AN APPLICATION OF FAMA FRENCH 3 FACTORS MODEL ON HOSE

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1. INTRODUCTION
1.1. Overview of Research
The Viet Nam stock market has been formed since July, 1998. Having some certain
achievements, there is still riskiness and weakness. Currently, in Viet Nam, most of investment
decisions of individual investor and business were based on recommendations of securities
companies which using discounted cash flow method or relative method. However, with the
current volatility of the market, these methods proved to be inefficient and could not predict the
real market price of stock. So that investors could not make decisions in a more flexible way.
Therefore, the study of the application of modern financial theory of investment in Viet Nam
stock market in the current period is very important and urgent. Moreover, there were several
researches in the world in applying of the theory of financial investment in the stock market,
especially the empirical research on the stock market in emerging countries. These researches
gave significant and extremely practical results. It reinforces more accuracy and empirical
models. Seeing the need of the application of the asset pricing model to predict the stock
market, I decided to investigate research topic: AN APLICATION OF FAMA FRENCH 3
FACTORS MODEL ON HOSE (HO CHI MINH CITY STOCK EXCHANGE).
The Fama French 3 factors model has been one of the most widely used techniques in the global
investing community for calculating the required return of risky asset but it seems very new in
Viet Nam. This is the reason that motivates me empirically analyse the Fama French model in
the context of Viet Nam stock market.
This project aims to test whether Fama French model is a valid model for estimating risk and
return of stocks listed on the HOSE.
The study’s result might be useful recommendations for investor about how and what Fama
French can be used for predicting risk and return and making investment decision in Viet Nam
stock market.
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1.2. Research Objectives
With the motivation of specifying appropriate measurement for investors to evaluate risk and
return, this study’s main objective is to evaluate the explanatory power of this model in Viet
Nam stock market. While most empirical tests of this models’ validity conducted with HOSE
data. Therefore, this study will re-examine the forecasting capability of Fama French 3 factors
model for 120 companies listed on HOSE which is listed from July 2008 to June 2013. The
study will examine the effect of market premium, size premium and book value/market value
premium to the stock and portfolio. It is expected to estimating risk and return of stock and
portfolio during the period of down-turn to find whether this model is suitable to evaluate cost
of equity in Viet Nam stock market or not?
1.3. Research Structure
I intend to organize my study as follows: section 1 introduces the thesis topic, section 2 reviews
the literature on Fama French model. Section 3 describes the data of 120 companies in HOSE
(excluding banks, insurance companies, securities firms), which is employed to test the
predicting capability of explanatory variables. Section 4 discusses about the methodology which
is used to conduct the testing. Section 5 reports and analyses the empirical test results. In final
section, summary and limitations of the study are provided.
1.4. Previous Research in Viet Nam
In Vietnam, there are few previous studies on capital asset pricing model, as Vietnam stock
market has been formed 15 years ago. So I will summarize some typical researches on
CAPM and Fama French in Viet Nam.
In 2009, Doctor Quach Manh Hao, a lecturer at the National Economics University, has a
research “Finding an asset pricing model in Viet Nam” posted in Financial Market
imagine. He collected 50 stocks on HOSE and HASTC from 2007 to 2009. He found
that there were 4 most important factors that effect to stock return: Rm, P/E, liquidity
and company size. He applied Fama French model and found the result: R2: 55% and
confidence level: 95%.
In 2010, Vuong Minh Giang has a research ''An Empirical Examination of the Validity of the
CAPM in the Vietnamese Stock Market” at Social Science Research Network. The author
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collected data from 30 largest capitalization stocks in the period 2007-2009. The linear relation
between risk and rate of return in Vietnam stock market is tested for the falling period 2007-
2009 based on adjusted-price data. As a result, the failure of the test shows that VN-Index,
conventionally regarded as the "market portfolio", is not mean-variance efficient to the asset set
being examined. By optimization calculation, a general envelope containing the efficient
frontier of the stock set in the Vietnamese case of short-sales restrictions is produced. Finally,
some remarks are noted for stock pricing practice in the emerging market.
In 2011, a group of students in Ho Chi Minh Economics University conduct a research, namely
“Application CAPM and Fama French Model in Viet Nam stock market”. The authors collected
data from 88 stocks from 2007 to 2010 and reach a result as follow: R
2
is 90.56% and
confidence level is 95%
In my study, I would like to confirm that I cannot create a new model but basing on the result of
previous researches in Viet Nam and Fama French methodology, I want to conduct a intensive
study with more observation in a longer period, through a bigger portfolio (120 tickers) to find
out more useful results.
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2. LITERATURE REVIEW
2.1. The Fama French 3 factors model
The capital asset pricing model is a set of predictions concerning equilibrium expected returns
on risky assets. Harry Markowitz laid down the foundation of model portfolio management in
1952. The CAPM was developed 12 years later in articles by William Sharp, John Lintner and
Jan Mossi. The time for this gestation indicates that the leap from Markowitz’s portfolio
selection model to the CAPM is not trivial.
CAPM tests the relation between market risk and asset return, assuming that investors are risk
averse and only mean and variance of their assets return are occupied. Thus, the portfolio is
chosen if it can minimize loss, given expected return or maximize return at a certain level of
risk.
The CAPM equation is as follows:
• Denote E(r
i
) and E(r
M
) as expected return of asset i and market portfolio, respectively.
• R
f
is the return of risk-free asset.
• is the relationship between excess expected return of asset i and that of market
portfolio as a fraction of total variance of market portfolio .
In Roll’s critique, he also points out a number of drawbacks of CAPM such as its implication of
market efficiency or formation of market portfolio which challenge the practical of the model.
Roll and Ross (1994) and Kandel and Stambaugh (1995) support for Roll’s view. They argue
that CAPM provides an adequate evaluation of beta only in an efficient market which is hardly
measured in the real world. Thus, it is uncertain about the predicting capability of CAPM.
Besides, numerous studies suggest other factors which might outweigh beta in estimating
companies’ return such as price to earnings (Basu, 1977), firm size (Banz, 1981 and
Reinganum, 1981). Findings of other significant variables in explaining rate of return imply the
need to incorporate additional factors to modify CAPM (Lawrence et al, 2007).
Fama and French (1992) confirm the insufficiency of CAPM by illustrating a weak relation
between market beta and average US return of the period 1963-1990 by cross-sectional test.
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They point out other variables, namely E/P, leverage, firm size (market equity) and book to
market equity (hereafter BE/ME) can explain the stock returns. Combining these variables, two
latter variables seem to absorb explanation power of the two former. In short, firm size (market
equity) and BE/ME could enhance CAPM’s efficiency in pricing stocks.
Fama French three factors model is presented as follow:
Where , , , are expected premiums, is a constant
term; , , are parameters in the regression.
The ex-post regression of Fama French three factors model is given by:
Where , are excess asset and market return, respectively.
Fama and French’s later study (1993) expands the previous one in three dimensions. First, they
involve US government and corporate bonds in the set of tested assets instead of stock only in
Fama and French (1992). Second, one more variable, term-structure, which plays an important
role in bonds’ value, is added. In their theory, bonds and stocks might be closely related as
markets are integrated. Hence, they try to test whether a crucial variable of bonds has
significant explanation power to stocks’ returns or not, and vice versa. Third, the methodology
used to examine assets’ returns is different. If bonds is included in the cross- sectional
regression, size and BE/ME variables have no relation to bonds. Therefore, Black, Jensen and
Scholes (1972)’s approach, a time series regression, is adopted while cross-sectional test in
Fama French (1992) might be unsuitable in this case. Portfolios are therefore formed on size
and book to market equity. It is suggested that size factor be equal to the monthly average return
of small companies minus that of big companies, denoted by SMB (small minus big). The other
explanatory variable, book to market is the difference between return of stocks with high book
to market ratio and that of stock with low book to market ratio, given by HML (high minus
low).
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Fama and French (1995, 1996a) continue to prove the outperformance of Fama-French three
factors model compared to CAPM, using time series regression. They point out the trend that
strong firms with high earnings usually have low book to market equity and negative slope on
HML. In contrast, weak firms with low earnings tend to have high book to market equity and
positive slope on HML.
2.2. Extended Fama-French 3 factors model- Carhart (1997) four factor model
Mark Carhart (1997) continues the momentum issue studied by Jegadesh and Titman (1993).
He constructs a risk factors relating to momentum effect (WML- Winner Minus Loser) and
makes a four factors model by adding momentum factor into the Fama French 3 factors model.
The momentum effect is the effect that past winner or loser continues performing well or
poorly. The WML factor is measured by the return of winner stocks portfolio minus the return
of loser stocks portfolio. As momentum strategy, the investors buy the stock with higher returns
as well as sell stocks with lower return over the previous 3 to 12 months could largely generate
returns in the stock market.
The theoretical model of 4 factors regression is as follow:
Where: WML is the return difference between past 1 year winner and loser portfolios, is
parameters in the regression and the others are the same in Fama French model.
From the result of Carhart’s study, we get the flowing suggestion. We should not invest in the
funds that their rate of returns always are negative. The funds that having the high return in the
year before will continuously generate the higher return in the year after, but it is not accurate in
a long term. Management cost, total net asset, investment cost effect directly and inversely to
fund’s return and take away the excess return of the fund having high return in the year before
in a long term.
Toward the result of Carhart (1997), Daniel et al.(1997) and Wermers (1997) find evidence that
four factor model of Carhart (1997) does well in investigating the strategies that drive the
persistence in mutual fund performance. Brave et al.(2000) reveals that the four factors model
of Carhart could explain the underperformance in return from a sample of IPO (initial public
offering) and SEO (seasoned equity offering) companies.
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In fact, results from some research in the global indicated that the four factors model have
significant exposure in explaining the variations in average excess stock return. R
2
from Carhart
model is just slightly higher than Fama French model.
In Viet Nam, a group of students in Ho Chi Minh Economics University (2011) conduct a
research, namely “Application financial models in pricing investment portfolio in Viet Nam
stock market”. They find that four factor model of Carhart have higher capability in explaining
the variations in average excess portfolio return. R
2
in CAPM, Fama French and Carhart is
respectively 71.66%, 86.23%, 88.41%.
2.3. Empirical evidence of Fama French
2.3.1 Empirical of Fama French model in developed markets
Evidence of Fama French three factors model can be seen mostly in US market such as Fama
and French (1992, 1993, 1995, 1996a, 2006). In these studies, Fama and French show
statistically insignificant of the CAPM factor in estimating cost of equity during different
periods of time in the US market. Meanwhile, size and book to market factor are proved to have
a significant role in explaining asset return (ibid). In contrast, examining the validity of the two
models in the US, Bartholy and Pearre (2005) find out that there is no significant difference
between their estimates. Thus, they raise the issue that whether or not the other two variables of
Fama French should be adopted while they insignificantly contributed to cost of equity
evaluation.
According to Drew and Veeraraghavan (2003) and Artmann et al (2012), the efficiency of an
asset pricing model is strongly proved if its evidence is shown in sufficient new markets rather
than only in the biggest one. Using Australia data for the period 1981-1991, Halliwell et al
(1999) show the significance of Fama French factors in explaining Australian average returns.
But the role of BE/ME is not as powerful as it has been proved in Fama French’s studies. Faff
(2004) continues to research the validity of the Fama French model in Australia market, dealing
with daily data of industry portfolio from 1 May 1996 to 30 April 1999. His finding basically
supports Fama French factors. In the case that an estimated risk premium is considered,
robustness of the model is decreased. Expanding the examined time from 1982 to 2006,
Brailsford et al (2012) prove the superior performance of Fama French model in comparison
with CAPM in evaluating Australia stocks. Their empirical test results show the statistical
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significance of book to market factor and the opposite in case of size effect in explaining assets’
returns.
However, studying Germany firms’ data for the period from 1969-2002, Schrimpf et al (2007)
support the traditional asset pricing model, CAPM, rather than the modern one, Fama French in
the sense that it provides less error than its counterpart. Also dealing with German market but
for longer stage, 1960-2006, Artmann et al (2012) reject the explanatory power of both CAPM
and Fama French model. There is limited statistical evidence on both size and book to market
effect in German stock returns (ibid).
Chan and Chui (1996), when testing Fama French model for UK companies during the period
1971-1990, found out the significance of book to market factor but insignificance of firm size
factor in explaining stock returns. Meanwhile, Zhang (2009) and Chabi-Yo and Fourseni (2009)
demonstrate the strong capability in estimating UK stock returns of Fama-French factors.
Testing UK companies between 1975 and 2000, Hung et al (2003) combine Fama French
factors with the CAPM, then examine whether the additional factors support explanatory power
of CAPM beta or not. They conclude that both CAMP beta and Fama French factors are
significant in estimating the cross-section of UK stock returns. Similarly, using UK data (1992-
2001), Charitou and Constantinidi (2003) sum up with the outperformance of Fama French
model over the CAPM.
Various applications of the model result in conflicting conclusions about the explanation of
Fama French model. Kothari et al (1995) argue that book to market has a marginal relation to
expected stock returns. In their points of view, Fama and French (1992, 1993)’s results are due
to survivor bias in COMPUSTAT data. This data source usually includes historical firms’ data
in which survivor companies used to have unexpected higher return than the dead ones. Thus,
there might be a positive bias for the former (high book to market equity) in comparison to the
latter. On the contradictory, Chan et al (1995) and Fama and French (1996b) illustrate that
survivor bias plays no role in explaining the relation between average return and book to market
equity. In their arguments, omissions in COMPUSTAT data are financial institutions which
have higher leverage than other types of firms. The missing firms do not cause survivor bias.
Additionally, Fama and French (1993) use value-weighted portfolios in their test. Survivor bias
does not matter in these portfolios as high weights are given to large companies.
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Black (1993) and MacKinlay (1995) state that the value premium of Fama-French’s factors
stems from data-snooping, whereas Lakonishok et al (1994) suggest, it is due to behavior of
investors. As investors are used to buying stocks which are big and well-performed in the past,
thus, overprice these stocks. Conversely, they tend to sell stocks which might be small but have
high growth rate. Thus, these stocks are underpriced. La Porta (1996) shares the same idea that
investors might favor value stocks as they concentrate on these firms’ enduring development
rather than those with rapid growth rate. Fama and French (1992) suppose that investing in
growth stocks which are small and have high book to market ratios should reward higher
returns as bearing higher risks.
Extending Fama and French (1992)’s research, Knez and Ready (1997) also apply Fama and
Macbeth procedure in their study. They compare a standard least square regression with a least
trimmed square regression which monthly omits 1% most extreme observations. The results
suggest that these trimmed observations are reasons for negative relation between firm size and
its average return. Without them, coefficient of size in the regression for stocks’ return is
significantly positive. They share the same idea with Fama French (1992)’s explanation that
investors who bearing risk investing in small firms should reward high returns. It is suggested
that there should be systematic driven forces for risk premium which also help explaining firm’s
development process. Vassalow (2003) points out a significant factor in explaining stock return
that is news related to future GDP growth. His study illustrate that including the variable in
CAPM model dramatically increases its explanatory power. Moreover, the existence of news
related to future GDP growth might outweigh HML and SMB role in pricing equities. Thus,
there might be a close relation between HML, SMB variables and news about future GDP
development.
Initially stated by Banz (1981), size effect has been found in all common stocks listed on NYSE
from 1926 to 1975. In his study, small firms are proved to bear higher risk than large ones.
These former stocks provide significantly higher average returns than the latter ones. According
to Banz (1981), one possible explanation for the phenomenon is limited information of small
firms which leads to higher risk-adjusted returns for holding these stocks. Studying the same
topic, Chan and Chen (1991) examine risk and return characteristic of 19 industry groups on
NYSE during the period 1956-1985. In their findings, production efficiency, leverage and the
ability to absorb external capital cause higher risk of small firms than large ones. Zarowin
(1990) also concludes a size effect in stocks’ returns when re-examining the overreaction
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hypothesis stated by DeBondt and Thaler (1985). The hypothesis is about the outperformance of
loser to winner stocks. Following Zarowin (1990), losers tend to be small stocks and vice versa.
When winners are smaller than losers, they provide higher returns than the latter.
2.3.2.Empirical evidence of Fama French model in emerging markets
There is limited evidence on Fama French model’s validity in Asian countries. As Drew and
Veeraraghavan (2003) has observed, Chui and Wei (1998) are first examining the validity of
Fama French in Asian market. Their finding proves the significance of two more factors in
Fama French, firm size and book to market. In their study, the relation between market beta and
average return is weak, as opposed to that of Fama French factors. Similarly, Drew and
Veeraraghavan (2002, 2003) once comparing the validity of two models in emerging markets
(Malaysia, Hongkong, Korea and Philippines) during the 1990s, point out the outperformance
of Fama French over CAPM model. More recently, Taneja (2010) also confirms strong
explanatory power of Fama French in comparison with CAPM in Indian stock market, from
June 2004 to June 2009. His result shows that variations in stock average return can be captured
better by multifactor Fama French model than single factor model CAPM. Using Shanghai
Stock Exchange data, Lin et al (2012) illustrates the superior explanation power of Fama French
factors in estimating portfolios’ returns in China market. However, it is shown in their study
that market factor seems to be more appropriate in evaluating individual stock’s return.
3. DATA
Kothari, Shanken and Sloan (1995) report that beta from annual returns produce stronger
positive relation between beta and average return than beta from monthly return. But Fama-
French (1996) prove the contrast conclusion that annual and monthly returns have same
inferences about beta premium.
This study uses historical monthly data of 120 Viet Nam companies based on the following
criteria:
• Listed in HOSE at least 60 months (before July 31 2008). De-listed companies and
those which are listed after July 2008 are excluded to maintain the continuation of
portfolios.
• Banks, insurance companies, securities firms are excluded because of their distinctive
high leverage.
• Fiscal year ended on 31 December.
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