-cap, mid-cap, or a small-cap stock, the

stock‟s market capitalization, which is the market value of all of the company's

outstanding shares, should first be determined. This is done by multiplying the

company's shares outstanding by the current market price of one share. The

investment community uses the market capitalization figure to determine a company's

size, as opposed to the company‟s sales or total asset figures. Investment

professionals in the United States differ on their exact definitions, but the current

approximate categories of market capitalization in the United States are (Wayman,

2015):

Large-cap: Above $10 billion.

Mid-cap: $2 billion to $10 billion

Small-cap: Less than $2 billion.

In Indonesia, unfortunately, the distinction between large-cap, mid-cap and small-cap

companies is not documented. However, it is often accepted that companies who are

featured in the top 50 leading companies in market capitalization in the annual IDX

Fact Book are considered large-cap companies (Kontan, 2008).

When making fund allocation decisions in equity markets, often do investors (both

individual and institutional investors) first categorize stocks into distinct broad

classes such as large-cap stocks, small-cap stocks, technological stocks, non-

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technological stocks, value stocks or growth stocks and then decide how to allocate

their funds among these classes (Bernstein, 1995; Swensen, 2000). This process of

allocating resources among distinct classes rather than among individual securities is

known as style investing (Barberis & Shleifer, 2003).

One of the most popular debates which had been present for the last few decades in

the academic world of financial economics is the debate of the superiority in

investing in either the style of value investing or growth investing. Value investing

refers to the investing strategy which focuses on investing in value stocks, while

growth investing is an investing strategy whereby growth stocks are preferred.

Investment managers tend to have a preference for one of the two styles, depending

on their personal investment style, or they may be guided by the state of the economy

(Bourguignon & de Jong, 2003). This tendency among investors is so common that it

has given rise to actual style indices that allow academics to track the performance of

growth and value separately, market by market, and that provide benchmarks for

investors pursuing a particular style. The indices in question refer to the Morgan

Stanley Capital International (MSCI) Value and Growth Indices respectively (Morgan

Stanley, 2007). As the aim of this study is to conduct a test of the efficacy of

Benjamin Graham‟s value stocks selection criteria using market-adjusted returns of

portfolios of value stocks screened according to Benjamin Graham‟s value stocks

criteria in the Indonesia stock market, subsequent literature review focuses on the

concepts of value investing, the efficiency market hypothesis and prior researches

related to the field of value investing.

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2.1.3 Value Stocks and Value Investing

Value investing is currently one of the most famous investment strategies available in

the world of investments. It was first proposed by Benjamin Graham, a British-born

American investor and academic who taught his approach to investing, the value

investing approach, at the Columbia Business School in 1928. With the help of his

assistant, David L. Dodd, Graham introduced the concept of value investing to the

public through his books “Security Analysis”, first published in the aftermath of the

Great Depression in 1934, and the layman edition of “Security Analysis”, entitled

“The Intelligent Investor”, which was released in 1949.

Value investing is defined by Graham and Dodd as the process of finding and

purchasing securities that are selling below their true value (or intrinsic value), based

upon fundamental analysis (Graham & Dodd, 1934). Graham defined stocks which

trade below their intrinsic value as „value‟ stocks (Graham & Dodd, 1934). This

definition of value stocks is shared by other scholars such as Basu (1977);

Oppenheimer (1984); Capaul, Rowley and Sharpe (1993); Chan, Hamao, and

Lakonishok (1991); Fama and French (1992; 1998); Lakonishok, Shleifer and Vishny

(1994); Klerck and Maritz (1997); Piotroski (2000); Dhatt, Kim and Mukherji (2004);

Athanassakos (2009); Singh and Kaur (2014).

Graham & Dodd argued that value stocks are traded below its intrinsic value in the

market may be due to poor performance in the past in which the expectation from

majority of investors arises that this performance will continue in the future (Graham

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& Dodd, 1934). As a result, these stocks became „out-of-favor‟ stocks in the market

(De Bondt & Thaler, 1985). Graham and Dodd added that poor performance does not

have to refer in particular towards bankruptcy or default, but that it was a signal that

the company reached its maturity, in which the company‟s earnings and growth

becomes stable and the company could not give any indication of excessive growth

that investors expect since the company do not have any profitable investment

opportunities to diversify into within a particular year (Graham & Dodd, 1934). This

is acknowledged by both De Bondt & Thaler (1985) and Athanassakos (2009).

While Graham & Dodd (1934) argued that stocks become value stocks due to poor

performance or due to indications on maturity and stability, Fama & French took a

different approach. Fama and French assumed that „value‟ companies are in distress

and are therefore trading at low prices, and thus propose a higher risk for investors

(Fama & French, 1998). The assumption of distress was acknowledged by Chen and

Zhang (1998). These scholars suggest that, besides distress, other factors such as high

financial leverages, overcapacity, and uncertainty in future earnings made them „out

of favor‟ by majority of investors in the market.

Benjamin Graham further proposed the concept of “margin of safety” as the

cornerstone principle for operationalizing value investing. Margin of safety is a

measurement of the degree to which an asset is trading at a discount to its intrinsic

value. In other words, a stock‟s margin of safety is the difference between the stock‟s

intrinsic value and the market price (Graham, 1973). The basic premise of the margin

of safety is that the higher the discount of the market price to the intrinsic value of a

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particular stock, the better the odds that the investment will not result in a loss to the

investor (Graham, 1949). Graham recommended investors to invest in stocks which

have significant gap in its market price and the intrinsic value so that the margin of

safety can protect the investor in the event of a market downturn (Graham & Dodd,

1934). Thus, the value investing strategy proposed by Graham calls for investing in

companies that have low prices relative to earnings, book value or other measures of

value with a significant margin of safety in order to protect the investor (Graham &

Dodd, 1934; Graham, 1949; Graham, 1973).

Although academic research on the concept of margin is lamentably limited, a

quantitative analyst and professor at the Columbia Business School, Kenton Yee

(2008), proposed a quantitative formula based on real options to carry out a valuation

of stocks and determine the appropriate margin of safety for every particular listing.

In his research, Yee took into account the risks that the value investor faces such as

market risk: volatility of the market price; news risk: possibility of bad news affecting

the time when the investor expects the market to recognize the fair value of his stock;

valuation risk: the risk of bias or imprecision on the stock valuation; and convergence

risk: uncertainty about the date when the market will converge to the projected

valuation estimate (Yee, 2008). Yee discovered that for high quality issues (blue chip

stocks), value investors should expect to pay up to a maximum of 90% of intrinsic

value while more speculative stocks require more room for error and should be

purchased for only a maximum of 50% discount to intrinsic value (Yee, 2008). While

Graham did not explicitly how much should a margin of safety be, his own testimony

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of his time in the Graham-Newman Corporation indicated that the margin of safety

for a stock should be 33% below its intrinsic value, thus was more conservative

compared to contemporary investors as discovered by Yee (Graham, 1949).

Most significantly, Graham and Dodd proposed 10 criteria for screening for value

stocks. The 10 criteria are (Graham & Dodd, 1934; Blustein, 1977):

1. An earnings-to-price yield at least twice the AAA bond yield.

2. A price-earnings ratio less than 40 per cent of the highest price-earnings ratio

the stock had over the past five years.

3. A dividend yield of at least two-thirds the AAA bonds yield.

4. Stock price below two-thirds of tangible book value per share.

5. Stock price below two-thirds "net current asset value."

6. Total debt less than book value.

7. Current ratio greater than two.

8. Total debt less than twice "net current asset value."

9. Earnings growth of prior 10 years at least at a 7 percent annual (compound)

rate.

10. Stability of growth of earnings in that no more than two declines of 5 per cent

or more in year-end earnings in the prior 10 years are permissible.

Multiple scholars have tested the value stocks criteria proposed by Benjamin Graham

over the past 4 decades, albeit not exactly using the 10 criteria which Graham

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proposed for screening value stocks. More information regarding previous researches

in the field of value investing is given in section 2.1.10.

2.1.4 Growth Stocks and Growth Investing

On the other side of the coin lies growth investing. Growth investing is a style of

investing which focuses on investing on growth stocks (Barberis & Shleifer, 2003).

Growth stocks, or glamour stocks as Graham and Dodd (1934) called them, are

generally defined as stocks which trade at high prices in the markets relative to the

underlying firm‟s fundamentals. Scholars argued that growth stocks have high prices

relative to the firm‟s fundamentals in markets since investors have high expectations

on their future earnings and future growth rate (Bourguignon & de Jong, 2003). For

most growth stocks, a high degree of future growth expectation tends to correlate

with high price-to-book ratios, high price-earnings ratios and low dividend yields

(Johnson, 2004).

The rationale behind growth investing is to buy the stocks of companies with

maintainable growth and then benefit from the company‟s stock price increase as the

company grows in the future (Capaul, Rowley, & Sharpe, 1993). One of the

weaknesses of this approach is that investors often assume that the stock price and

company‟s growth are positively correlated (Bourguignon & de Jong, 2003).

Empirical evidence throughout the years has showed that investing in growth stocks

has rarely beat value investing (Chan, Hamao, & Lakonishok, 1991; Fama & French,

1992; 1998; Bauman, Conover, & Miller, 1998; Athanassakos, 2009). In these

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researches, the researchers employed the P/E ratio and the P/BV ratio to differentiate

between value stocks and growth stocks. It was found that stocks with low P/E ratio

and low P/BV ratio generated significantly higher returns compared to stocks with

high P/E and P/BV ratios. These scholars identified this phenomenon as value

premium.

2.1.5 Value Premium

When value stocks outperform growth stocks within a particular setting, it is said that

a positive value premium exists (Capaul, Rowley, & Sharpe, 1993). The positive

value premium refers to the difference between the returns obtained from portfolios

composed of value stocks and portfolios composed of growth stocks, whereby

positive means that the returns of the portfolios composed of value stocks

significantly beat the returns of the portfolios composed of growth stocks. Graham

and Dodd (1934) first coined this phenomenon in the aftermath of The Great

Depression as the „value-effect‟.

The value premium is deemed as important since the value premium refers to whether

investors are more contented in purchasing value stocks or growth stocks (Capaul,

Rowley, & Sharpe, 1993; Bird & Cassavechia, 2007). The higher the value premium,

the more likely it is that investors give preference to value stocks due to the

providence of higher returns compared to growth stocks (Bird & Cassavechia, 2007).

When the value premium lies around zero, it is an indication that the investor may be

indifferent towards investing in either value or growth stocks since there are no

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significant return spread between investing in either of them (Capaul, Rowley, &

Sharpe, 1993). However, when the value premium is below zero, several scholar

argued that it denotes not the existence of a value premium but the existence of a

value discount, which indicates investing in growth stocks generates significantly

greater returns compared to investing in value stocks (Brown, Rhee, & Zhang, 2008).

Prior empirical studies have proved that value stocks outperform growth stocks. Fama

and French (1992) demonstrated that, on average, value stocks outperformed growth

stocks in the US stock market. Researches conducted in international markets showed

that value premium also existed in international markets, as evidenced by Capaul et

al‟s (1993), and Fama and French‟s (1998) researches. Bauman, et al. (1998) further

confirmed the previous researches by documenting the existence of value premium in

international markets. In an attempt to update earlier researches, Fama and French

performed a new study on the international value premium. In all of the four regions

examined (North America, Europe, Japan and Asia Pacific), value premium is found,

echoing results from their earlier thus firmly supporting the argument of the existence

of a value premium in world markets (Fama & French, 2012).

Although the existence of the value premium is widely recognised, the source of the

value premium has been a subject of debate. According to scholars who accepted the

behavioral explanation of the existence of value premium such as Kahneman and

Tversky (1979), De Bondt and Thaler (1985), Lakonishok, et al. (1994) and Haugen

(1995), value premiums exist due to expectation and overreaction errors in returns

made by investors and do not function as a compensation or proxy for risk. The

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behavioral point of view argued investors‟ cognitive biases under-values the „out-of-

favor‟ stocks and overvalues growth stocks. For instance, De Bondt & Thaler (1985)

argued that higher returns of value stocks are the results of the notion that investors

have a tendency to overreact towards past events, such as earnings announcements,

thus overvaluing growth stocks. Over time, these market under-valuations and over-

valuations are corrected, resulting in a lower expected return for growth stocks and a

higher expected return for value stocks (Lakonishok, Shleifer, & Vishny, 1994).

Fama and French (1992), on the other hand, took a position as a proponent of the

efficient market hypothesis and attributed the higher returns of value investing to

increased risk in investing in value stocks. Chen and Zhang (1998) and Black and

Macmillan (2006) shared Fama and French‟s viewpoint and contented of the

importance of risk factor in value investing.

The behavioral explanation of the value premium thus refers towards the existence of

market inefficiency. This clearly contradicts the efficient market hypothesis since the

basic premise of the efficient market hypothesis is that errors are unbiased since the

theory implies that stocks fully reflect all publicly available information, thus,

implying informational efficiency. The next section of the literature review further

details the efficient market hypothesis and behavioral finance, and the debate between

behavioral finance and efficient market hypothesis in search for the explanation for

the existence of value premium.

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2.1.6 Efficient Market Hypothesis

In its original incarnation, the efficient market hypothesis (EMH) is the simple

proposition that market prices incorporate all available information. The nature of

information does not have to be limited to financial news and research alone.

Information about political, economic and social events will all be reflected in the

stock price. Eugene Fama created this hypothesis in his groundbreaking work

“Efficient Capital Markets: A Review of Theory and Empirical Work” (Fama E. ,

1970), which was created based on the Random Walk Theory, an earlier financial

theory that stated that stock prices change randomly, like a drunkard walking down

the streets, making it impossible to predict stock prices, thus implying that historical

patterns cannot be used to predict future movements in any kind of meaningful way

(Malkiel, 2007). The EMH complemented the Random Walk Theory in which the

EMH take into consideration not only historical records of a firm which is already

available publicly, but also any future expectations, such as earnings or dividend

payments (Fama E. , 1970). There are three basic assumptions surrounding EMH.

First, no transaction costs exist (Fama E. , 1970). Second, past, current and correct

information is available for everyone and lastly, everyone would interpret the results

similarly and act accordingly (Fama E. , 1970). Shleifer (2000) commented that the

important idea that needs to be true for the EMH to be correct is the assumption that

majority of investors are rational in their security valuation and that if there are any

irrational investor, their effects on the securities‟ prices cancel each other out back to

its true value.

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According to the EMH, as prices respond to information available in the market, and

because all market participants have the access to the same information, stocks tend

to trade at their fair value on stock exchanges, thus making it impossible for investors

to either purchase undervalued stocks or sell stocks for inflated prices (Fama E. ,

1970). As such, it should be impossible to outperform the overall market through

expert stock selection or market timing, and that the only way an investor can

possibly obtain higher return is by purchasing riskier investments (Fama E. , 1970;

Chen & Zhang, 1998).

Different forms of the EMH are defined in terms of the rapidity and accuracy of price

adjustment to news within different information sets. In its weak form, prices on

traded securities already reflect all past overtly available information (Fama E. ,

1970). The semi-strong-form of the EMH suggests that prices reflect not only all

publicly available information but that security prices instantly change to reflect new

public information (Fama E. , 1970). The strong-form of EMH goes even further by

asserting that prices instantly adjust to all information, even unrevealed or insider.

The strong form of EMH considers that not even insider information could give an

investor an advantage over other investors in the market (Fama E. , 1970). In all three

of its forms, the EMH challenges the principles of value investing since in efficient

markets there wouldn‟t be any opportunities for any investor to buy stocks with a

discount to its intrinsic value, and investing with a margin of safety wouldn‟t be

possible without assuming superior risk.

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Fama (1970) emphasized that the EMH must be tested in the context of excess returns

in order to prove its validity. Damodaran argued that since an excess return on an

investment is the difference between the actual and expected return on that

investment, there is implicit in every test of market efficiency a model for this

expected return (Damodaran, 2002). When there is evidence of excess returns in a test

of market efficiency, it may be an indication that markets are inefficient or that the

model used to compute expected returns is wrong or even both. In most cases, the

expected return is adjusted for risk using the capital asset pricing model (CAPM),

which was created in the 1960s by William Sharpe (Sharpe, 1964; Damodaran,

2002). According to the CAPM, 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 (Sharpe, 1964; French, 2003), a concept that is also used in

Modern Portfolio Theory (Markowitz, 1952).

However, Fama and French argued that if the beta measure of systematic risk from

the CAPM is accepted as the correct risk measurement statistic, then the size effect

can be interpreted as indicating an anomaly and market inefficiency, because by using

the beta, portfolios which consist of smaller stocks would have excess risk-adjusted

returns (Fama & French, 1992; 1998). Fama and French‟s comments sparked the

debate of the „death of beta‟ and numerous scholars have attempted to search for

empirical evidence regarding the usefulness of beta. Chan and Lakonishok, in their

paper “Are the Reports of Beta's Death Premature” (1993) reported that they found

little evidence of a link between beta and stock returns in the NYSE over the period

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of 1926 to 1991. Yet oddly enough, when they excluded data from the period after

1982, the results suggested that beta worked (Chan & Lakonishok, 1993). Grundy

and Malkiel, in their defense of beta argued that beta is a quite serviceable measure of

risk since they discovered that from 1968 to 1992, high-beta stocks tend to experience

greater losses than low-beta stocks in a declining market (Grundy & Malkiel, 1996).

Fisher Black was of the same opinion, that since investors are becoming more risk-

averse, investors and portfolio managers need beta more than ever to identify high-

beta and low-beta stocks (Black F. , 1993). Thus, beta was deemed to be still useful

for the CAPM by these researchers in order to discover the volatility of stocks

compared to the market index.

Damodaran indicated there are two distinct ways of testing for market efficiency, and

the approach used depends on the investment scheme being tested (Damodaran,

2002). An investment scheme which is based upon trading on information events

such as stock splits or earnings announcements is tested using an event study while an

investment scheme based upon trading on an observable characteristic of a firm, such

as categorizing firms with low P/E together or low P/BV together, is tested using a

portfolio approach (Damodaran, 2002). In both methods, the CAPM is used to

calculate the risk-adjusted returns of a stock or a portfolio (Damodaran, 2002).

One of the leading theories in modern finance is the Modern Portfolio Theory which

stands on the same ground as EMH and contended that for an investor to gain excess

return, the investor must bear additional risk. The Modern Portfolio Theory also

applies CAPM and uses beta as its main risk measure. Most of the previous studies

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briefly described earlier (Fama & French, 1992; Lakonishok, Shleifer, & Vishny,

1994; Chen & Zhang, 1998; Xiao & Arnold, 2008; Singh & Kaur, 2014) tested not

only the efficiency of markets but also the concepts of Modern Portfolio Theory. The

next subsection presents a brief description of the Modern Portfolio Theory.

2.1.7 Modern Portfolio Theory

The Modern Portfolio Theory (MPT), championed by Markowitz (1952), accepted

EMH‟s argument that investing without assuming superior risk means the investor

will not get significantly higher return compared to the market. The MPT also utilizes

several assumptions similar to EMH, that is, transaction costs are non-existent, and

that investors are rational (Markowitz, 1952). The main argument of the MPT is that

it is not enough for investors to look at the expected risk and return of only one

particular stock, but that investors may be better off by diversifying their investment

in several stocks simultaneously. The basic premise of this argument is that by

investing in more than one stock, Markowitz believed that diversification leads to a

reduction in the risk of the portfolio. Risk, as defined by the earliest version of MPT,

is the deviation from the average return of the stock. In other words, it is the standard

deviation of the mean of the stock returns (Markowitz, 1952). According to the MPT,

it is possible to construct an "efficient frontier" (figure 2.1) of optimal portfolios

offering the maximum possible expected return of a portfolio, for a given level of risk

which the investor is willing to accept.

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Figure 2.1 - The Efficient Frontier (Investopedia, 2015)

In one of the earliest studies of the MPT by William Sharpe (1964), Sharpe

introduced Capital Market Line (CML) as an extension of the efficient frontier idea

proposed by Markowitz (1952), where the risk and the rate of return are on the axes.

Sharpe‟s model represents capital markets for rational investors and it is believed an

investor is able to choose which ever point from the line, but if more return is

expected of the portfolio, it automatically means the investor should bear more risk as

well (Sharpe, 1964). It is important to note that Sharpe‟s assumptions was a

precipitate to the birth of the EMH, that Sharpe created the CML with the assumption

that investors are rational and there are no informational inefficiency (Sharpe, 1964).

To calculate for additional risk, Sharpe created the CAPM with the standard deviation

of the stock as the core risk measurement (Sharpe, 1964).

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Figure 2.2 - Capital Market Line (CML) (Investopedia, 2015)

Jack Treynor was also one of the researchers who developed the efficient frontier

theory and introduced the concept of the security market line (SML) (Treynor, 1965).

The SML, as opposed to the CML, represents the expected returns of the asset as a

function of its sensitivity to market fluctuations, or more commonly known as the

asset‟s beta (Treynor, 1965). Thus, the main difference between SML and CML is

that SML uses beta as the standard measurement of risk while the CML uses the

asset‟s standard deviation as the standard measurement of risk. However, both the

SML and CML were created under the same assumptions, that investors are rational

and there are no informational inefficiency in the market.

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Figure 2.3 - Security Market Line (SML) (Investopedia, 2015)

There are generally three basic methods for risk-adjusted performance evaluation for

a portfolio. They are: Sharpe‟s measure (Sharpe‟s ratio), Treynor‟s measure and

Jensen‟s measure (also known as Jensen‟s alpha). Sharpe‟s measure was introduced

by William Sharpe in 1966 and is commonly used to measure the reward-to-volatility

trade-off of a portfolio of risky assets. This is calculated by dividing the average

excess return of the portfolio by the standard deviation of returns of the portfolio

(Sharpe, 1966). Treynor‟s measure is a slight modification of Sharpe‟s measure

whereby it uses systematic risk (beta of portfolio) rather than the total risk (standard

deviation) in its calculation (Treynor, 1965). Ergo, the Treynor‟s measure of a

portfolio is obtained by dividing the average excess return of the portfolio by the beta

of the portfolio (Bodie, Kane, Marcus, & Jain, 2014). The primary purpose of the

Sharpe ratio is to determine whether the investor is making a significantly greater

return investing in equities compared to investing in risk-free instruments such as

government bonds (Bodie, Kane, Marcus, & Jain, 2014). On the other hand,

Treynor‟s measure examines how well a portfolio outperforms the equity market as a

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whole, thus using beta as its main risk measurement (Bodie, Kane, Marcus, & Jain,

2014). Both measures, however, measure the excess return of a portfolio per unit of

risk. Jensen‟s measure, or Jensen‟s alpha, is used to define the excess return of a

portfolio over and above that which is predicted by the CAPM, given the portfolio‟s

beta and the average market return (Jensen, 1969). Simply explained, a positive

Jensen‟s alpha for a given portfolio indicates that the portfolio has “beaten the

market” (Bodie, Kane, Marcus, & Jain, 2014). The higher the alpha, the higher the

excess return and the better is the risk-adjusted returns (Bodie, Kane, Marcus, & Jain,

2014).

Although they are the cornerstones of modern financial theory, the EMH and the

MPT are highly controversial and often disputed by both proponents of value

investing and behavioral finance scholars (behaviorists). If the EMH and the MPT

holds completely true, then researches into value investing in general, and low P/E

and P/B ratio stock portfolios (value stocks portfolios) versus high P/E and P/B stock

portfolios (growth stocks portfolios) in particular should not show any superior

profits, implying the non-existence of any value premium nor any value discounts.

This is due to the fact that the prices of these stocks should have already incorporated

the potential gains from them in the future. In other words, no significant risk

adjusted returns should be found.

However, previous studies in the field of value investing (Fama & French, 1992;

Lakonishok, Shleifer, & Vishny, 1994; Chen & Zhang, 1998; Xiao & Arnold, 2008;

Singh & Kaur, 2014) have mostly used the portfolio approach as described by

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Damodaran (2002) and, as observed, have given ample evidence of market

inefficiency while significantly supported the argument that value investing beat the

market average.

Additionally, the MPT would be deemed as obsolete as the MPT incorporated EMH‟s

assumptions that markets are efficient and that investors are rational individuals. As

already evidenced in the discussion regarding value investing and value premium,

investing in value stocks have proven to generate returns higher than the market and

that value premium exists in the world (see subsection 2.1.3 and 2.1.4). Most

famously Warren Buffett, in his famous essay “The Superinvestors of Graham and

Doddsville” (Buffett, 1984), which was delivered at Columbia University in 1984,

challenged the existence of the EMH and the MPT by displaying the performances of

value investors who have beaten the S&P 500 index on an annual basis. Even the

champions of EMH, Fama and French, had empirically proven shown the existence

of value premium around the world (Fama & French, 1998). How then could the

value premium be explained if not by the EMH, CAPM or MPT? Behaviorists then

tried to explain this phenomenon using behavioral finance, which is explained in the

next subsection.

2.1.8 Behavioral Finance

One of the main criticisms to the EMH and the MPT comes from researchers of the

field of behavioral finance, who were of the opinion that investor‟s cognitive biases,

such as overconfidence, overreaction, representative bias, information bias, and

25

numerous predictable human errors in reasoning, were liable for the failures in

financial markets (Kahneman & Tversky, Prospect Theory: An Analysis of Decision

Under Risk, 1979; De Bondt & Thaler, 1985; Lakonishok, Shleifer, & Vishny, 1994).

Behaviorists pointed out that the EMH forgot the essential concept that human beings

are sometimes not rational in nature and that this does make a difference when it

comes to investing (Kahneman & Tversky, Prospect Theory: An Analysis of Decision

Under Risk, 1979). One general conclusion about behavioral finance studies is that

errors in reasoning lead most investors to avoid underpriced stocks and buy growth

stocks at any price no matter how expensive the growth stock is. DeBondt and Thaler

(1985) argued that investors are prone to overconfidence in their ability to forecast

the market movements and waves of optimism and pessimism which causes the stock

market to overreact, thus generating arbitrage opportunity for shrewd investors to

profit from bargains in ignored value stocks. While one would expect most errors to

cancel out, this is not the case as a significant behavioral pattern in finance known as

herding behavior tends to accentuate the mistakes of those who lead (De Bondt &

Thaler, 1985).

One example is a study by Kahneman and Tversky (1973) where it was concluded

that a person tends to overemphasize recent experiences compared to the overall

picture of a phenomena. This manifests itself as too extreme of reactions built on not

enough evidence, depending on how recent events have unfolded (Kahneman &

Tversky, 1973). Barberis, Shleifer and Vishny (1998) have found evidence which

showed that in the short term investors often overreact to private information and

26

underreact to public information. This, in turn, created mispricing in the market

which arbitrageurs took advantage of (Barberis, Shleifer, & Vishny, 1998).

Another example of investors‟ irrational behaviors is regret avoidance, a cognitive

dissonance whereby investors refuse to admit to themselves that they've made a poor

investment decision so they don't have to face the unpleasant feelings associated with

that particular decision, in turn causing investors to hold losing positions too long in

the hope that they will become profitable or sell winners too soon to lock in profits

lest they turn into losses (Kahneman & Tversky, 1979). Tykocinski, Israel and

Pittman (2004) coined another term for this phenomenon: inaction inertia. In

Indonesia, this was confirmed by Sitinjak and Gozali‟s (2012) study which indicated

that there is a tendency for investors in Jakarta, Semarang and Yogyakarta to release

their winning stocks faster than they would for their losing stocks.

Financial bubbles are also thought to be one of the examples of investors‟

irrationality in the market. Robert Shiller defined a financial speculative bubble as “a

situation in which news of price increases spurs investor enthusiasm, which spreads

by psychological contagion from person to person, in the process amplifying stories

that might justify the price increase”, which in turn, attracts “larger and larger class of

investors, who, despite doubts about the real value of the investment, are drawn to it

partly through envy of others‟ successes and partly through a gambler‟s excitement.”

(Shiller, 2000). Former Chairman of the Federal Reserve of the United States, Alan

Greenspan, called this phenomenon “irrational exuberance”, in a speech given at the

American Enterprise Institute during the midst of the Dot-com bubble at the end of

27

the 1990s (Greenspan, 1996). Speculative bubbles start when a new paradigm is

introduced to the market, such as the introduction of new technologies during the

Dot-com bubble at the end of the 1990s (Geier, 2015), or low interest rates, as

evidenced prior to the bursting of the US housing bubble in 2007 (Denning, 2011),

driving optimism levels to its peak which causes asset prices to soar, ergo generating

a financial bubble (Tuckett & Taffler, 2008).

Behavioral pattern during financial bubbles is explained under the 5 stage model

proposed by Minsky (1984), who suggested that bubble episodes can be divided in 5

stages, namely: 1) displacement, 2) boom, 3) euphoria, 4) profit taking, and 5) panic.

Although there are various interpretations of the cycle, the general pattern of bubble

activity remains fairly consistent: new environment conditions gives excitement to

investors, thus giving the initial momentum for the boom. Valuations of the asset then

skyrocket and speculators start buying with the hope of selling at even higher prices.

Suddenly, a number of savvy investors would notice the potential maturity of the

bubble and start selling their positions to take profit until finally the asset values fall

dramatically as the remaining investors panic sell their holdings to minimize or to cut

losses (Minsky, 1984).

While these anomalies are not the main focus of this research, these researches in the

field of behavioral finance support the assumption that investors do not always make

rational decisions based on the information that is available to them.

28

2.1.9 Holding Period and Investment Returns

Multiple researches have focused mainly on proving the existence of value premium

but less attention has been given towards portfolios‟ holding periods the effect of

different holding periods on return on investment. Samuelson (1969) and Merton

(1969) were considered to be the first to recognize the theoretical impact of

investment horizons on portfolio decision making. These researchers discovered that

the variance of total return risky assets actually increases with the investment horizon

(Merton, 1969; Samuelson, 1969). This is because although the probability of loss

decreases with the investment horizon, the magnitude of such losses increases with

longer investment horizon (Merton, 1969; Samuelson, 1969).

One of the leading researchers in the field of the effect of investment horizons

towards returns on investment is Professor Haim Levy. In his seminal paper,

“Portfolio Performance and the Investment Horizon” (1972), Levy (1972)

investigated the effect of investment horizons on return on investment and found that

more attention should be devoted to the selection of the investment horizon since

different investment horizons would generate different returns. Further researches

conducted by Levhari and Levy (1977) discovered the effect of investment horizons

on systematic risk (beta) and the CAPM. Levhari and Levy (1977) believed that

investors with longer horizons should hold a higher proportion of their portfolio in

risky assets since lengthening the investment horizon may reduce risk, an effect most

commonly known as „time diversification‟ (Lee, Kim, & Kim, 2015). They also

discovered that short-term investment horizons are poor assumptions to test the

29

CAPM and systematic risk of a portfolio (Levhari & Levy, 1977). This finding is

consistent with Handa, Kothari and Wasley (1993), Daniel and Marshal (1997), and

Parker and Julliard (2005) who were able to reject the CAPM using monthly returns

but fail to reject over annual holding periods.

Benjamin Graham recommended that the stocks should be held for either two years or

until the investor has received a 50 percent price appreciation on the stocks,

whichever came first (Graham, 1973; Oppenheimer, 1984). Kryzanowski and Zhang

(1992) discovered that undervalued stocks would revert to its mean price in 1.5 to 2.5

years. Gunthorpe and Levy (1994) found that defensive stocks generated more returns

when held on longer period, which may explain Kryzanowski and Zhang‟s (1992)

findings that holding undervalued stocks at longer period would generate larger

returns due to the tendency of the stocks to revert to its mean price. On a similar note,

Lakonishok et al (1994), Bird and Whitaker (2003), Rousseau and Van Rensburg

(Rousseau, 2003) and Bird and Casavecchia (2007) discovered that longer holding

periods will increase the returns of portfolios of value stocks.

2.1.10 Previous Researches

Multiple scholars have tested the value investing strategy proposed by Benjamin

Graham over the past 4 decades, albeit not exactly using the 10 criteria which

Graham proposed for screening value stocks as stated in section 2.1.3. The evidence

of outperformance for stocks with relatively lower price-to-earnings (P/E) ratios on

the New York Stock Exchange (NYSE) is first discovered by Basu (1977) via data

30

back-testing from 1956 to 1971. Basu‟s test focused on utilizing the P/E ratio and

evaluated the risk-adjusted returns of value stocks against growth stocks (Basu,

1977). Basu (1977) showed that even when risk-adjustments have been performed on

the portfolio of value stocks, the portfolio of value stocks outperformed both the

market and growth stocks significantly, which indicated some sort of market

inefficiency in the NYSE.

Lakonishok, Shleifer and Vishny (1994) drew similar conclusion on the topic of

value investing. In their research with the time span between 1968 until 1994, they

have formed portfolios which consisted of value stocks and growth stocks. These

stocks were characterized by price-to-book, price-to-cash flow and price-to-earnings

ratios. Additionally, they also use sales growth as their independent variables in the

study. In their conclusion, they concluded that, though a myriad of definition used to

define value stocks and growth stocks, value stocks have consistently without fail

outperformed growth stocks with significant margins.

Dhatt, Kim and Mukerji (1999) found similar findings couple of years later when

studying small-cap stocks and the value versus growth effect in returns. In their study

value stocks outperformed growth stocks whether categorization was defined by P/E,

P/S or M/B. Price-to-sales seemed to be the best indicator out of these three but

results were even better if all three ratios were used in selecting the portfolio.

In a follow-up study, Dhatt, Kim and Mukherji (2004) suggested that using more than

one ratio may result in even better returns. In their study, Dhatt et al. (2004)

31

documented that best results regarding both return and risk were achieved when the

portfolio was formed based on price-to-sales, price-to-book and PER rather than just

one of these ratios. Dhatt et al.‟s (2004) study confirmed an earlier argument raised

by Oppenheimer (1984) that a combination of criteria would grant the highest return.

Outside the US capital market, there are large evidence that support the existence of

value premium as evidenced by Capaul et al. (1993) via their study on France,

Germany, Switzerland, United Kingdom and Japan. They concluded that value stock

(categorized as low P/B) did better than those from growth stocks (high P/B). This is

in line with Chan et al.‟s (1991) earlier study in Japan which indicated that a value

premium existed in the Japanese stock market. This is further supported by Haugen &

Baker (1996) as they reached a similar conclusion even after taking into consideration

the risk, liquidity, growth rate and historical prices. Bauman et al. (1998) extended

Capaul et al. (1993) study by including stocks from the MSCI-EAFE and Canada.

Later, several studies have been conducted on international markets as well. Fama

and French (1998; 2012), Kargin (2002), Bird and Whitaker (2003), Chan and

Lakonishok (2004) and are among the researchers who verified superior returns of

value over growth stock held around the world after researching some of the biggest

global stock markets. Bauman et al. (1998) tested the value premium using both the

P/E and the P/B indicator, while Fama and French (Fama & French, 1998; 2012)

tested only for the P/B indicator. Chan and Lakonishok (2004) however, used a

combination of price-to-sales, price-to-book and PER, yet argued that the P/B

indicator provided the best returns out of the other.

32

Most interestingly, majority of researches on the value premium did not implement

all 10 of Benjamin Graham‟s value stock selection criteria, as stated in section 2.1.3.

Oppenheimer was the first one to test Graham‟s value stock criteria in his paper “A

Test of Ben Graham‟s Stock Selection Criteria” (1984). According to Rea (1977),

Graham‟s 10 criteria measures two distinct indicators. The first 5 criteria measures

the reward associated with investing with the stock and the later 5 measures the risk

associated with investing with the stock (Rea, 1977). The 10 criteria, as written in

section 2.1.3, is as follows (Graham & Dodd, 1934; Blustein, 1977):






5. Stock price below two-thirds "net current asset value".



8. Total debt less than twice "net current asset value".


rate.


or more in year-end earnings in the prior 10 years are permissible.

33

Criteria 1: The firm‟s earnings to price (E/P) yield should be at least twice the AAA

bond yield:

The earnings yield is the inverse of the P/E (price-to-earnings) ratio. Graham and

Dodd (1934) were of the opinion that firms who had been experiencing high earnings

growth are unlikely to able to sustain its growth to the extent which was expected by

the market. When the actual growth rate was not as expected, it resulted in the

revision of the firm‟s earnings‟ expectations, a fall in firm‟s P/E ratio due to a

downward correction in its stock price (Bird & Gerlach, 2003). Therefore, Graham

and Dodd suggested investors to concentrate on stocks whose market prices are

depressed while depicting excellent value at the same time. Due to the Great

Depression of 1929, Graham and Dodd (1934) strongly believed that the stocks are

far riskier bonds and thus recommended that a firm‟s earnings yield should have at

least double the yield on AAA bonds to protect the investors against unexpected loss.

Singh and Kaur (2014), however, argued that due to the 2007-2008 financial crisis in

which several corporations with AAA ratings were downgraded to junk bond ratings,

it is more advisable that investors rely on the yield on government security as the

benchmark for the comparison of a firm‟s earnings yield.

Criteria 2: The firm‟s current P/E ratio should be less than 40 per cent of the highest

P/E ratio the stock had over the past five years:

Graham and Dodd (1934) believed that the stock selection should not be based solely

on the current P/E performance of the company but investors should reflect upon the

34

historical accounting figures of the firm as well. Singh and Kaur (2014) were of the

opinion that 5 years of historical P/E data tend to absorb and even out the distorting

influences of the business cycle and create a more objective P/E benchmark for

investors.

Criteria 3: The firm‟s dividend yield should at least be at two-thirds the AAA bond

yield:

Graham and Dodd (1934) deeply believed that bonds are safer than stocks, and that

the investor should be rewarded with a larger total return when investing in stocks.

Graham and Dodd (1934) argued that firms which offer high dividend yields not only

reward investors with greater returns compared to investing in bonds but that firms

which have high dividend yields are more resistant to decline in price than firms

which have lower dividend yields. Singh and Kaur (2014), however, discovered

evidence from Indian stock market that firms with high dividend yields do not

necessarily offer greater returns compared to the market returns. Additionally, as

explained previously, Singh and Kaur (2014) recommended that investors rely on the

yield on government security as the benchmark for the comparison of a firm‟s

dividends yield.

Criteria 4: The market price of the firm should be less than two-thirds of the tangible

book value per share (BVPS):

As evidenced by Chan et al (1991) and Fama and French (1992), the price-to-book

value ratio is the most important determinant in determining whether a stock is

35

overvalued or undervalued in the market. Book value per share is an accounting

concept that indicates should the company decide to dissolve (liquidated), the book

value per common share is the dollar value distributed to common shareholders after

all assets are liquidated and all debtors are paid (1937). This is calculated by

deducting total debt and preferred stock from total tangible assets (Graham, 1937).

The concept of the margin of safety introduced by Graham and Dodd (1934) centered

upon the idea that a stock should only be purchased when it is selling at a

considerable discount to its intrinsic value. Graham and Dodd (1934) believed that

the book value per share could be used as an indicator of a firm‟s intrinsic value.

Thus, when a stock is selling at a high discount to its book value per share, thus

having a large margin of safety, the investor may decide to invest in this particular

stock.

Criteria 5: The market price of the stock should be less than two-thirds of the net

current asset value per share:

Graham's tried-and-tested formula for bargain stocks, however, is the Net Current

Asset Value (NCAV) formula, which was perfected during his tenure as a fund

manager in his investment firm, the Graham-Newman Corporation. Graham first

introduced the NCAV formula in “Interpretation of Financial Statements” (1937),

and it is as follows:

36

Graham wrote that he typically bought these stocks “at two-thirds (67%) or less of

such stripped-down asset value” during his tenure in the Graham-Newman

Corporation.

Evidence from researches conducted by researchers from the State University New

York, using data from the NYSE, and from Salford Business School from

Manchester, using data from the London Stock Exchange, indicated that stocks which

fulfilled the net-net criteria has successfully outperformed the market. Oppenheimer

examined the investment results of stocks selling at or below 66% of net current asset

value during the 13-year period from December 31, 1970 through December 31,

1983. Oppenheimer indicated that investment in the net-net stocks would generate an

average rate of return of 29.4% per year versus 11.5% per year for the NYSE-AMEX

Index (Oppenheimer, 1986). Xiao and Arnold found that based on a research period

from January 1980 to December 2005, the NCAV portfolio of stocks netted a total

return of 254%, compared to the market index which only produced a total return of

137% (Xiao & Arnold, 2008).

Criteria 6: Total debt of the company should be less than the book value:

This principle measures the financial strength of the company in case the company is

liquidated at any moment‟s notice. Damodaran (2002) was of the opinion that when

the book value is higher than the total debt, then stockholders would still be able to

get compensation from the company in case of liquidation. Thus, this was the

37

explanation as to why Graham and Dodd (1934) looked for stocks which has a total

debt of less than its book value.

Criteria 7: Current ratio of the company should be greater than 2.0:

The current ratio is a ratio which measures the short-term liquidity of a company.

Optimally, current assets should and must be able to fulfill the firm‟s short-term

obligations to its creditors or the company would be in a state of short-term

illiquidity. Typically, current assets are assets which are held for a maximum period

of one year while current liabilities are short-term obligations which have a maximum

period of one year. Every stakeholder of the firm has interest in the liquidity position

of the firm, particularly it short-term liquidity. For example, suppliers for the firm

will check the firm‟s liquidity before agreeing to selling goods on credit. Employees

are also concerned about the company‟s liquidity to know whether the firm can afford

to pay their salaries on. Thus, a company needs to maintain adequate liquidity to meet

its obligations to its stakeholders. Graham and Dodd (1934) believed that a firm‟s

current assets should be at least double its current liabilities in order to have a

sizeable cushion of working capital that should sustain the firm‟s daily operations.

Criteria 8: The firm‟s total debt should be less than twice the net current asset value:

This principle stresses that the net current assets of the company should be sufficient

enough to provide complete coat to the total debt of the company. The basic premise

is that if the company decided to liquidate, it could use its most liquid assets to pay

38

off all debt holders and actually have money left at the end for shareholders to net a

gain (Graham & Dodd, 1934) .

Criteria 9: Earnings growth for the last 10 years should be at least at a 7 percent

annual compounded rate:

At first glance, this principle seems to against the concept of value stocks and favors

the purchase of stocks having high earnings growth (growth stocks). However, this is

a misconception. Graham did not mean to use the historical earnings growth as a

means to predict strong future growth but rather as an indication of financial stability

(Reese, 2009). By having a consistent earnings growth in the past, it is a sign that the

firm has been a consistent, solid performer (Graham & Dodd, 1934).

Criteria 10: Stability of growth of earnings in that no more than two declines of 5 per

cent or more in year-end earnings in the prior 10 years are permissible:

As explained previously, Graham and Dodd (1934) preferred firms which have been

consistent performers in the past. Graham and Dodd (1934) argued that in times of

economic uncertainties firms may suffer from macroeconomics conditions, leading to

a drop in earnings. However, they contended that financially sound firms are those

who would not have great declines in their earnings (Graham & Dodd, 1934).

Damodaran (2002) was of the same opinion by stating that firms with stable earnings

are safer investments since these firms are less likely to surprise investors with great

fluctuation in earnings. For criteria 9 and 10, Lakonishok et al. (1994) and and Singh

and Kaur (2014) provided an alternative for investors to look for firm‟s year-end

39

earnings at minimum 5 years prior to date in case the past 10 years‟ data are not

available.

Rea suggested that for a stock to be able to be classified as a value stock, it had to

fulfill at least 1 reward criteria and 1 risk criteria (Rea, 1977). Oppenheimer (1984)

randomized the stock selection criteria in his research, which was based on data

available on the NYSE, albeit still obeying the 1 reward-1 risk criteria rule, and found

out that even though selected on a random basis, a value stock which fulfills the 1

reward-1 risk criteria rule still beat the market returns. Oppenheimer‟s study used the

following combinations of criteria in his test: (1) criteria 1 and 6, (2) criteria 3 and 6,

(3) criteria 1, 3 and 6, (4) criteria 1, 6 and 9, and (5) criteria 1, 3, 6 and 9.

Oppenheimer (1984) discovered that portfolio consisting of stocks fulfilling the first

combination (criteria 1 and 6) provided the largest return compared to the other

portfolios which used other combinations. However, it should be noted that while

portfolios which used other combinations did not beat portfolio screened according to

criteria 1 and 6, they still beat the market returns significantly, indicating the efficacy

of Graham‟s screening method (Oppenheimer, 1984). Oppenheimer (1984) added that

adding additional criteria to screen stocks may or may not lead to additional excess

returns.

Further studies of Graham‟s selection criteria were conducted by Klerck and Maritz

(1997) in South Africa, Chang in Malaysia (Chang, Testing Some of Benjamin

Graham's Stock Selection Criteria: A Case of the FTSE Bursa Malaysia EMAS Index

from Year 2000 to 2009, 2011) and Singh and Kaur in India (2014). Klerck and

40

Maritz' (1997) study did not apply all the criteria simultaneously, similar to the earlier

study conducted by Oppenheimer (1984). Klerck and Maritz‟ (1997) argument for not

applying all the criteria simultaneously was that if all the criteria were applied

simultaneously, then no stocks will qualify to be put in the portfolio. Singh and

Kaur‟s (2014) study in the Indian market confirmed this argument. According to

Singh and Kaur (2014), the maximum criteria which a stock could fulfill were 9

criteria only. Klerck and Maritz (1997), however, did not use as many combinations

as Oppenheimer (1984). Klerck and Maritz‟ (1997) study only involved the following

combination: 1) criteria 1 and 6, 2) criteria 3 and 6, 3) criteria 1, 3 and 6. Klerck and

Maritz (1997) discovered than in the Johannesburg Stock Exchange, combination 3

(criteria 1, 3 and 6) yielded the best return compared to the other combinations, even

after risk-adjustment is taken into factor. Yet, similar to Oppenheimer‟s study (1984),

Klerck and Maritz (1997) found that portfolio created according to the other

combination significantly beat market returns as well. Singh and Kaur (2014)

extended Oppenheimer‟s study and used a total of 25 combinations of Graham‟s

criteria in the Indian stock market. Singh and Kaur (2014) discovered that after risk-

adjusting the returns of the portfolios, the market adjusted return has not been

significant at 5 per cent level of significance till the stocks fulfill at least 4 Graham

criteria. Thus, when the stocks fulfill 5 or more criteria, their mean market-adjusted

returns become significantly positive at 1 per cent level of significance. Ergo, the

stocks in the Indian stock market must follow at least 5 rules of Graham in order to

expect them to outperform the market. This is a deviation from earlier studies

conducted by Oppenheimer (1984) and Klerck and Maritz (1997). However, Singh

41

and Kaur (2014) argued that investments in different markets yield different returns,

thus more researches are needed to confirm the efficacy of Graham‟s stock selection

criteria.

The following table summarizes the various studies on value investing and the

screening method or indicators the researchers used to categorize value stocks.

Table 1.1 - Summary of Prior Researches

Author Year Research Area

(Geographic)

Research

Period

Indicators

Used

Results and Conclusion

Basu 1977 USA

1957-

1971

P/E

Risk-adjusted returns of

stocks with low P/E

ratio significantly beat

risk-adjusted returns of

stocks with high P/E

ratio. Market

inefficiency present in

US market from 1957-

1971.

Oppenheimer 1984 USA

1971-

1984

Combinations

of Ben

Graham‟s 10

stock selection

criteria

Stocks with earnings to

price ratio at least twice

the AAA bond yield and

total debt less than book

value yielded the

highest return compared

42

to other combinations.

Stocks screened

according to Ben

Graham‟s criteria

significantly beat

market returns. More

combinations may or

may not give higher

returns.

Chan, Hamao,

Lakonishok

1991 Japan

1971-

1988

B/M , P/E ,

P/CF

Value stocks outperform

growth stocks, but the

B/M and price-to-cash

flow ratios are stronger

indicators than the P/E

indicator.

Fama, French 1992 USA

1962–

1989

B/M, E/P, D/E

B/M ratio is a superior

indicator of stock

returns. Evidence of

market inefficiency

confirmed. Firm size

has an effect on stock

returns.

Capaul, Rowley,

Sharpe

1993

France,

Germany,

Switzerland, UK,

1981-

1992

P/B

Value stocks provided

superior risk-adjusted

performance in each of

43

Japan, USA the researched countries.

However, it is not clear

what causes the

outperformance.

Lakonishok,

Shleifer, Vishny

1994 USA

1963-

1990

P/E, B/M,

P/CF,

projected sales

growth rate

Returns of value stocks

significantly beat

returns of growth

stocks. Stocks with

excessive projected

sales growth rate

generate low returns.

Klerck, Maritz 1997 South Africa

1977-

1995

Combinations

of Ben

Graham‟s 10

stock selection

criteria

Stocks screened

according to Ben

Graham‟s criteria

significantly beat

market returns. Stocks

with earnings to price

ratio at least twice the

AAA bond yield, has a

dividend yield of at least

two-thirds the AAA

bond yield and total

debt less than book

value provided the

highest returns.

44

Chen, Zhang 1998

US, Japan, Hong

Kong, Malaysia,

Taiwan and

Thailand

1970-

1993

B/M ,

Dividend

Yield , Firm

Size

Strong value stock

effects persist in the

U.S, but Japan, Hong

and Malaysia markets

show less value

investing advantage. In

Taiwan and Thailand

the benefits of value

investing are

undetectable.

Bauman,

Conover, Miller

1998

Australia,

Austria, Belgium,

Canada,

Denmark,

Finland, France,

Germany, Hong

Kong, Italy,

Japan, Malaysia,

Netherlands,

Norway,

Singapore, Spain,

Sweden,

Switzerland, UK

1985-

1996

B/M , P/E ,

P/CF ,

Dividend yield

Value stocks generally

outperform growth

stocks, but in some

years value stocks

underperformed.

Fama, French 1998

US, Japan, UK,

France,

Germany, Italy,

1974-

1994

B/M , P/E ,

P/CF ,

Value stocks tend to

have higher returns than

growth stocks in

45

Netherlands,

Belgium,

Switzerland,

Sweden,

Australia, Hong

Kong, Singapore

Dividend yield markets around the

world for each of the

indicators.

Dhatt, Kim,

Mukerji

1999 USA

1979-

1997

P/E, P/S, B/M

Value stocks in the

study outperformed

growth stocks, had

lower standard

deviations and lower

coefficients of variation

than growth stocks did.

Kargin 2002

Argentina,

Brazil, Chile,

Colombia,

Mexico, Peru,

Venezuela, India,

Sri-Lanka,

Indonesia, Korea,

Malaysia,

Pakistan,

Philippines,

Taiwan,

Thailand, China,

1976-

2000

P/E, B/M

The portfolio of value

stocks in all the

emerging markets tested

generated superior

returns compared to

market returns.

46

Greece, Turkey,

Hungary, Poland,

Czech Republic,

Romania, Russia,

Slovakia, Israel,

Egypt, Morocco,

South Africa,

Zimbabwe

Bird, Whitaker 2003

France,

Germany, Italy,

The Netherlands,

Spain,

Switzerland, UK

1990-

2002

B/M, Dividend

yield, E/P,

Sales-to-price,

price

momentum

Stocks with low B/M

and high sales-to-price

ratio performed well and

generated added value

when applied over

holding periods of up to

36 months using

momentum strategy.

Applying multiple

criteria to form

portfolios can result in

even better

performance.

Dhatt, Kim and

Mukherji

2004 USA

1980-

1999

MVE, P/E,

P/S, M/B,

P/CF

No size effect can be

attributed to returns.

Value stocks beat

growth stocks, with

higher returns and lower

47

risk. Price-to-sales ratio

provides the highest

excess returns, while

low price-to-cash flow

offers the lowest risk

and best risk-return

trade-off.

Yen, Sun, Yan 2004 Singapore

1975-

1997

B/M , P/E ,

P/CF

Value stocks outperform

growth stocks based on

each of these indicators.

Athanassakos 2009 Canada

1985-

2005

P/E , P/B

Value investing strategy

beats Growth investing

strategy. Forming

portfolios based on the

value investing

approach can help

investors to achieve

superior long-term

performance.

Chang 2009 Malaysia

2000-

2009

Combinations

of Ben

Graham‟s 10

stock selection

criteria

Stock with price-to-

earnings ratio of not

greater than 15, a price-

to-book value of not

greater than 1 and a

dividend yield of at least

48

the risk-free rate

generated the highest

returns in the Malaysian

market. Only counting

on low price-to-book

value does not guarantee

success.

Sareewiwatthana 2011 Thailand

1996-

2010

P/B , P/E ,

Dividend

Yield

The value portfolios

significantly

outperformed growth

portfolios on the

Thailand stock market.

Fama, French 2012

North America,

Europa, Japan,

Asia Pacific (23

countries, not

specifically

mentioned one by

one)

1989-

2011

B/M , Size

Value premiums were

found in each of the four

regions. When taking

size into account, the

value premium is larger

for small stocks in all

countries except Japan.

Singh, Kaur 2014 India

1996-

2010

Combinations

of Ben

Graham‟s 10

stock selection

criteria

Only stocks which

fulfill 4 or more criteria

generate excess returns.

49

As evidenced by previous researches described previously, they have been no

documented researches regarding Benjamin Graham‟s stock selection criteria in

Indonesia. Yet, significant value premium is present and the semi-strong form of

market efficiency has been confirmed by several researchers to exist in Indonesia.

Thus, this study aims to test the efficacy of Benjamin Graham‟s stock selection

criteria in the Indonesian stock exchange.

2.2 Theoretical Framework

This research is adapted from Singh and Kaur‟s (2014) test of Benjamin Graham‟s 10

stock selection criteria performed in the Indian stock market, which by itself is a

modification of earlier tests conducted by Oppenheimer (1984) in the New York

Stock Exchange, and by Klerck and Maritz (1997) in the Johannesburg Stock

Exchange. All three researches tested the following stock selection criteria (Graham

& Dodd, 1934; Blustein, 1977):






5. Stock price below two-thirds "net current asset value".



8. Total debt less than twice "net current asset value".

50


rate.


or more in year-end earnings in the prior 10 years.

As explained previously, Oppenheimer‟s (1984) study only involved 5 combinations,

they are: (1) criteria 1 and 6, (2) criteria 3 and 6, (3) criteria 1, 3 and 6, (4) criteria 1,

6 and 9, and (5) criteria 1, 3, 6 and 9. Klerck and Maritz (1997) narrowed down their

criteria combinations for their test. Klerck and Maritz‟ (1997) combinations were: (1)

criteria 1 and 6, (2) criteria 3 and 6, (3) criteria 1, 3 and 6.

Singh and Kaur (2014), however, extended the test into a whopping total of 25

combinations (presented in the table below). Singh and Kaur‟s (2014) combinations

are as follows:

Table 2.2 – Proposed Risk-Reward Combinations

Risk R

ewa

rd

Criteria 6 (C6) Criteria 7 (C7) Criteria 8 (C8) Criteria 9 (C9) Criteria 10 (C10)

Criteria 1 (C1) C1 - C6 (H1) C1 - C7 (H2) C1 - C8 (H3) C1 - C9 (H4) C1 - C10 (H5)



51



Additionally, Singh and Kaur (2014) also measured the risk-adjusted returns of stocks

fulfilling no criteria at all, those fulfilling 1 criterion only, those fulfilling 2 criteria,

to those fulfilling 10 criteria all at once. Singh and Kaur (2014) used the following

procedure to perform the described test: If a stock meets one particular criterion, it is

given score 1 and otherwise 0 and then the scores of all the criteria which that stock

meets are totaled to calculate the composite score. For instance, if a stock meets only

three of the ten proposed criteria, then it is given a composite score of 3 out of 10. If

the stock fulfills all of the ten proposed criteria, it is given a composite score of 10.

Hence, the composite score is the sum of individual binary signals.

Singh and Kaur (2014) also modified criteria 1 and 3. In their researches, the earnings

yield and the dividends yield of the firm are compared with the rate of return on

government securities, arguing that due to the 2007-2008 financial crisis in which

several corporations with AAA ratings were downgraded to junk bond ratings, it is

more advisable that investors rely on the yield on government security as the

benchmark for the comparison of a firm‟s earnings yield. This research would follow

the same argument as Singh and Kaur‟s study (2014). Since this research‟s scope

involves investing in stocks in a 10-year time period, the yield of long-term

government bond, specifically the yield on 10-year Indonesian Government Bond

52

from 2005 to 2015 is taken as the risk-free rate. The 10-year Indonesian Government

Bond from 2005 to 2015 is given as 9.5% (Bank Indonesia, 2015). Thus, criteria 1

and 3 for this study become:

1. An earnings-to-price yield at least twice the 10-year Indonesian Government Bond

yield.

3. A dividend yield of at least two-thirds of the 10-year Indonesian Government Bond

yield.

Lakonishok et al. (1994) added that in case the past 10-years‟ financial data

availability are missing, the investor could use the past 5-years financial data as a

minimum requirement for fundamental analysis of the firm‟s historical earnings

growth. Thus, criteria 9 and 10 for this study become:

9. An earnings growth of prior 5 years at least at a 7 percent annual (compound) rate.

10. A stability of growth of earnings in that no more than two declines of 5 per cent

or more in year-end earnings in the prior 5 years.

For computation of total returns given by a stock, both the capital gains and the

dividends are added to give the total return. Thus, the total return given by a stock is

calculated using the following formula (Bodie, Kane, Marcus, & Jain, 2014):

(

) (

)

Where,

53

= Monthly rate of return for share .

= Adjusted closing price of share at the end of month .

= Adjusted closing price of share at the end of month .

= Cash dividend received from share during month , taken from ex-

dividend date.

Portfolios for this research are equal-weighted. There has been empirical evidence

that equal-weighted portfolios are superior compared to value-weighted and price-

weighted portfolios (Plyakha, Uppal, & Vilkov, 2014). Therefore, the formula for

calculating the return of the portfolios is given as:

∑

Where,

= Monthly rate of return of portfolio .

= Weight of share in portfolio .

Since the holding period for this research is determined at 24-months, the holding

period yield for 24-months is given by the following formula (Bodie, Kane, Marcus,

& Jain, 2014):

( ∑

)

Where,

54

= The annualized holding period yield of portfolio .

Since the annualized holding period yield of portfolio is equal to the annualized rate

of return of the portfolio, therefore .

The general formula for variance of a portfolio with two risky assets is given as

(Bodie, Kane, Marcus, & Jain, 2014):

∑∑

Where,

= The variance of portfolio .

= Weight of share in portfolio .

= Covariance between assets and

On an equally weighted portfolio, = 1/n for each security. Thus the equation

above may be re-written as follows (Bodie, Kane, Marcus, & Jain, 2014):

∑

∑∑

Where,

= the variance on asset .

55

The average variance of all assets in the portfolio and the average covariance of all

securities in portfolio can be defined as (Bodie, Kane, Marcus, & Jain, 2014):

∑

∑∑

( )

Where,

= The average variance of all assets in portfolio .

= the average covariance of all the assets portfolio .

Thus, the portfolio variance can be re-expressed as the following formula (Bodie,

Kane, Marcus, & Jain, 2014):

The beta of each individual stock in the portfolio is calculated using the following

formula (Bodie, Kane, Marcus, & Jain, 2014):

Where,

= Beta for share .

56

= Monthly rate of return for share .

= Monthly market rate of return (taken from adjusted closing price of the

market each month).

Thus the systematic risk (beta) of the portfolio is expressed as (Bodie, Kane, Marcus,

& Jain, 2014):

∑ ( )

∑

If any stock which has been a part of the portfolio lacks further information regarding

its closing price, then the last known closing price is used to calculate the return. If

any stock gets delisted during the holding period, that stock is still included in the

study in order to avoid the survivorship bias and is assigned the return of -100%

(minus 100%), if no information regarding the amount received on delisting is

available.

To calculate the monthly return of the market portfolio, the following formula is

used:

(

)

Where,

57

= Monthly rate of return for market portfolio .

= Adjusted closing price of market portfolio at the end of month .

= Adjusted closing price of market portfolio at the end of month

.

Since the holding period for this research is determined 24-months, the holding

period yield for the market portfolio is also determined at 24-months. Thus, the

holding period yield of the portfolio is given by the following formula:

( ∑

)

Where,

= The annualized holding period yield of market portfolio .

Since the annualized holding period yield of market portfolio is equal to the

annualized rate of return of the market portfolio, therefore .

To examine the significance of the risk-adjusted return of the stocks meeting the

criteria, the one sample T-test and linear regression is employed in this study. The

null hypothesis to study the significance of risk-adjusted returns is:

To analyze the risk-adjusted performance of the portfolios, the Treynor‟s measure,

Sharpe‟s ratio and Jensen‟s alpha are used.

58

The Treynor‟s measure is used to calculate the excess return of the portfolio per unit

of risk using systematic risk (beta) as its main risk measurement. The formula for

Treynor‟s measure is given as (Bodie, Kane, Marcus, & Jain, 2014):

Where,

= The annualized rate of return of portfolio p.

= The annual risk-free rate.

The Sharpe‟s measure is used to calculate the excess return of the portfolio per unit of

risk using the standard deviation of the portfolio as its main risk measurement. The

formula for Sharpe‟s measure is given as (Bodie, Kane, Marcus, & Jain, 2014):

Where,

= The annualized standard deviation of returns of portfolio .

The Jensen‟s alpha is used to calculate the average return on the portfolio over and

above that is predicted by the CAPM, given the portfolio‟s beta and the market

return. Thus, the Jensen‟s alpha is calculated by the following formula (Bodie, Kane,

Marcus, & Jain, 2014):

[ ( ) ]

59

Where,

= The Jensen‟s measure, or Jensen‟s alpha of portfolio .

= The rate of return of portfolio for month .

= The monthly rate of return of the risk-free asset.

= The rate of return of market portfolio for month .

= Error term.

For this study, the risk-free rate is assumed to be equal to the yield on 10-year

Indonesian Government Bond from 2005 to 2015. The 10-year Indonesian

Government Bond from 2005 to 2015 is given as 9.50% (Bank Indonesia, 2015).

Therefore, the average risk-free rate of return for this study is determined at 9.50%.

Therefore, based on the reviewed researches, 26 hypotheses are proposed for this

research. These 26 hypotheses are:

Hypothesis 1 (H1): Portfolio of stocks which have earnings-to-price yield twice the

long-term government bond yield AND have total debt less than its book value

(criteria C1 and C6), held with a 24-months holding period, generates significant

positive risk-adjusted returns compared to the market returns.


long-term government bond yield AND have current ratio greater than two (criteria

60

C1 and C7), held with a 24-months holding period, generates significant positive risk-

adjusted returns compared to the market returns.


long-term government bond yield AND have total debt less than twice its "net current

asset value" (criteria C1 and C8), held with a 24-months holding period, generates

significant positive risk-adjusted returns compared to the market returns.


long-term government bond yield AND have earnings growth of prior 5 years at least

at a 7 percent annual (compound) rate (criteria C1 and C9), held with a 24-months

holding period, generates significant positive risk-adjusted returns compared to the

market returns.


long-term government bond yield AND have stability of growth of earnings in that no

more than two declines of 5 per cent or more in year-end earnings in the prior 5 years



61

Hypothesis 6 (H6): Portfolio of stocks which have price-earnings ratio less than 40

per cent of the highest price-earnings ratio each stock had over the past five years

AND have total debt less than its book value generates (criteria C2 and C6), held with

a 24-months holding period, significant positive risk-adjusted returns compared to the

market returns.



AND current ratio greater than two (criteria C2 and C7), held with a 24-months


market returns.



AND have total debt less than twice its "net current asset value" (criteria C2 and C8),

held with a 24-months holding period, generates significant positive risk-adjusted

returns compared to the market returns.



62

AND have earnings growth of prior 5 years at least at a 7 percent annual (compound)

rate (criteria C2 and C9), held with a 24-months holding period, generates significant




AND have stability of growth of earnings in that no more than two declines of 5 per

cent or more in year-end earnings in the prior 5 years (criteria C2 and C10), held with

a 24-months holding period, generates significant positive risk-adjusted returns

compared to the market returns.

Hypothesis 11 (H11): Portfolio of stocks which have dividend yields of at least two-

thirds long-term government bond yield AND have total debt less than its book value




thirds long-term government bond yield AND have current ratio greater than two



63


thirds long-term government bond yield AND have total debt less than twice its "net

current asset value" (criteria C3 and C8), held with a 24-months holding period,

generates significant positive risk-adjusted returns compared to the market returns.


thirds long-term government bond yield AND have earnings growth prior 5 years at

least at a 7 percent annual (compound) rate (criteria C3 and C9), held with a 24-

months holding period, generates significant positive risk-adjusted returns compared

to the market returns.


thirds long-term government bond yield AND have stability of growth of earnings in

that no more than two declines of 5 per cent or more in year-end earnings in the prior

5 years (criteria C3 and C10), held with a 24-months holding period, generates


Hypothesis 16 (H16): Portfolio of stocks whose stock prices are below two-thirds of

its tangible book value per share AND have total debt less than its book value

64

(criteria C4 and C6) generates, held with a 24-months holding period, significant



its tangible book value per share AND have current ratio greater than two (criteria C4

and C7) , held with a 24-months holding period, generates significant positive risk-



its tangible book value per share AND have total debt less than twice its "net current

asset value" (criteria C4 and C8) , held with a 24-months holding period, generates



its tangible book value per share AND have earnings growth of prior 5 years at least

at a 7 percent annual (compound) rate (criteria C4 and C9) , held with a 24-months


market returns.

65


its tangible book value per share AND have stability of growth of earnings in that no

more than two declines of 5 per cent or more in year-end earnings in the prior 5 years



Hypothesis 21 (H21): Portfolio of stocks whose stock price trades below two-thirds

its "net current asset value" AND have total debt less than its book value (criteria C5

and C6), held with a 24-months holding period, generates significant positive risk-



its "net current asset value" AND have current ratio greater than two (criteria C5 and

C7), held with a 24-months holding period, generates significant positive risk-



its "net current asset value" AND have total debt less than twice its "net current asset

value" (criteria C5 and C8), held with a 24-months holding period, generates


66


its "net current asset value" AND have earnings growth of prior 5 years at least at a 7

percent annual (compound) rate (criteria C5 and C9), held with a 24-months holding

period, generates significant positive risk-adjusted returns compared to the market

returns.


its "net current asset value" AND have stability of growth of earnings in that no more

than two declines of 5 per cent or more in year-end earnings in the prior 5 years



Hypothesis 26 (H26): Portfolio of stocks which fulfill more than 3 criteria proposed

by Benjamin Graham, held with a 24-months holding period, generates significant


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