Application of fama french factors to industrial corporations in vietnam stock market bachelor thesis of banking and finance

MINISTRY OF EDUCATION AND TRAINING

STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY

BACHELOR THESIS
Major: Financial – Banking
Number : 7340201

Topic: APPLICATION OF FAMA FINANCIAL
MODEL TO INDUSTRIAL CORPORATIONS IN
VIETNAM
Student’s name

: Dương Đại Phát

Student’s ID

: 030631152010

Guiding teacher : Msc Nguy n Minh Nh t

HCMC, February 2021


MINISTRY OF EDUCATION AND TRAINING

STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY

BACHELOR THESIS
Major: Financial – Banking
Number : 52340201

Topic: APPLICATION OF FAMA FINANCIAL MODEL
TO INDUSTRIAL CORPORATIONS IN VIETNAM
Student’s name

: Dương Đại Phát

Student’s ID

: 030631152010

Guiding teacher : Msc Nguy n Minh Nh t

HCMC, February 2021


ABSTRACT
The study concentrates on one of the primary advantage pricing models which offer a
selection of selections for investors enthusiastic about evaluating returns. From 2014
to 2019, the writer selects the Fama Five-factor French style and uses every aspect to
calculate hundred listed manufacturing businesses in Vietnam. To be able to make sure
that the regression test is wholly explicable, the writer additionally determines
Gibbons et al. (1989) GRS F assay if all of the sorted portfolios will likely show
beneficial results in the study. The results show that the factor MRP (market
component) is actually a significant professional in all the portfolios and that SMBs
play a good role than many other threes. The time series average return of these

companies could be defined by Fama French Five-factor variables which don't
generate pricing errors.
Keywords: Fama French five-factor, asset pricing model; market capitalization; bookto-market equity; profitability; investment; trading businesses


DECLARATION OF AUTHENTICITY
I affirm that I wrote this and have provided credit for each quote. I certify that I have
completed all processes and methods faithfully and honestly. I mentioned to all of the
people who contributed significantly to this effort.
I would like to report that all representations and material found here are valid, right
and authentic.
Ho Chi Minh City, February 2021


ACKNOWLEGEMENTS
First of all, I'd like to express my appreciation to Mr. Nguyen Minh Nhat for providing
me with helpful advice and motivation during this project.
Secondly, I would like to thank my family and friends who have been there every step
of the way during my four years in Banking University.
Lastly, best wishes to my lecturers and BUH for their knowledge, encouragement, and
understanding.


COMMENTS FROM GUILDING TEACHER
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HCMC, .............................................2021
Signature of guiding teacher


Table of Contents
CHAPTER 1: INTRODUCTION ........................................................................................................ 8
1.1 Reason to research ................................................................................................................. 8
1.2 Research objective .................................................................................................................. 9
1.3 Research questions ................................................................................................................. 9
1.4 Research subject and range ................................................................................................ 10
1.5

Methodlogy .......................................................................................................................... 10

1.6 Research contribution ......................................................................................................... 11
1.7

Research outline .................................................................................................................. 11

CHAPTER 2: LITERATURE REVIEW AND PREVIOUS RESEARCHES ............................... 13
2.1 Literature review ................................................................................................................. 13
2.1.1

Arbitrage Pricing Theory (APT) ................................................................................ 13

2.1.2

The Fama French three-factor model ....................................................................... 14

2.1.3

Carhart four factor model .......................................................................................... 16

2.1.4

The Fama French five factor model .......................................................................... 17

2.2

Previous researches ............................................................................................................. 19

2.2.2

Previous researches from developed countries ......................................................... 19

2.2.3

Previous researches in developing countries ............................................................. 21

2.2.4

Previous research in Vietnam .................................................................................... 23

CHAPTER 3: DATA AND METHODOLOGY ............................................................................... 26
3.1

Data construction and processing method ........................................................................ 26

3.2 Model .................................................................................................................................... 27
3.3

Factors calculating .............................................................................................................. 31

3.4

Testing methods and Hypotheses of research ................................................................... 32

CHAPTER 4: EMPERICAL RESULTS .......................................................................................... 35
4.1

Descriptive statistics ............................................................................................................. 35

4.2

Regression details ................................................................................................................ 37

4.3 Relevant test ......................................................................................................................... 39
4.4

About the result ................................................................................................................... 40

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS ..................................................... 42
5.1 Conclusion .................................................................................................................................. 42
5.2

Recommendations ............................................................................................................... 43

REFERENCES .................................................................................................................................... 47


CHAPTER 1: INTRODUCTION
1.1 Reason to research
The financial exchange and the banking sector are critical aspects of the national
economy. Early years, clearly for all investors (institutional or individual), the key aim
is to get the best possible return from investments. Choosing stocks for your portfolio
are close to gambling. Knowing the statesman will definitely have a chance to find the
side which will benefit a certain match. A business share price can change regularly to
match its actual market valuation, resulting in higher profit margins and thorough
examination of pricing fluctuations, risk, past success and unpredictable future.
Investors like to consider whether or not their investments are successful before
buying. Understanding of different fundamental forces is the key option to make
successful investment, the same with the skilled bettor that the football game requires
to be understood which influences can carry the outcome. During over one hundred
years of study, researchers have identified many pricing models. Studies started in the

mid-1960s and went on as part of the global economy, usually including the Capital
Asset Pricing Model (CAPM) from Sharpe (1964), Lintner (1965) and Mossin (1968).
(1966). In this model, only beta (market risk factor) is used to calculate the anticipated
return of the stock. There is a considerable denial regarding the reliability of CAPM
theory. According to Basu (1977), he noticed that all the above alternative
interpretations fail absolutely in the Indian sense. As a result, Rolf W. Banz (1981)
found that the CAPM was misspecified and that others have accepted that the
calculation is inadequate for NYSE stocks. After that, Fama and French conducted
observational research that investigated the relationship between income and stocks,
company scale, B/M ratios and beta. Finally, the French three-factor model was
released. This model was later replaced the CAPM model after 30 years of use. The
three-factor model was, by all accounts, a popular model for forecasting business
demand in the 1980s and in the future. The Fama-French three-factor model was
checked for its usage in the global capital markets in Australia, Canada, Germany,
France, Japan, the United Kingdom and the United States. Price and scale play a part
in both sectors. In 1997, Mark Carhart substituted the three-factor model with a revised
four-factor model that used a momentum factor to measure the monthly valuation of an
asset. The Carhart model is also used as an example to evaluate and administer mutual
funds. Analysis has shown that the complementarity effect can affect returns for the
plurality, but not everyone. Novy-Marx (2013) concludes that businesses with
significantly higher earnings produce significantly more sales. Aharoni, Grundy, and
Zeng (2013) find that a rise in spending and a decline in profit margins were associated
with an increase in profit. From these results, Fama and French developed that
diversification enhances return. A five-factor model for understanding financial
decision-making was released in the Journal of Financial Economics in early 2015.
Their aim is to remove gains from the equation and


prioritize investments (CMA-Conservative Minus Aggressive Investment). This model
has been tested in 23 developing markets, and reported to be successful in four regions

– North America, Japan, the Asia Pacific and Europe (Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Ireland, Italy, UK,...).
The Fama five-factor model is attracting massive interest from investors in general and
from the equity market in particular. However, most researchers have not yet explicitly
solved the problem. In the analysis of Vo Hong Duc and Mai Duy Tan (2014), they
graded the portfolio by running several regression models and splitting the portfolio
according to their findings. However, implementing the same portfolios will lead to
surprise, such as various sets of variables that might be associated and bound to each
other. In comparison, modelling portfolios on just 14 individuals is not necessary to
achieve reputation.
However, to the best of the author's understanding, "Application of Fama French
factors to industrial companies in Viet Nam stock market", I think the article would
analyze the introduction of the concept into the Vietnam stock market and help
investors maximizing their value in the stock market.
1.2 Research objective
The aim of this thesis was to:
Firstly, analyze the influence of the five-factor model, including industry, scale,
valuation, benefit, and investment factors has on listed industrial stocks returns in the
Vietnam stock market.
Secondly, describe the relevant valuation model and the fluctuation of the Vietnamese
capital market returns in a simple and detailed manner.
Finally, offer several ideas on how owners, regulators, and other stockholders may
enhance the continuing management of the fund.
1.3 Research questions
To accomplish the above study's purpose, these are the questions it seeks to address:
-

-

Does a company's book-to-market ratio, profitability, scale, market premium,

and investment risk impact the portfolio's returns? Is there a favorable or
negative connection between the stock results and the external factors?
The Fama French five-factor model is sufficient method for describing the
shifts in returns in the equity market in Viet Nam?
Why investors make use of analysis to raise equity capital and reduce
investment risks?


1.4 Research subject and range
The study emphasis is on utilizing the Fama French Five-Factor Pricing Model for
mentioned manufacturing firms on the HNX and HOSE exchanges.
Research range:
-

The time frame for the study is from 2014 to 2019. Prioritizing the objective to
create an accurate analysis, any earlier return data is disregarded in this study.

-

Firms from the study are expected to be majored in Industrials, listed as
securities, and need to provide accessible data concerning Market Price, Total
Assets, Total Liabilities, Shares Outstanding, Book Value and Treasury bill
taken from the VNCB from the three-month duration of the survey.

-

Space: This analysis used closed market details of the reported market
capitalization of industrial firms on HOSE and HNX. Companies outside of the
banking industry, including insurance companies, insurers and brokerage
companies, are not listed in these rankings.

1.5 Methodlogy
The aim of the analysis was to evaluate the Fama French Five Factor Model in
Vietnamese industrial firms, a quantitative methodology was implemented:
-

Follow the Ordinary Least Square (OLS) procedure to quantify the Betas, and
analyze the association between variables and portfolios.
Using Gibbons, Ross, and Shanken (1989) GRS model to approximate the
fundamental influence of the model on the list of firms.
Excel Office is used to synthesize data and equations accompanied by the usage
of Stata version 13 to execute regression and other related hypothesis testing
procedures.
Research model:
(

)

Where:
the expected return on asset i,

the risk-free rate of Treasury bill,

the excess market return, ,

(Small minus Big) the size factor,

(High minus Low) the value factor,

(Robust minus Weakness) the

profitability factor and (Conservative minus Aggressive) the investment factor .
The coefficients is the asset’s sensibility, the intercept and the error term of
asset i at time t.
1.6 Research contribution
The thesis provides many unique contributions:
The purpose of the study is to validate the usability of Fama French five-factor
pricing models. Thus, the study can clarify more precisely the factors of the Fama
French model for investors and researchers who are studying and discovering the ways
to predict future income rates by limiting the immediate risks. As a consequence, the
concept can be extended directly to the Vietnam capital exchange.
Experimentally, by assessing the feasibility of the templates, analyzing the test
findings, and presenting any hints to investors and individuals when choosing and
handling the portfolio.
1.7 Research outline
Chapter 1: Introduction
This chapter introduces the motives for conducting this project, the research aims, the
research subject, the research range and the scope of work.
Chapter 2: Literature review
This chapter presents the theoretical background behind the current study, and
previous research into a similar subject.
Chapter 3: Data and methodology
This chapter outlines the study architecture and the specifics of the experiment. The
author defines the dependent and influencing variables, gives guidance for
constructing a portfolio, and describes regression analysis and the steps involved in
using it.
Chapter 4: Empirical results

This chapter includes a regression study to demonstrate the effects of the key model
discussed in Chapter 3. This section includes data on all variables, including
association, graph, and compare and contrast of models. Any segment concludes with a
description of the findings and a reference to previous research.
Chapter 5: Conclusions and recommendations
Overall, I noticed that this study was useful in many respects. The author offers a
deeper interpretation of this analysis and gives suggestions for company owners, bank
officers and public policy leaders. The shortcomings of the analysis are stated and
recommendations for future studies are made.


CHAPTER 2: LITERATURE REVIEW AND PREVIOUS
RESEARCHES
2.1 Literature review
2.1.1 Arbitrage Pricing Theory (APT)
In 1976, Ross did not merely expand an established theory but to establish a new idea.
This theory, regarded as the Arbitrage Pricing Theory (APT), became extremely
popular. The derivative is used in exchanging stocks and goods from one market to
another, and a currency between various markets, in order to arbitrage. The APT is a
general principle of asset valuation whereby the projected return on a financial asset
may be uniquely modeled as a linear feature of some macro-economic variables or
theoretical market indices.
However, it is not a model, but rather a simplified hypothesis of financial returns. The
Expected Return of a stock i is a function that represents both systemic and nonsystematic risk factors.
(1)
Where:
is the expected return on asset i
the riske-free interest rate in government bonds
the asset beta sensitivity of different risk factors
the risk premium of the factor

k= (1,2, ...n) the number of the factor
i the variable of stock
We accepted that non-systematic threats can be almost reduced by diversification of
the portfolio as long as the compensatory considerations can only be attributed to
systematic risks. Systematic risk factors come under the concept of the APT
hypothesis, including:
-

Inflation
Economic cycle.
Economic prosperity, GNP.
Evaluates a difference between short-term and long-term interest rate.
How different government and business bonds vary.
Exchange rate
Gold price change.


Comparing APT and CAPM, the latter is more of a limited strategy. The APT would
not require that a stock portfolio exists, but unlike the CAPM does not point out all of
its risk factors. The APT allows for specific stocks to be mispriced, and hence only
refers to investments that are diversified. Additionally, different variables may be used
for multifactor models because the number of factors and individual factors are not
known. Despite not following any of the unrealistic CAPM expectations, the CAPM
tends to gather attention because of its versatility and broad sector proxies. Research
has suggested alternate asset valuation mechanisms to the CAPM. Research has often
specifically criticized the CAPM assumption. As a consequence, the APT is a financial
asset model, which was given various macroeconomic elements for a suitable level
when interpreting the shift of expected returns in some particular economy and at some
specific point. However, it is not the best result of the APT model, and is tougher to
decide which variables and what variables to pick into the model.

2.1.2 The Fama French three-factor model
For scholastics through the 1990s, the capital asset pricing model (CAPM) as a model
for acknowledging the pricing of companies in a sector – was essentially the biggest
distraction around the local region. Moreover, the CAPM was renowned for its
expansive business acknowledgments. Nonetheless, as was furthermore observed, the
CAPM was absolutely not succeed as a historical asset pricing model, which further
incited Eugene Fama and Kenneth French's confidence in a non-beta model as
providing an increasingly clarification of the data. Following the footsteps of William
Sharpe, an analysis was found out which had a major effect on how the CAPM model
was designed and created. Their analysis is focused on a model that integrates all the
variables that usually influence the predicted return, including company scale,
financial leverage, E/P ratio, BE/ME ratio, and stock beta. They conclude that the
association between beta and standard deviation does not justify average stock returns
since the 1960s through the 1980s. They decided that this model would kick off a great
hunt for factors that can help justify stock returns than that of the single variable, the
sector β implemented in the Sharpe (1964), Lintner (1965), and Black (1972) assetpricing model. Others also studied the average stock returns with respect to scale,
book-to-market ratio (value), and market premium. They have found that these
variables are very relevant and have stronger signals. The firm scale and book-tomarket ratio are outlined in the paper since they are related to equity returns. The rest
element (P/E and financial leverage) are blurred by placing these variables into the
formula.
Continuing with this analysis, Fama and French (1993) perform a review on two forms
of stock: stocks with limited capital market value and stocks with broad capital market
value (also called valuable stocks). When the size factor and value factor were used in
the regression model before including beta, the findings showed that the size factor
and value factor had a greater influence on market price movements than did the beta


factor. According to the reports, Fama and French had applied two variables, scale and
meaning, to the model to represent the role of the factors in portfolios. They say that
the following regression model can describe equity prices.

(

)

(2)
Where:
is the expected return of asset i at time t
is the risk-free interest rate of government bonds
is the excess market return
(Small minus Big) the size risk factor
(High minus Low) the value risk factor
The coefficients

, , and

are the asset’s sensibility

is the constants intercept
is the error term at time
The Fama French model illustrates how citizens who take greater chances earn
greater returns. In this analysis, the variables SMB and HML have an effect on the
profitability of a portfolio i. Portfolio i is composed of stocks that have strong
growth potential and low risk. Portfolio i includes valuable stocks with high and
growth stocks with low . Besides, portfolio involves what relates to the financial
sector in addition to what happens in the equity market.
This model appeared to fit well in summarizing the findings of previous study
studies, including analyses of well-known research studies CAPM. Other than
being checked, observational data were collected from various playing fields in
South Africa, India, Ukraine and Taiwan. Following the Fama French three-factor,
the rate of return in the portfolio has proved to be consistent. However, some

researchers simply believed these three variables could not strictly decide the
systematic risk premiums and did not expect there might be other factors


impacting profitability. Novy-Marx (2013) found that improved gross profitability
clarified the difference in stock returns rather than the book-to-market ratio. Hou et
al. (2015) observed that investment and return levels clarified variance in stock
results.
2.1.3 Carhart four factor model
Nartea et al. (2009) analyzed markets and noticed that the Carhart four-factor model
largely clarified momentum returns, but the Fama–French model didn't. Centered on
the Fama-French three factor model, this model introduces a new factor: momentum.
Carhart expands the Fama-French three factor concept by adding the momentum factor
in the combination. The momentum factor is described as the difference between the
return on winners' portfolio (the stocks which performed best in the last 3 -12 months)
and the return on losers' portfolio (the stocks which performed worst in the last 3 -12
months). According to the analysis of Jegadeesh and Titman (2001), they noticed that
purchasing stock that was doing well and selling stock that were doing poorly in the
previous 3 to 12 months would improve your overall earnings. This case is seen
because you have a limited time to determine what stocks you would like to purchase.
The explanation is that being the case, the economy still self-corrects. Some buyers
prefer to cash out their gains and sell their stocks so that they can get a cheaper deal.
Another hypothesis suggested that after a significant increase in valuation, a stock was
beyond its true value and would soon revert to it. Therefore, Carhart (1997) changes
the Fama and French model by introducing a fourth element: anomaly, which is
described as the difference between the returns on one-year winners and losers 'Robust
minus Weakness' (RMW). The WML factor is measured in the same way as the HML
factor except that the second type is done on stock results from the previous year
instead of from the current year (excluding the last month, t-1). The explanation
behind the B/M or momentum breakpoints emerging from a universe of large

capitalization firms is such that small capitalization stocks don't show the same traits
as large capitalization firms. The outcome was of a rough formula.
(

)

(3)


Where:
is the expected return on asset i
is the risk-free interest rate of government bond
the excess market return
(Small minus Big) the size risk factor
(High minus Low) the value risk factor
(Winner minus Loser) the momentum risk factor
The coefficients

,

is the asset’s sensibility

is the constants intercept
the error term of asset i at time t.
Since then, the four-factor approach has been implemented to a range of established
economies like the United States, Europe, Southeast Europe and several others. The
model has been found to suit the data better than a three-factor model. In comparison,
Wei Zhang (2018) updated the Carhart four-factor model, and pointed out that reversal
effects cannot be explained by the Fama-French three-factor except for Carhart's, their
findings were better explaining on the relation between Chinese stock returns and their

historical results, and provide alternative investment strategies for investors. The
French and Fama (2014) model also combines the five elements.
2.1.4 The Fama French five factor model

∑
Where:
is the share price at time t
E(dt+τ) is the expected dividend per share in period t+τ


r is (approximately) the long-term average expected stock return (the internal
rate of return on expected dividends)
Miller & Modigliani (1961) suggest the overall market valuation of the firm's portfolio at time t by (4)
as following:

∑
Where:
, is total equity earnings for period
the change in total book equity. Dividing by the time t
book equity gives:

∑
Because of the dividend discount model, Fama-French showed that, on average, stocks
with robust profitability appear to have higher projected returns than stocks with poor
profitability. Fama-French (2015) propose to complement their classic Fama-French
three-factor model with two additional variables, namely profitability (RMW – Robust
minus Weak) and investment (CMA – Cautious minus Aggressive) factors, resulting in
a 5-factor model, which is likely to become the current benchmark for asset pricing
studies:
(

)

(4)
Where:
is the expected return on porfolio i for period t
is the risk-free interest rate of government bonds for period t
is the excess market return for period t
(Small minus Big) the size risk factor
(High minus Low) the value risk factor


(Robust minus Weak) the profitability risk factor
(Conservative minus Aggressive) the investment risk factor
The coefficients

are the portfolio’s sensibility

is the constants intercept
is the error term of asset i at time t
According to statistical model and paper published by Stephen Cochrane, expenditure
factors and profitability factors are more susceptible to economic times than scale
factors. Thus, these considerations are very important in evaluating the asymmetrical
actions of hedge fund strategies over the market cycle. One of the key targets of many
hedge fund strategies is to capture the risk premium correlated with market anomalies;
such as where the typical small business outperforms the market. This asset pricing
paradigm has largely used industrialized world data and industry data from the United
States. In addition, Chan and Hamao (1991) also noticed that the value aspect of return
has a strong position in understanding market portfolio returns of Japanese stocks
(contradictory to the Fama and French (2010) findings). Vietnam stock market also

lacks scientific research promoting the use of the five-factor model in asset price
calculation, however, there are methodological and empirical evaluations supporting
the use of the five-factor model over the three-factor model. At the end of the
forthcoming chapter, the methodology and findings from the four experiments will be
addressed in greater depth.
2.2 Previous researches
2.2.2 Previous researches from developed countries
 Research in US of Ferson & Harvey (1999)
This research is focused on describing unconditional mean returns, and several studies
have studied the capacity to characterize average returns. Autocorrelations of fund
returns are normally poor approximately 0.1 for limited size portfolios, although some
are statistically important. The HML section does not give us the opportunity to
measure estimated returns depending on different time horizons. The regressions, the
coefficients on HML become smaller and t-ratios irrelevant. The business beta
coefficient is typically higher where there is a fit. The intercepts are usually much
narrower while the model is also used. In short, the regression intercepts are similar to
zero for their three-factor construct. The alphas are there, but they are often timevarying. It says that the Fama-French three-factor model describes neither the returns
of this portfolio nor its Sharpe ratio. Also a variant of the Fama French model which
implements time-varying betas can be rejected.


 Research in Japan of Daniel, Titman and Wei (2001)
The analytical study presented a statistically important association between the
average excess returns and the ex-ante factor loading rankings. The study revealed that
Fama-French three-factor model over-predicts there be a considerable stock market
returns link among factors. This loss could conceivably come from a low variance
HML beta. Furthermore, the investigators have found that there is a monotonic
ordering of ex ante HML factor and ex post factor loadings. On the basis of their
individual stock portfolio, there is a 0.586 contribution from the HML factor, with a t
statistic of 14.14. According to the three-factor model, a zero intercept should be

predicted. According to the model, the expected slope is negative 20.205, with 1.80
standard errors from zero. The gap between the two portfolio returns is too minimal,
which calls for the Fama-French model to be dismissed.
 Research in French of Souad Ajili (2002)
For all equity portfolios, Souad Ajili contrasted the Fama French three-factor model
and the CAPM in the case of the French economy between July 1991 and June 2001
with the equal-weight returns of all the commodities, the value-weight returns of all
the securities, the four indices CAC40, SBF80, SBF120 and SBF250, and considered
CAPM not to be inferior. The findings indicate that the Fama French three factor
model is preferable in describing returns on common stock than CAPM. At the
conclusion of three-factor regressions, the typical

is 0.905.

 Research in US of Zhu (2016)
To fill the void, Zhu expanded the Fama French five factor model with SSAEPD
(Standardized Standard Asymmetric Exponential Power Distribution) and GARCHtype volatility. This study is to evaluate whether the factor model is greater than the
initial Fama French 5-factor model. We use US stocks data gathered from July 1963 to
December 2013. (2015). Empirical evidence from the historical share prices indicate
that the Fama French five variables have an impact on asset valuation and the cost
assessment.
 Research in Australia of Chiah and partners (2016)
This research used Fama-French portfolio of stocks for 31 years. According to the
proof, the scale, demand, rate of return, and profitability elements have a mutually


interdependent relationship with each other. In general, in developing economies, the
five-factor Fama French model gives a clearer description of returns than the threefactor Fama French model and the CAPM model.
 Research in Japan of Keiichi Kubota and partner (2017)
For the long-run data for Japan, Keiichi Kubota and Hitoshi Takehara find that the

market prices are well calibrated. They oppose that the Fama and French five‐ factor
model can't differentiate between Japanese data better than standard. Specifically, the
effects of experiments using the RMW (Robust–minus–Weak) and the CMA
(Conservative–minus–Aggressive) factors of Fama French five-factor were not good
explanatory variables when they were used for generalized GMM tests with the
Hansen–Jagannathan distance scale. It was concluded that three-factor model
represents at the same strength as the five-factor model, and it was the best way to
view the details. The test figure of the four-factor model is strong at 9.989, which is an
almost equal as that of the Capital Asset Pricing Model (CAPM) at 10.064. Despite the
moderate performance of the four-factor and five-factor versions, the three-factor
model performs higher than all of them.
2.2.3 Previous researches in developing countries
 Research in European countries of Steven L, K. Geert and Roberto (1999)
This research explores the potential of beta and t factor by CAPM and Fama French
three factor to clarify the return diversity in 12 European countries. The authors rated
accessible securities according to their betas. The analysis shows that the lowest asset
allocation investments produce the best potential returns. Results revealed that the
chosen beta portfolios were actually growing return portfolios (t=2.08 and t=2.58).
Each portfolio size has a declining logarithmic trend. Statistically, the small firm
portfolio (ie, enterprisers) has a significant optimistic intercept 0.62% per month
(t=3.43). Despite the point figures that indicate negligible amounts of the intercepts,
the joint F-statistic of Gibbons et al. (1989) firmly opposes the assumptions that the
actual intercepts are equivalent to nil. This is because the beta and size-sorted
portfolios have a greater diversification than does the traditional nation portfolio by the


high . The return premium associated with size-based portfolios may be attributed to
the similarity risk between portfolios of various sizes.
 Research in China of Grace Xing Hu and partners (2018)
This analysis suggests the demand returns to firms' scale in China. The point sample is

from 1990 to 2016. In general, smaller businesses outperform bigger companies. This
essentially decreases the uncertainties by growing the variety of portfolios average
returns of 1.23%. To look for the Fama-French technique, SMB gains an economically
high return of 0.61 per month, not just statistically important but also economically
large. In comparison, stocks' average returns do not show consistent relationship with
their B/M ratios. The HML factor returned an average monthly return of 0.23, which is
above zero but not important. There would be no relation between the demand
parameter and premium in this situation. SMB has consistent positive coefficient of
Fama French cross-sectional experiments and association with the return of the sector.
Among three variables, SMB is the most significant for catching cross-sectional
fluctuations in Chinese stock returns. Both studies suggest that the variations in returns
are largely induced by elevated uncertainty in early years. Their effect would become
much lower as you impose the correction on data that have been gathered for a long
time.
 Research in India of Harshita and partners (2015)
The analysis of data on the CNX500 over fifteen years is provided. Results indicate
that Indian equity market has a favorable relationship between market capitalizations
and returns, profitability and returns, and B/M ratio and returns. The Fama and French
(1993) three factor asset pricing model (CAPM) is best for one portfolio, whereas the
Fama and French (2015) five factor model is better for multiple portfolios. This model
provides the maximum results when there is no element in the portfolio. For assets
investing, the five factor approach is preferable.
 Research in Turkey of Songul Kakilli Acaravci and partner (2017)
This research checked the validity of the five factor model by implementing it in Borsa
Istanbul (BIST) during the 132-month period between July 2005 and June 2016. These


14 intersection portfolios built on the basis of size, B/M ratio, profitability and
valuation parameters have been used. When the GRS-F test is performed, the null ( ) is
acknowledged. Therefore, it is assumed that the consumption model is right. The fivefactor approach seems true for the BIST as well. In addition, it tends to influence

fund efficiency dramatically. Once average

is examined, mean average

value in

this model is 0.33. This gives help to the nature of Fama French five-factor model
describing excess portfolio returns.
2.2.4 Previous research in Vietnam
 Research of Truong Dong Loc and Duong Thi Hoang Trang (2014)
This research offers analytical data of extending the Fama-French three factor model to
the HOSE stock market. The numbers came from January 2006 to December 2012.
The findings indicate that the profitability of listed firms on HOSE is positively
correlated with the risk of the sector, size of companies and B/M ratio. The data
reveals that in the six portfolio, the business conditions have a major influence on
profitability of all portfolios. The size factor is favorably linked with the profitability
of small-size companies (S), but negatively linked with the rate of return of large-size
companies (B). Finally, the HML is only positively associated with high (H) and
medium (M) B/M ratio portfolios but negatively correlated with low (L) B/M ratio
portfolios. We may confirm that Fama-French three-factor model is suitable in
explaining the change in profitability reported on the HOSE indices.
 Research of Vo Hong Duc và Mai Duy Tan (2014)
Fama French three-factor and Fama French five-factor model where evaluated in this
report. The data used in the project is focused on 281 companies published on the Ho
Chi Minh City Stock Exchange from January 2007 to December 2015. Regarding the
three factor model, the factor has the most consistent impact on such factor. The
following variables, all of which are relevant in the estimation model but add to
reserve the model. The model describes that the demand factor still bears positive
anticipation and original negative as well as statistically meaningful and accurate. The
size factor is optimistic and the importance factor is statistically significant. Aside

from profitability, the positive element in expenditure is profitability. In conclusion,


Fama French five-factor isn't sufficient to clarify return outcomes for Vietnam stock
market.
 Research of Nguyen Thi Thuy Nhi (2016)
This research is primarily focused on two versions of variables and factors of the Fama
French model and the four-factor model by Hou (Q-factor model). This paper uses data
collected from two stock markets in HOSE and HNX over the period from January
2009 to June 2015, as well as 3 strategies to divide portfolios. As a consequence, the
demand effect is positive, the SMB factor is positive with limited portfolio scale, the
HML factor is negative with a large portfolio value, the RMW
factor is positive with a high ROE, the CMA is positive with low OP. of the regression
model increased from 80% to 96%. This paper utilizes the Fama French five-factor
model to illustrate more than the Q four-factor model.
 Research of Huynh Ngoc Minh Tram (2017)
Based on the analysis, the findings indicate that the SMB factor ( ) has multiplied
importance of HML factor ( ) and the MRP market return factor ( ) sufficient to
understand predicted return of stocks because of the coefficient estimates of factors
which is statistically significant at the 5%. Even, only the SMB factor and MRP factor
are supposed to stay optimistic while the HML factor is almost negative. This means
that businesses that have a reduced scale or lower B/M ratio, they will still garner
income. All of the remaining RMW and CMA factors are not important. Therefore,
Fama French five-factor is not totally account the investment return in the Vietnam
stock market. There is a good association between equity price variability, market risk
index and stock returns in Vietnam.
2.3 Research gap
Research on asset pricing is comprehensive in the world. Also, recent findings have
provided other findings multiple study goals and implications worldwide on assets
pricing and taking plenty more from this focus.

For example, research in developing nations, they provide several different analytical
observations with several different types of measuring: OLS, GRS, GMM, GARCH-


type... Or re-process the data in different forms and shapes. The researches had
unfavorable findings with the influences of Ferson & Harvey (1999), Japan of Daniel,
Titman, Wei (2001) and Keiichi Kubota and partner (2017) but they showed an
understanding completely in accordance with the Fama French model. According to
research from Zhu, Souad Ajili, Chiah and collaborators, conducted on US, French and
Australia find that their model worked better than that of Fama: APT, CAPM, Carhart
model but Fama French model is outperformed them all.
Centered on research of Steven L, K. Geert and Roberto (1999), this research clearly
confirms that by utilizing portfolio beta and size-sorted portfolios are more diversified
than by high of the regressions.
There are more experiments on measures such as Fama French three-factor than the
five's. There are several weekly, annual, and quarterly Fama French model that have
not been measured. Most variables are optimistic such as HML, Nguyen Thi Thuy Nhi
(2016), but certain factors are not on the authority of Truong Dong Loc and Duong Thi
Hoang Trang (2014) and Huynh Ngoc Minh Tram (2016). These results regarding
Vietnamese companies make them fascinating country and subject of more studies.
Conclusion of Chapter 2
Chapter 2 attempts to summarize recent research on asset pricing that applies to Fama
French five-factor model. Other variables include the three-factor built by Fama
French, four-factor generated by Carhart, and five-factor created by Fama French. The
findings of this research are combined with those of previous reports. Chapter
continues with references to other works and fields that require more study.