INTRODUCTION
Reason to research
The financial exchange and banking sector play a vital role in the national economy, with investors—both institutional and individual—aiming for optimal returns on their investments Selecting stocks can resemble gambling, as understanding market dynamics is crucial for identifying profitable opportunities Share prices fluctuate based on market valuation, impacting profit margins and necessitating careful analysis of pricing trends, risks, historical performance, and future uncertainties Successful investors assess the potential of their investments before committing, much like skilled bettors in sports who analyze factors influencing outcomes Over the past century, researchers have developed various pricing models, including the Capital Asset Pricing Model (CAPM), which has been influential since its introduction in the mid-1960s.
The Capital Asset Pricing Model (CAPM), introduced in 1966, relies solely on beta as a market risk factor to predict stock returns; however, its reliability has been widely questioned Basu (1977) noted that alternative interpretations of CAPM did not hold true in the Indian context, and Rolf W Banz (1981) identified misspecifications in the model, particularly for NYSE stocks This led to Fama and French's observational research, which explored the connections between income, company size, book-to-market ratios, and beta, resulting in the development of the Fama-French three-factor model that replaced CAPM after three decades This model gained popularity in forecasting business demand during the 1980s and was validated across global markets, including Australia, Canada, and the UK In 1997, Mark Carhart introduced a four-factor model incorporating a momentum factor for asset valuation, which has been utilized to assess mutual funds Research by Novy-Marx (2013) indicated that firms with higher earnings achieve greater sales, while Aharoni, Grundy, and Zeng (2013) found that increased spending and reduced profit margins correlate with profit growth Building on these findings, Fama and French proposed that diversification enhances returns, culminating in a five-factor model published in 2015 aimed at refining financial decision-making by focusing on conservative versus aggressive investments This model has been successfully tested in 23 developing markets, particularly in North America, Japan, and Europe.
The Fama five-factor model is gaining significant attention from investors, particularly in the equity market, yet many researchers have not effectively addressed its complexities Vo Hong Duc and Mai Duy Tan (2014) conducted an analysis that involved grading portfolios through multiple regression models and segmenting them based on their findings However, replicating these portfolios can yield unexpected results due to the interrelated nature of various variable sets Moreover, modeling portfolios based solely on 14 individuals may not be sufficient for establishing credibility.
The article "Application of Fama French Factors to Industrial Companies in the Vietnam Stock Market" explores the integration of Fama French factors within Vietnam's stock market It aims to provide insights that can assist investors in maximizing their value and enhancing their investment strategies.
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.
Research questions
To accomplish the above study's purpose, these are the questions it seeks to address:
The book-to-market ratio, profitability, scale, market premium, and investment risk significantly influence a portfolio's returns Analyzing these factors reveals both positive and negative correlations with stock performance, highlighting the importance of external elements in shaping investment outcomes Understanding these relationships can provide investors with valuable insights for optimizing their portfolios.
- 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?
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
- 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
The study focuses on firms in the Industrials sector that are publicly listed and must provide accessible data for key financial metrics, including Market Price, Total Assets, Total Liabilities, Shares Outstanding, Book Value, and Treasury Bill information sourced from the VNCB over a three-month survey period.
This analysis focuses on the reported market capitalization of industrial firms listed on HOSE and HNX, excluding companies in the banking sector such as insurance firms and brokerages from the rankings.
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
The expected return on asset i is influenced by several factors, including the risk-free rate of Treasury bills and the excess market return Key components affecting this return are the size factor (Small minus Big), the value factor (High minus Low), the profitability factor (Robust minus Weakness), and the investment factor (Conservative minus Aggressive) Additionally, the asset's sensitivity is represented by the coefficients, while the intercept and error term for asset i at time t further refine the return calculation.
Research contribution
The thesis provides many unique contributions:
This study aims to validate the usability of the Fama-French five-factor pricing model, providing clarity on its factors for investors and researchers focused on predicting future income rates while minimizing immediate risks Consequently, the findings can be directly applied to the Vietnam capital market.
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.
Research outline
This chapter introduces the motives for conducting this project, the research aims, the research subject, the research range and the scope of work
This chapter presents the theoretical background behind the current study, and previous research into a similar subject
This chapter details the study's framework and experimental specifics, defining both the dependent and influencing variables It provides guidance on portfolio construction and explains regression analysis, including the necessary steps for its implementation.
This chapter presents a regression analysis to illustrate the impact of the key model introduced in Chapter 3 It encompasses data on all relevant variables, featuring associations, graphical representations, and comparisons of different models Each section concludes with a summary of the findings and references to prior research.
This study provides valuable insights, offering a comprehensive interpretation that benefits company owners, bank officials, and public policy leaders It highlights the analysis's limitations while also presenting recommendations for future research.
LITERATURE REVIEW AND PREVIOUS RESEARCHES
Literature review
In 1976, Ross introduced the Arbitrage Pricing Theory (APT), a groundbreaking concept that revolutionized asset valuation APT posits that the expected return on a financial asset can be modeled as a linear function of various macroeconomic variables or theoretical market indices This theory gained immense popularity and is widely utilized in the trading of stocks, goods, and currencies across different markets to capitalize on arbitrage opportunities.
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 non- systematic risk factors
Where: is the expected return on asset i
The risk-free interest rate in government bonds serves as a benchmark for assessing investment returns, while the asset beta measures the sensitivity of different risk factors Additionally, the risk premium associated with each factor, denoted as k (where k = 1, 2, n), plays a crucial role in evaluating potential stock returns, with 'i' representing the variable of the stock in question.
Diversification of a portfolio can effectively mitigate non-systematic threats, while compensatory considerations are primarily linked to systematic risks Systematic risk factors are encompassed within the Arbitrage Pricing Theory (APT) hypothesis.
- Evaluates a difference between short-term and long-term interest rate
- How different government and business bonds vary
The Arbitrage Pricing Theory (APT) offers a more flexible approach compared to the Capital Asset Pricing Model (CAPM), as it does not require a stock portfolio and allows for the mispricing of specific stocks within a diversified investment framework Unlike CAPM, which is often criticized for its unrealistic assumptions and limited strategy, APT accommodates various unknown factors in multifactor models, making it adaptable to different economic contexts Although CAPM remains popular due to its versatility and broad sector proxies, research has proposed alternative asset valuation mechanisms that challenge its foundational assumptions Ultimately, while APT incorporates macroeconomic elements to interpret expected return shifts, it presents challenges in selecting the appropriate variables for accurate modeling.
2.1.2 The Fama French three-factor model
Throughout the 1990s, the capital asset pricing model (CAPM) was a significant focus in academia for evaluating company pricing within sectors, yet it ultimately failed as a historical asset pricing model This shortcoming led to Eugene Fama and Kenneth French advocating for a non-beta model that offered a better explanation of data Following William Sharpe's foundational work, their analysis revealed that a comprehensive model, incorporating variables such as company size, financial leverage, E/P ratio, BE/ME ratio, and stock beta, was essential for predicting returns They found that the relationship between beta and standard deviation did not adequately explain average stock returns from the 1960s to the 1980s Their research sparked a search for additional factors influencing stock returns beyond the single variable of sector β used in earlier models Subsequent studies highlighted the importance of firm size and book-to-market ratio in relation to equity returns, while financial leverage and P/E ratios were less clearly defined within the model.
Fama and French (1993) conducted an analysis comparing stocks with limited capital market value to those with broad capital market value, also known as valuable stocks Their findings indicated that, when the size and value factors were included in the regression model prior to beta, these factors had a more significant impact on market price movements than beta itself They introduced two variables, scale and meaning, to represent the influence of these factors in portfolios, suggesting that their regression model effectively describes equity prices.
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 , 𝑠 , and are the asset’s sensibility is the constants intercept is the error term at time 𝑡
The Fama French model demonstrates that investors who embrace higher risks can achieve greater returns In this framework, the factors SMB and HML significantly influence the profitability of portfolio i, which is made up of low-risk stocks with strong growth potential This portfolio not only includes valuable stocks but also encompasses high-growth and low-risk stocks, while considering the dynamics of the financial sector and equity market.
The model effectively summarizes findings from previous studies, including analyses of the CAPM Observational data were collected from various playing fields in South Africa, India, Ukraine, and Taiwan, demonstrating consistent portfolio returns following the Fama French three-factor model However, some researchers argue that these three variables alone cannot fully determine systematic risk premiums and suggest that additional factors may influence profitability Novy-Marx (2013) indicated that improved gross profitability better explains stock return differences than the book-to-market ratio, while Hou et al (2015) found that investment and return levels account for variance in stock performance.
Nartea et al (2009) found that the Carhart four-factor model significantly explains momentum returns, unlike the Fama-French model This model builds on the Fama-French three-factor framework by incorporating a momentum factor, which measures the difference in returns between top-performing stocks (winners) and bottom-performing stocks (losers) over the past 3 to 12 months Jegadeesh and Titman (2001) demonstrated that buying stocks that have performed well and selling those that have performed poorly can enhance overall returns This phenomenon occurs as investors often seek to lock in gains and reallocate their investments Additionally, it is posited that stocks experiencing a significant valuation increase may exceed their intrinsic value, leading to a reversion Carhart (1997) further modifies the Fama-French model by introducing an anomaly factor, defined as the return differential between one-year winners and losers, measured similarly to the HML factor but based on prior year performance Notably, the behavior of large-cap stocks differs from that of small-cap stocks, resulting in a distinct set of characteristics.
𝑟 is the expected return on asset i
𝑟 is the risk-free interest rate of government bond
(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
The four-factor approach has been successfully applied to various established economies, including the United States and Europe, demonstrating superior performance compared to the three-factor model Wei Zhang (2018) enhanced the Carhart four-factor model, revealing that reversal effects are inadequately explained by the Fama-French three-factor model, while providing a better understanding of the relationship between Chinese stock returns and historical data, thus offering alternative investment strategies for investors Additionally, the Fama-French model (2014) incorporates five key factors for a more comprehensive analysis.
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:
, is total equity earnings for period 𝑡 the change in total book equity Dividing by the time t book equity gives:
The Fama-French research indicates that stocks with strong profitability tend to offer higher projected returns compared to those with weak profitability In 2015, they expanded their traditional three-factor model by introducing two additional factors: profitability (RMW – Robust minus Weak) and investment (CMA – Cautious minus Aggressive) This enhancement led to the development of a five-factor model, which is expected to serve as the new standard in asset pricing studies.
𝑟 is the expected return on porfolio i for period t
𝑟 is the risk-free interest rate of government bonds for period t
𝑟 𝑟 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 𝑠 𝑟 are the portfolio’s sensibility is the constants intercept is the error term of asset i at time t
A statistical model by Stephen Cochrane indicates that expenditure and profitability factors are more sensitive to economic conditions than scale factors, making them crucial for assessing hedge fund strategies during market cycles Many hedge funds aim to exploit risk premiums associated with market anomalies, such as the outperformance of small businesses While existing asset pricing research primarily relies on data from industrialized nations, including the U.S., Chan and Hamao (1991) highlighted the importance of value in understanding Japanese stock market returns, contrasting with Fama and French's (2010) findings The Vietnamese stock market lacks extensive research on the five-factor model for asset pricing, yet there is evidence supporting its superiority over the three-factor model The upcoming chapter will delve deeper into the methodology and results from four experiments.
Previous researches
2.2.2 Previous researches from developed countries
Research in US of Ferson & Harvey (1999)
This research examines unconditional mean returns, highlighting that various studies have attempted to characterize average returns It finds that the autocorrelations of fund returns for small portfolios are typically low, around 0.1, although some correlations may be statistically significant The HML section does not allow for measuring estimated returns across different time horizons, leading to diminishing coefficients and irrelevant t-ratios in regressions The business beta coefficient tends to be higher where there is a fit, while regression intercepts are generally close to zero in the three-factor model Although alphas are present, they often vary over time Ultimately, the Fama-French three-factor model fails to adequately explain the returns and Sharpe ratio of this portfolio, and a time-varying beta variant of the model is also rejected.
Research in Japan of Daniel, Titman and Wei (2001)
The analytical study identified a significant relationship between average excess returns and ex-ante factor loading rankings, highlighting that the Fama-French three-factor model tends to overestimate stock market returns This discrepancy may arise from a low variance in the HML beta Additionally, the research uncovered a consistent ordering of ex-ante HML factors and ex-post factor loadings, revealing a 0.586 contribution from the HML factor in individual stock portfolios, supported by a t-statistic of 14.14 The model predicts a zero intercept; however, the expected slope is negative at -20.205, with a standard error of 1.80 from zero Ultimately, the minimal gap between portfolio returns suggests that the Fama-French model should be reconsidered.
Research in French of Souad Ajili (2002)
Souad Ajili's analysis of equity portfolios in the French economy from July 1991 to June 2001 compared the Fama French three-factor model with the Capital Asset Pricing Model (CAPM) The study evaluated equal-weight and value-weight returns across various commodities and securities, including the CAC40, SBF80, SBF120, and SBF250 indices The results suggest that the Fama French three-factor model is more effective in explaining common stock returns than CAPM, with a typical regression result of 0.905.
Research in US of Zhu (2016)
Zhu enhanced the Fama French five-factor model by incorporating the Standardized Standard Asymmetric Exponential Power Distribution (SSAEPD) and GARCH-type volatility to assess its effectiveness compared to the original model This study utilized US stock data from July 1963 to December 2013, revealing that the five factors significantly influence asset valuation and cost assessment based on empirical evidence from historical share prices.
Research in Australia of Chiah and partners (2016)
This research analyzed a Fama-French stock portfolio over a 31-year period, revealing that scale, demand, rate of return, and profitability are interdependent factors In developing economies, the five-factor Fama-French model provides a more accurate depiction of stock returns compared to the three-factor Fama-French model and the Capital Asset Pricing Model (CAPM).
Research in Japan of Keiichi Kubota and partner (2017)
Keiichi Kubota and Hitoshi Takehara's long-term analysis of Japan's market prices reveals that they are well-calibrated, challenging the effectiveness of the Fama and French five-factor model Their findings indicate that the RMW and CMA factors do not serve as adequate explanatory variables in generalized GMM tests, as assessed by the Hansen–Jagannathan distance scale Ultimately, the research concludes that the three-factor model performs comparably to the five-factor model, with the four-factor model showing a strong test figure of 9.989, nearly matching the Capital Asset Pricing Model's score of 10.064 Despite the moderate performance of the four-factor and five-factor models, the three-factor model outperforms all alternatives.
2.2.3 Previous researches in developing countries
Research in European countries of Steven L, K Geert and Roberto (1999)
This research investigates the effectiveness of beta and t-factor models, specifically CAPM and the Fama-French three-factor model, in explaining return variability across 12 European countries The authors evaluated accessible securities based on their betas, revealing that lower asset allocation investments yield higher potential returns The findings indicate that the selected beta portfolios are indeed growth-oriented, with significant t-values of 2.08 and 2.58 Additionally, each portfolio size exhibits a declining logarithmic trend, with the small firm portfolio showing a notable positive intercept of 0.62% per month (t=3.43) Although the individual intercept figures appear minimal, the joint F-statistic from Gibbons et al (1989) robustly challenges the notion that these intercepts are negligible The research concludes that beta and size-sorted portfolios offer greater diversification compared to traditional national portfolios, with the return premium linked to size-based portfolios likely stemming from the inherent risk similarities among portfolios of varying sizes.
Research in China of Grace Xing Hu and partners (2018)
This analysis indicates that demand returns are linked to firm size in China, based on a sample from 1990 to 2016 Generally, smaller businesses outperform larger ones, leading to reduced uncertainties and an average portfolio return increase of 1.23% Utilizing the Fama-French model, the SMB (Small Minus Big) factor reveals a significant monthly return of 0.61, both statistically and economically substantial In contrast, the average returns of stocks do not consistently correlate with their book-to-market (B/M) ratios, as evidenced by the HML (High Minus Low) factor, which yields an average monthly return of 0.23—positive but not significant This suggests no relationship between the demand parameter and premium in this context The SMB factor consistently shows a positive coefficient in Fama-French cross-sectional analyses and correlates with sector returns, making it the most crucial variable for capturing cross-sectional fluctuations in Chinese stock returns Both studies indicate that early years' return variations are primarily driven by heightened uncertainty, with these effects diminishing when long-term data corrections are applied.
Research in India of Harshita and partners (2015)
An analysis of fifteen years of data on the CNX500 reveals a positive correlation in the Indian equity market between market capitalizations and returns, profitability and returns, as well as the book-to-market (B/M) ratio and returns, as supported by the findings of Fama and French.
The Capital Asset Pricing Model (CAPM) from 1993 is most effective for analyzing a single portfolio, while the Fama and French five-factor model introduced in 2015 is superior for evaluating multiple portfolios The five-factor model achieves optimal results when there are no elements in the portfolio, making it the preferred choice for asset investing.
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
Fourteen intersection portfolios based on size, book-to-market (B/M) ratio, profitability, and valuation parameters were analyzed The GRS-F test confirmed the null hypothesis, suggesting that the consumption model is valid Additionally, the five-factor model appears applicable to the BIST, significantly impacting fund efficiency The average mean value in this model is 0.33, supporting the Fama-French five-factor model's effectiveness in explaining excess portfolio returns.
Research of Truong Dong Loc and Duong Thi Hoang Trang (2014)
This research extends the Fama-French three-factor model to the HOSE stock market, analyzing data from January 2006 to December 2012 The findings show a positive correlation between the profitability of listed firms on HOSE and factors such as sector risk, company size, and book-to-market (B/M) ratio Business conditions significantly impact the profitability across six portfolios, with the size factor positively affecting small companies while negatively influencing large firms Additionally, the high and medium B/M ratio portfolios show a positive association with the HML factor, whereas the low B/M ratio portfolios exhibit a negative correlation Overall, the study confirms that the Fama-French three-factor model effectively explains profitability changes in HOSE indices.
Research of Vo Hong Duc và Mai Duy Tan (2014)
This report evaluates the Fama-French three-factor and five-factor models using data from 281 companies listed on the Ho Chi Minh City Stock Exchange between January 2007 and December 2015 The three-factor model reveals that the demand factor consistently influences outcomes, demonstrating a positive anticipation alongside a statistically significant negative component Additionally, the size factor shows an optimistic trend, while the importance factor is statistically significant Beyond profitability, the positive aspect of expenditures is highlighted, underscoring the model's relevance In conclusion, the findings indicate the efficacy of both models in assessing stock performance.
Fama French five-factor isn't sufficient to clarify return outcomes for Vietnam stock market
Research of Nguyen Thi Thuy Nhi (2016)
This research examines the Fama-French model and Hou's Q-factor model, utilizing data from the HOSE and HNX stock markets between January 2009 and June 2015 It employs three portfolio division strategies, revealing that the demand effect is positive, the SMB factor is positive with limited portfolio scale, while the HML factor is negative for larger portfolio values Additionally, the RMW factor shows a positive correlation with high ROE, and the CMA factor is positive with low operating profit The regression model's explanatory power increased from 80% to 96%, demonstrating that the Fama-French five-factor model provides a more comprehensive understanding than the Q four-factor model.
Research of Huynh Ngoc Minh Tram (2017)
The analysis reveals that the SMB factor significantly enhances the importance of both the HML factor and the MRP market return factor in predicting stock returns, with coefficient estimates being statistically significant at 5% Notably, only the SMB and MRP factors are expected to remain positive, while the HML factor shows a negative trend, indicating that smaller businesses or those with lower book-to-market ratios can still generate income Conversely, the RMW and CMA factors are deemed insignificant Consequently, the Fama-French five-factor model does not fully account for investment returns in the Vietnamese stock market, although there is a strong correlation between equity price variability, market risk, and stock returns in Vietnam.
DATA AND METHODOLOGY
Data construction and processing method
This research examines industries listed on HOSE and HNX from 2014 to 2019, utilizing quarterly data from January 2014 to December 2019 The study focuses on newly listed manufacturing companies, excluding many firms due to poor financial performance and regulatory issues Ultimately, the analysis highlights the top 100 firms over the six-year period.
In 2017, Trinh Minh Quang utilized the Thomson Reuters database to analyze stock returns of listed industrial companies in the Vietnam stock market The study incorporated data from various Vietnamese newspapers, including Vietstock, Cafef, VnDirect, and Sbv, which were also reflected in the annual reports of the projects The primary objective was to assess the effectiveness of the Fama French five-factor model in explaining stock returns within this market context.
Key financial metrics, including outstanding bonds, property values, quarterly net profit after tax, security closing prices, book value (BE), market value (ME), daily VN-index, and treasury bill yields, are sourced from the State Bank of Vietnam's official website.
In this analysis, I utilize the phase 1 database to assess the return costs of individual stocks, the market fund's return rate, and key financial ratios including the Book-to-Market (B/M) ratio, market capitalization (Size), net profit after tax (OP) as a measure of profitability, total asset development (Inv) as an investment pattern indicator, and the risk-free interest rate derived from Vietnam Treasury bills.
Step 3: Dividing and building up portfolios
According to the 4 quotas, including scale, B/M ratio, OP, and Inv divided yield to 18 portfolios will be created, and the detailed information will be given in the following section
This article summarizes five key financial variables in Microsoft Excel: Small minus Big (SMB), which measures the return difference between small and large stocks; High minus Low (HML), reflecting the return disparity between stocks with high and low book-to-market ratios; Robust minus Weak (RMW), indicating the return difference based on profitability; and Conservative minus Aggressive (CMA), which assesses the returns of conservative versus aggressive investments The article details the statistical calculations of these variables and explores their associations, employing regression methods to analyze their interactions effectively.
Step 5: Running the simulations, then doing the regression study
Efficiently managing variables in Excel using Stata allows for streamlined portfolio sorting, facilitating the analysis of return variance among explanatory variables By running regression models, businesses can assess the effectiveness of these models, ultimately enhancing decision-making and strategic planning.
Step 6: Analysis of research results and give conclusions
The research findings indicate that specific causes significantly influence investment rates, prompting a thorough mathematical analysis through regression techniques to guide final decisions for investors and business owners.
Data is analyzed using Stata version 13 and regression results are generated Microsoft Office Excel is used to incorporate the sample results.
Model
The model for time-series research:
(4) 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
The Small Minus Big (SMB) factor, introduced by Fama and French in 1993, measures the average return difference between small-cap and large-cap stocks within the industry for quarter t This factor highlights the performance gap, utilizing market capitalization as a proxy for size, based on financial statements from the previous quarter (t - 1).
The High Minus Low (HML) risk factor, introduced by Fama and French in 1993, measures the return disparity between portfolios of stocks with the highest and lowest book-to-market ratios This factor highlights the performance gap on day t, where portfolios are constructed based on inventory values relative to revenue for the fiscal quarter ending in t - 1.
The Robust Minus Weak Low (RMW) is a key risk factor introduced by Fama and French in 2015, highlighting the average performance disparity between the best and worst performing stocks within an industry To assess profitability, portfolios are constructed quarterly in each sector, utilizing accounting results from the previous year.
The Conservative Minus Aggressive (CMA) risk factor, introduced by Fama and French in 2015, highlights the performance disparity between the most cautious and the most aggressive portfolios in the capital market These portfolios are constructed quarterly, based on the increase in net assets from the previous fiscal quarter (t - 1), divided by the total assets at the end of that same quarter.
The Fama and French (1993, 2015) strategy involves a systematic process of sorting firms into portfolios each quarter based on four key factors: market capitalization, book-to-market ratio, profitability, and investment.
Stock return refers to the average rate of return calculated over the days within a quarter It is determined by the ending price of the stock at quarter \( t \) compared to its ending price at quarter \( t-1 \) The stock's rate of return for quarter \( t \) can be approximated using these ending prices.
The market return is assessed through the VN-Index on a daily basis, with measurements taken quarterly and influenced by the interest rates of each quarter The VN-Index for quarter t is specifically referred to as VN-Index quarter t, and the regular rate of return is calculated accordingly.
The risk-free rate of return, representing the market, is defined as the principal interest rate for a 1-year Treasury bill issued by the Reserve Bank of Vietnam from January 2014 to December 2019 This rate is calculated by dividing the quarterly risk-free interest rate by four quarters.
Market capitalization (stands for “Size”): the product of number of shares outstanding and the market price per share, as on the last day of each quarter t:
Profitability, represented by the term "OP," refers to the return on equity (ROE), which is calculated by subtracting all operating expenses, interest, depreciation, taxes, and preferred stock dividends from a company's total revenue to determine the remaining sales.
Investment (stands for “Inv”): using asset growth as a proxy for investment If the total assets in quarter t and the total assets in quarter t-1, following Cooper et al
(2008), Fama and French (2008), Gray and Johnson (2011) and Fama and French
(2014) Asset growth is defined as follows:
Starting in January 2014, the paper examines the judgment of cut-off portfolios as analyzed by Brailsford et al (2014) and progresses to the sorting and construction of portfolios The research utilizes factors similar to the methodology outlined by Fama and French (2015), specifically employing a 2x3 sorting approach, despite the availability of other sorting methods like 2x2 and 2x2x2x2 However, the application of these templates lacks a fundamental principle, leading to inconsistencies in the selection of suitable sorting approaches and their effectiveness across different datasets Consequently, the study focuses on sorting 100 large business stocks based on expenditure, scale, and profitability.
First, to compiling SMB, firms are divided into two groups: Small (S) and Big (B) – on the basis of market capitalization (using median value as the break point)
To define HML, companies are categorized into three groups based on their book-to-market (B/M) ratio: High (H), Neutral (N), and Low (L) This classification results in three distinct portfolios, determined by breakpoints corresponding to the bottom 40%, middle 20%, and top 40% of B/M values The 2x3 sorting method generates six portfolio blocks, as illustrated in Table 1.
Table 1 Size and B/M bivariate sorting
The process for calculating RMW (Robust Minus Weak) and CMA (Conservative Minus Aggressive) follows the same steps, differing only in the second sorting criteria based on variables: Robust (R), Neutral (N), and Weak (W) for RMW, and Conservative (C), Neutral (N), and Vigorous (A) for CMA The outcomes of these alternate portfolios are illustrated in Tables 2 and 3.
Table 2 Size and Investment bivariate sorting
Table 3 Size and Profitability bivariate sorting
This segment analyzes the return on investment (ROI) associated with the Fama-French five-factor model We utilized the quarterly returns of 18 portfolios, focusing on size, market equity to book equity (ME/BE), operating profitability (OP), and investment (Inv) as dependent variables Additionally, we incorporated excess returns from mimicking portfolios, including Small Minus Big (SMB), High Minus Low (HML), Robust Minus Weak (RMW), and Conservative Minus Aggressive (CMA), as explanatory variables in our regression analysis.
Factors calculating
From the model developed by Fama and French, we can see that there are three explanatory variables involved
SMB (SH+SN+SL+SR+SN+SW+SC+SN+SA)/9 –
(BH+BN+BL+BR+BN+BW+BC+BN+BA)/9
HML (SH+BH)/2 – (SL+BL)/2
RMW (SR+BR)/2 – (SW+BW)/2
CMA (SC+BC)/2 – (SA+BA)/2
Table 4 Construction of size, ME/BE, profitability and investment factors
The six portfolios categorized by size and book-to-market (B/M) ratios are labeled as SH, SN, SL, BH, BN, and SMB The SMB metric is calculated by taking the total of small-sized portfolios and subtracting the total of big-sized portfolios, expressed mathematically as [(SH + SN + SL + SR + SN + SW + SC + SN + SA) / 9].
The portfolio value coefficient (HML) is calculated by excluding neutral portfolios, represented as [(SH+BH)/2 - (SL+BL)/2] When evaluating the profitability and investment shares of the 2x3 sorts, particularly RMW and CMA, it is essential to consider OP and Inv as the secondary sorting criteria instead of B/M The author's research involved a systematic step-by-step process, utilizing multiple regression analysis to rank explanatory variables based on their marginal contributions This analysis determines whether any explanatory variables in the regression equation can be deemed redundant and thus removed, ensuring that the model selection is finalized only when no further variables can be added or omitted.
Testing methods and Hypotheses of research
First, use Ordinary Least Square (OLS) test applied to Fama Macbeth two-way Regression
This study employs time-series regression to examine the relationship between market returns and five risk factors for each portfolio Utilizing Ordinary Least Squares (OLS) models, we analyze the risk factors prevalent in financial markets The Betas derived from this model are calculated and tested, building upon the foundational work of Fama and French (1992) to ensure robust results.
In 1992, it was posited that a high danger of exchange correlates with a greater likelihood of benefit, leading to a significant relationship among various risk factors such as SMB, HML, RMW, and CMA According to Fama and French (2015), these factors are positively associated, suggesting that investors are ultimately rewarded for embracing the risks tied to their investments However, in the Vietnamese stock market, investors do not adhere to a standard theory, and research continues to yield positive outcomes across various factors.
: There is a positive correlation between the market factor and the excess return of the portfolio
: There is a positive correlation between the SMB factor and the excess return of portfolio
: There is a positive correlation between the HML value factor and the excess return portfolio
: There is a positive correlation between the RMW profit factor and the return on investment return
: There is a positive correlation between the CMA profit factor and the return on investment return
Heteroskedasticity White test is applied
The White test for homoskedasticity is a test to ensure that the errors in a regression model are normally distributed
Hypothesis : There is no hetoroskedasticity
If p-value≤0 05: reject , There is hetoroskedasticity
If p-value>0.05: accept , There is no hetoroskedasticity
Finally, use the GRS Regression test
The GRS test, created by Gibbons et al in 1989, evaluates the mean-variance performance by comparing a left-hand-side array of assets or portfolios with a right-hand-side model or portfolio.
N and T – N – K degrees of freedom (assumed that the errors are homoskedastic and uncorrelated)
T is the total number of observations
N is the number of assets (in this case, stocks)
K is the number of factors
The sample mean of the factor returns is denoted as (f), while the sample variance matrix of these returns is represented as ̂ The estimated alphas from the multivariate regression are indicated, and ̂ signifies the covariance matrix of the residuals from this regression analysis.
The GRS test evaluates the significance of alpha qualities in individual model regressions to determine if a model adequately captures the sample return variance A GRS score of zero for each regression indicates that the overall GRS score will also be zero Higher GRS statistic values suggest larger alpha values, indicating that the asset-pricing model is performing poorly as the alphas deviate further from zero.
The regression coefficient plays a crucial role in financial literature, necessitating the use of statistical tests to analyze its significance Among these, the GRS-F trial by Gibbons et al (1989) is recommended for assessing whether the alpha coefficients of various data sets are significantly different from zero Due to space limitations, the specific p-value indicating the significance level of the GRS-F calculation is not included (Gibbons et al., 1989, p 1124).
: All coefficients of Fama French five-factor got from various variables are identical to zero ( =0)
: Not all coefficients of Fama French five-factor got from various variables are not identical to zero ( ≠0)
Chapter 3 utilizes a comprehensive analysis of multiple database references and simulation methodologies to explore key indicators of major asset pricing models It examines various risks associated with firm returns, including price risk, size risk, value risk, profitability risk, and investment pattern risk Additionally, this section clarifies the use of regression analysis as a tool for processing and interpreting the results.
EMPERICAL RESULTS
Descriptive statistics
Table 5 Stationarity test results regarding level values of variables
Deviation Minimum Maximum Skewness Kurtosis
Source: data collected by the author and calculated on Stata version 13 software
Table 5 Panel A presents the descriptive statistics for the six-year intersection portfolio, detailing the quarterly factor premiums in industry companies The quarterly factor portfolio returns are recorded as 0.043% for MRP, -0.016% for SMB, -0.060% for HML, 0.034% for RMW, and -0.013% for CMAs When arranged from highest to lowest, these returns illustrate the varying performance of each factor within the portfolio.
Recent estimates indicate that the excess return on risk-free markets generally surpasses the excess returns of various portfolio strategies, including robust minus low income, conservative minus aggressive, small minus big, and large B/M ratios minus low B/M ratios Notably, the standard deviation of these results is relatively low, suggesting that the findings are closely clustered around the mean The smallest deviation observed is 0.0565 for SMB portfolios and 0.0756 for MRP portfolios.
MRP SMB HML RMW CMA
Correlation analysis provides an overview of the relationships among study variables, highlighting how one variable reacts to changes in another The linear regression coefficient indicates whether a relationship exists between two variables Notably, the relationship between the market portfolio and the CMW is negative, while a weak positive relationship exists between the market portfolio and SMB The correlation between the market portfolio and the HML factor is the strongest, exceeding 63.39%, indicating that corporate value significantly influences the stock market Additionally, the HML factor shows a positive correlation with the SMB factor, suggesting that the importance of enterprises is balanced with larger listed business organizations Conversely, the RMW factor exhibits the weakest correlations with other factors, particularly with the SMB factor The CMA factor shows positive correlations with both the demand factor and the SMB factor, indicating that listed industries can leverage these relationships to boost investment and market capital Overall, while the correlation factors among the explainable variables are generally weak, they still reflect returns from the research companies and the Fama French five-factor model.
The author presents a table illustrating the association measures among variables, highlighting the potential for repetitive knowledge and bias in regression effects The hypothesis suggests that multicollinearity is indicated by a tolerance value below 0.2 or 0.1, alongside a Variance Inflation Factor (VIF) of 10 or higher The author's findings support this hypothesis However, the secondary regression model confirms that multicollinearity is not present among the variables The VIF analysis reveals a maximum value of 1.87 and a mean of 1.49, indicating that multicollinearity does not adversely impact the regression results.
Regression details
The analysis categorizes portfolios into various types: small and big (S/B), medium (M), high and low (H/L), robust and weak (R/W), and conservative and aggressive (C/A) The t statistics are presented in parentheses, while P-values are shown in brackets Significance levels are indicated by asterisks, with coefficients marked at the 10%, 5%, and 1% levels Additionally, the t statistics have been adjusted using the Newey-West method to address heteroscedasticity issues.
The market risk factor ( ) – MRP
The market risk premium (MRP) coefficients for both fund management strategies are largely optimistic, with the exception of the RMW-BN Additionally, the MRP factor coefficients significantly impact the average returns of these portfolios.
0,391 (RMW-BN portfolio) to 1,321 (SMB-BH portfolio) These findings show that
MRP does not have a major impact on the performance of the large scale firms, and is consistent with large gains and strong B/M ratios
The size risk factor ( ) – SMB
In 11 out of 18 portfolios, the size factor is significant, showing substantial levels at both the 1% and 5% significance levels, while the CMA-BN portfolio is significant at the 10% level The SMB-BN coefficients range from a low of -1.286 to a high of 0.415 for the SMB-SH portfolio Notably, the size factor correlates with average yields for SMB-SH, RMW-SR, and major firms, including the CMA trend factor Overall, the SMB element does not exhibit a negative correlation with large industrial firms and is occasionally present in smaller businesses.
The value risk factor ( ) – HML
Among the 18 portfolios analyzed, five showed significant importance at both the 1 percent and 5 percent levels, while the remaining 13 portfolios were deemed negligible at these thresholds The HML component exhibited a negative response to SMB-SH, BL, CMA-SC, SN, SA, BC, and BA, with coefficients ranging from -0.648 to -0.091, whereas it demonstrated a positive reaction to SMB-SN.
SL, BH, BL, all RMW portfolios, and all the CMA-BN with coefficients ranging from 0.190 to 0.807
The profitability risk factor ( ) – RMW
The findings indicate that 33% of the total portfolios demonstrate significant performance at the 1%, 5%, and 10% levels Additionally, other portfolios exhibit positive responses to coefficients ranging from 0.104 to 0.582, attributed to the negative reactions of RMW-SR, SN, and SW.
The findings regarding the BN and BW portfolios align with the model's predictions, indicating that low-benefit portfolios exhibit a negative return on investment, while high-productivity portfolios show positive outcomes, with some exceptions This trend enhances the efficiency of the Association and contributes to a more consistent inventory management process.
The investment risk factor ( ) – CMA
At the 1% and 5% significance levels, the CMA factor is theoretically significant for CMA-SC, SN, and BA portfolios Notably, industrial firms with aggressive investments, such as SA and BA, provide a compelling insight into the investment factor.
77 percent and 86 percent projected returns Based upon the portfolios of low investment-trend, low coefficients are shown to concentrate on the portfolios SMB-
SH, SL, BN, BL, RMW-SN, CMA-SN, SA, BA and vice versa As the company builds the company, its usual stock drops.
Relevant test
The White test for Heteroskedasticity test
In 1980, White conducted a test for heteroskedasticity by regressing the squared residuals against all unique regression variables, their cross-products, and squared terms The resulting p-value, distributed as a Chi-squared statistic under the null hypothesis of homoskedasticity, was found to be 0.9091, which exceeds the significance level of α = 10% Consequently, the null hypothesis (H0) is accepted, indicating that the model does not exhibit heteroskedasticity.
In an asset price model, it is essential for the alpha intercepts to be close to zero to ensure the model's effectiveness in analyzing excess returns over risk-free rates The following test will provide insights into the influence of various factors on the returns of investment portfolios.
Dependent variables Model Average GRS
SMB_SH, SMB_SN, SMB_SL, SMB_BH,
SMB_BN, SMB_BL, RMW_SR, RMW_SN,
RMW_SW, RMW_BR, RMW_BN,
RMW_BW, CMA_SC, CMA_SN, CMA_SA,
CMA_BC, CMA_BN, CMA_BA
This table presents the excess return rates from 18 portfolios categorized by size, valuation, profitability, and investment The analysis includes average returns, GRS-F test statistics, and associated p-values Notably, the average percentage of 0.6581 is highlighted, indicating the robustness of the Fama-French five-factor model in explaining variations in excess portfolio returns.
The GRS-F test results indicate a Fama French five-factor score of 1.21 with a P-value of 0.48, leading to the acceptance of the null hypothesis This outcome confirms the model's effectiveness in explaining excess returns without the need for additional factors.
About the result
The study reveals that variables in the sequence significantly influence outcomes, with a 90.77% significance rating The business risk premium notably impacts manufacturing firms' returns, while SMB demonstrates the second highest explanatory power for fund returns The analysis indicates that the size of companies negatively affects return directions, and large portfolios show no correlation with the RMW component Additionally, the overconfidence effect (OP) plays a crucial role in shaping market perceptions and consumer behavior, impacting high-margin BR portfolios Investors remain concerned about firm capitalization and future organizational performance The application of HML to SMB highlights significant coefficients and marginal costs, alongside the noteworthy CMA effect, although it is less critical compared to others All regression coefficients in the study are statistically relevant, underscoring the importance of these financial variables.
This section explores the relationships between excess returns in the Fama-French five-factor model and manufacturing firms The author employs descriptive statistics and database analysis to validate the explanatory model To enhance prediction accuracy, necessary tests are conducted to mitigate common issues like multicollinearity and heteroskedasticity The regression results will be analyzed in line with contemporary scientific data.
CONCLUSION AND RECOMMENDATIONS
Conclusion
The aim of this analysis is to apply the Fama French five-factor asset pricing model to
The study examines 100 publicly traded companies from January 2014 to December 2019 to determine the relationship between excess returns of dividend portfolios and five key factors: market, size, value, profitability, and investment The findings reveal that these characteristics are statistically correlated with excess returns across various portfolio sorts, including Size-B/M, Size-OP, and Size-Inv While the CMA factor supports the other four, its impact is minimal, suggesting it may be unnecessary for a streamlined model The remaining factors exhibit significant power, but their effects are interdependent, complicating assessments when one factor dominates For instance, small-size stocks tend to outperform larger ones, while high B/M stocks generally see greater price increases than low B/M counterparts The market factor plays a crucial role, particularly for larger firms, driving stock prices upward during market growth Additionally, the study validates the five-factor model through average OLS regression intercepts and GRS tests, confirming its strong explanatory power for average returns across all tested portfolios.
Recommendations
5.2.1 Recommendations for those who use the Fama French five-factor model
The FF model fails to predict significant fluctuations in equity markets during extreme volatility, as evidenced by the bubble reports in Vietnam in 2007 and the drastic market price decline in 2008 due to the global economic downturn Consequently, the crisis phases can be excluded when applying FF templates This analysis serves as a valuable resource for students and researchers utilizing the model to align their comparisons and objectives effectively.
Demand conditions significantly influence the economy, making them a crucial consideration in selecting a multi-factor model for analysis While a simpler model with four factors—𝑟, SMB, HML, and RMW—can be effective, the Fama French five-factor model remains robust in explaining stock price movements However, certain stocks may not conform to expected effects, resulting in negative coefficients for specific portfolios This regression phenomenon, where a factor may show negative results for a limited number of categories, is not exclusive to Fama French's analysis; it can occur across various studies of securities in any industry.
When investing in stocks, investors should consider key factors such as interest, scale, volume, and potential benefits, as these significantly influence market fluctuations Understanding how these elements impact stock valuation is crucial, especially since market conditions can vary based on stock size Larger organizations typically experience higher profits and losses, prompting investors to assess evolving markets carefully Conversely, in times of sector uncertainty, it is advisable for investors to focus on stocks with lower book-to-market (B/M) ratios and share prices to mitigate risks.
The investment model identifies three key factors: scale, valuation, and benefit When the return gap between small-cap stocks (SMB) and large-cap stocks is significant, investors are advised to sell large-cap stocks and invest in small-cap stocks Additionally, it is recommended that investors sell stocks with low book-to-market (B/M) ratios in favor of those with strong B/M ratios for better returns.
The scale factor is frequently evaluated after the business factor, as investors differentiate between the returns of small and large stocks By calculating the Quality Capital Indicator (QCI), investors can identify opportunities to buy small-cap securities while selling larger ones This strategy allows buyers to acquire low-risk stocks while simultaneously selling those with higher profit potential.
Investors should monitor the average returns from robust operating performance (OP) and powerful OP If the gap between these returns begins to close, it may be wise to sell weak-OP stocks and invest in exceptionally strong-OP stocks Additionally, investors must approach corporate earnings claims with skepticism, as these figures can sometimes be exaggerated or misleading.
The Fama-French hypothesis suggests that while more stocks attract buyers, it does not address the impact of real stocks on public perception Poor-performing companies can lead to low stock prices and increased sales, prompting investors to reconsider purchasing securities expected to yield decent returns Interestingly, shares of non-profit firms with low stock valuations may still provide significant future returns.
5.2.3 Recommendations for stock market in Vietnam
Large businesses with a high book-to-market (B/M) ratio may struggle due to shifting business climates, unpredictable investment patterns, and declining earnings To mitigate these risks, companies should closely manage their stock and property scales, reducing reliance on the stock market Conversely, if businesses believe in an impending positive economic shift, they can downsize, leading to an increased B/M ratio.
A low operating margin can negatively affect businesses by leading to reduced revenues, but successful companies can mitigate other risks, such as market and valuation risks In the event of contraction, overall performance may decline Additionally, a low book-to-market (B/M) ratio on the balance sheet typically results in lower operating profits.
This research has certain limitations, including a data range of only six years, focusing primarily on industrial sectors, which may not adequately represent the entire market or accurately reflect the impact of various factors on average stock returns Consequently, both the size and duration of the database are relatively small Methodologically, while Fama and French (2014) suggest multiple ways to categorize portfolios, this study utilizes only a 2x3 sorting approach and does not implement the complete two-way Fama French regression, opting instead for the GRS F-test to assess the model's effectiveness.
Further research into pricing formulas could enhance the understanding of SMB and the arrangement of portfolios based on various factors Future studies might explore the use of business portfolios or reevaluate small-cap, mid-cap, and large-cap classifications It is essential to conduct more research to ascertain whether such segmentation takes place Fama and French (2016) noted that cash profitability outperforms operational profitability within a five-factor model Analyzing productivity in Vietnam's markets could provide valuable insights into the reasons for higher productivity losses during recovery periods Despite the challenges of addressing the imperfections of the five-factor model, findings from backward calculations suggest that additional factors, such as ROA and EBIT, may also influence stock prices on the Vietnam stock exchange.
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Make OLS Regression test with 18 sorted portfolios:
Regression of SMB- SN with Fama French five-factor
Regression of SMB- BH with Fama French five-factor
Regression of CMA- BA with Fama French five-factor
Regression of CMA- BC with Fama French five-factor
Regression of CMA-BN with Fama French five-factor
Regression of CMA-SA with Fama French five-factor
Regression of CMA-SC with Fama French five-factor
Regression of CMA-SN with Fama French five-factor
Regression of RMW-BN with Fama French five-factor
Regression of RMW-BR with Fama French five-factor
Regression of RMW-BW with Fama French five-factor
Regression of RMW-SN with Fama French five-factor
Regression of RMW- BR with Fama French five-factor
Regression of RMW- SW with Fama French five-factor
Regression of SMB- BL with Fama French five-factor
Regression of SMB- BN with Fama French five-factor
Regression of SMB- SH with Fama French five-factor
Regression of SMB- SL with Fama French five-factor