MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY BACHELOR THESIS Major Financial Banking Topic APPLICATION OF FAMA FRENCH FIVE FACTOR ASSET PRICING MODEL[.]
INTRODUCTION
REASON TO RESEARCH
The stock market is crucial to the national economy, serving as a primary system for investment Investors, whether institutional or individual, aim for maximum returns but often struggle to identify which stocks will yield the most profit Selecting the right stocks is akin to betting on soccer; informed bettors analyze team skills, injuries, and line-ups to make educated predictions Similarly, understanding the factors influencing stock returns is vital for making sound investment choices A company's share price reflects its market value and is influenced by price volatility, risk, past performance, and unforeseen future events Thus, assessing potential returns is an essential step for investors before committing their capital.
Over the past century, various pricing models have emerged, beginning with the Capital Asset Pricing Model (CAPM) developed by Sharpe, Lintner, and Mossin in the mid-1900s, which primarily assesses market risk through stock beta Despite its widespread application, CAPM has faced criticism, particularly in contexts like India, where studies by Basu demonstrated its ineffectiveness Further scrutiny by Banz revealed mis-specifications in the model when applied to NYSE stocks In contrast, the Arbitrage Pricing Theory (APT) offers a multi-factor approach, allowing arbitrageurs to exploit market value discrepancies, albeit not without risk Eugene Fama and Kenneth French expanded on these concepts, leading to the creation of the Fama-French three-factor model, which gained traction in the 1980s and was validated across multiple global markets Mark Carhart later introduced a four-factor model incorporating momentum, primarily used for mutual fund evaluation Subsequent research highlighted the significant relationship between profitability and stock returns, prompting Fama and French to develop a five-factor model in 2015, which added new dimensions of Profit and Investment factors This model has been extensively studied across 23 developed markets, garnering particular interest in Vietnam, although the relevance of its factors remains a topic of ongoing research.
This article aims to evaluate the application of Fama-French factors in the context of industrial companies within the Vietnam stock market The author anticipates that this analysis will enhance understanding of the model's integration into the market, ultimately assisting investors in optimizing their stock market value.
RESEARCH GOAL
This research aims to assess the influence of the Fama French five-factor model—comprising market, size, value, profit, and investment factors—on the expected returns of listed industrial stocks in Vietnam's HSX and HNX stock markets By employing analytical tools to measure model performance, the study seeks to clearly illustrate the fluctuations in average returns for these stocks Ultimately, the findings will provide valuable recommendations for investors, policymakers, and stakeholders to enhance portfolio selection and management strategies.
RESEARCH QUESTIONS
To fulfill the above research‘s goal, this thesis‘s important mission is to answer these following questions:
- Do the factors: market premium, size, book-to-market ratio, profitability, and investment risk affect the returns and positive or negative correlation to the returns of the portfolio?
- The asset pricing model Fama French five-factor model is suitable model for explaining the change of the returns in Viet Nam stock market?
- How investors use research results to increase profits and decrease risks?
RESEARCH SUBJECT AND RANGE
Research subject: using factors of Fama French five-factor pricing model with 100 industrial companies on HSX and HNX stock market
- The time frame for the study is from January 2012 to January 2018 Prioritizing the objective to create an accurate analysis, any earlier return data is disregarded in this study
The companies included in the sample must operate within the industrial sector and be classified as equity They are required to provide accessible data on key financial metrics, including stock price (market value), book value, total assets, total liabilities, shares outstanding, and return on equity (ROE) Additionally, the sample must incorporate the three-month Treasury bill rate as reported by the State Bank of Vietnam during the specified period.
This study analyzes data from publicly listed industrial companies on the HSX and HNX stock markets, explicitly excluding entities in the financial sector, such as banks, insurance firms, securities companies, and investment funds.
METHODLOGY
With the research goal is to test the Fama French five-factor asset pricing model in industrial companies of Vietnam stock market, the thesis applies mainly quantitative method:
- Use the Ordinary Least Square (OLS) method to measure the betas, check correlations between factors thus portfolios
- Use Gibbons, Ross, and Shanken (1989) GRS validation test to check the efficiency of the model whether itself has power on explanatory to listed companies below
- Use Excel Office to synthesize data and calculations then finally use Stata version 13 to run Regression models and other relevant tests
The expected return on asset i is determined by the risk-free rate of Treasury bills, along with several key factors: the excess market return, the size factor (Small minus Big), the value factor (High minus Low), and the profitability factor (Robust minus Weakness).
(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.
RESEARCH CONTRIBUTION
The thesis has following contributions: xii
This thesis investigates the practical application of the Fama-French five-factor pricing model, aiming to clarify its components for investors and researchers seeking to forecast future returns while minimizing risks The findings can be directly applied to the Vietnam stock market, enhancing understanding and usability of this model in local investment strategies.
Experimentally, by evaluating the effectiveness of the models, giving a review of the research results, thereby providing some hints to investors and individuals when selecting and managing the portfolio.
RESEARCH OUTLINE
This chapter will give an overview on reasons of the author decide to work with this topic, research goals, research subject, research range and research contribution.
LITERATURE REVIEW AND PREVIOUS RESEARCHES
Literature review
The industrial sector constitutes a significant portion of the economy, focusing on the production of material goods that are manufactured, processed, and manipulated to meet consumer demand and support business activities essential for daily life This economic activity involves large-scale production driven by advanced technology, scientific advancements, and developed mechanisms.
In Vietnam, the industrial sector encompasses a diverse range of companies involved in mining minerals, coal, stone, and petroleum, as well as processing and manufacturing in areas such as food, materials, and capital goods Additionally, it includes transportation, the production and distribution of electricity, gas, and water, garment manufacturing, household appliances, chemical processing, and various commercial and professional services.
His innovative work constructed the establishment of what is now well-known as
Modern Portfolio Theory (MPT), developed by Markowitz, emphasizes the importance of the number of securities in a portfolio and their covariance relationships for enhancing portfolio performance The Markowitz curve represents all optimal portfolios, showcasing the balance between minimizing risk for a given return or maximizing return for a specified risk The model focuses on determining the appropriate weight of each asset to maximize investment returns while minimizing risk Key components include the anticipated rate of return, the standard deviation of each asset, and the correlation coefficients among firms While not an asset pricing model, MPT serves as a framework for constructing the most advantageous portfolios Investors assess potential returns by evaluating the expected value of the probability distribution of returns, while risk is measured by the variability around this expected value, commonly represented by variance and standard deviation.
Given any set of risky assets and a set of weights that describe how the portfolio investment is separated, the general formulas of expected return for n firms is:
∑ 𝑤 is 1.0 is the return on security i and portfolio p
𝑤 is the proportion of the funds invested in security i
The difference of a portfolio mix with firms is equivalent to the weighted average covariance of the returns on its individual firms:
∑ ∑ 𝑤 𝑤 (2) Where: is correlation coefficient between the rates of return on firm or portfolio i,r i , i , and j are standard deviations of respectively
Markowitz pioneered modern portfolio theory by establishing optimization based on risk, return, variance, and covariance He noted that investors typically choose from Pareto optimal risk-return combinations, given the dual criteria of risk and return His mean-variance portfolio hypothesis laid the groundwork for the development of the capital asset pricing model, a crucial aspect of investment management practice.
The MPT model has evolved through research to reflect more realistic assumptions about real-life scenarios Notably, S Uryasev's 1999 research introduced transaction costs into the model, while others have suggested alternative risk measurement methods beyond the standard deviation of stock returns However, Kroll, Levy, and Markowitz (1984) highlighted that many practitioners remain skeptical about the effectiveness of standard deviation as a reliable risk measure.
2.1.3 CAPM (Capital Asset Pricing Model)
William Sharpe developed the Capital Asset Pricing Model (CAPM) in 1964, building on Harry Markowitz's theory from 1952 CAPM establishes a linear and positive relationship between systematic risk and expected return for financial assets, particularly stocks This model is widely used in finance to price risky firms and determine expected returns based on asset risk and cost of capital The CAPM equation evaluates whether a stock is fairly valued by comparing its risk and the time value of money against its expected return Its appeal lies in its ability to provide straightforward predictions about risk and the correlation between expected return and risk Additionally, the model utilizes beta (β) as an indicator of a stock's sensitivity to market returns.
In the 1990s, the Capital Asset Pricing Model (CAPM) emerged as a significant framework for understanding firm pricing within markets, gaining widespread recognition across the industry The CAPM formula remains a cornerstone in financial analysis, highlighting its importance in investment decision-making.
( ) (2) Where: is the expected return of asset i is the risk-free rate
( ) is the market risk premium xvii is the market risk factor is the beta of the asset i
Beta is a key metric in assessing a stock's risk, reflecting its price volatility relative to a broad market index, which accounts for approximately 70% of stock return fluctuations However, the risk associated with price movements is not symmetrical, and the timeframe for evaluating a stock's volatility can vary, as stock returns do not follow a normal distribution The Capital Asset Pricing Model (CAPM) presumes a stable risk-free rate over time, but fluctuations in this rate can inflate the perceived capital value of investments, potentially leading to overvaluation Additionally, the market portfolio used to calculate the market risk premium is theoretical and not directly investable, prompting investors to rely on major stock indices, such as the VN-Index, as imperfect substitutes for market performance.
A study by Chen (2003) highlights the encouraging empirical performance of the Capital Asset Pricing Model (CAPM) in the Taiwan stock market, demonstrating a statistically significant relationship between stock returns and beta, with the market beta explaining nearly 50% of equity return movements in the textiles sector Elmo (2018) further emphasizes the CAPM's ability to explain market behavior over specific timeframes, noting that Brazilian sustainability companies exhibit a positive correlation between average portfolio returns and size, indicating that larger portfolios yield significantly higher returns However, despite its simplicity, CAPM struggles to accurately explain realized returns Dempsey (2013) warns that recent factor models lack the risk-return foundation of CAPM, suggesting potential shortcomings, while Melody Nyangara (2016) advises analysts and investors to approach the CAPM with caution.
In 1976, Ross introduced the Arbitrage Pricing Theory (APT), a groundbreaking approach to asset pricing that diverges from existing theories APT is utilized in trading large volumes of stocks and foreign currencies across markets for arbitrage opportunities This theory posits that the expected return of a financial asset can be expressed as a linear function of multiple macroeconomic factors or theoretical market indices, with each factor's impact quantified by a specific beta coefficient.
The Arbitrage Pricing Theory (APT) is a general theory of expected returns on financial assets rather than a specific model It is based on two key assumptions: first, that investors can borrow or lend at a risk-free rate, which is uniform for all investors regardless of the amount; and second, that all investors share identical expectations, leading to a consensus on the distribution of asset returns Stephen Ross posited that non-systematic risks can be minimized through portfolio diversification, thus focusing risk compensation on systematic risks Key systematic risk factors in APT include inflation, the business cycle, economic growth (GNP), interest rate differentials, and exchange rates APT encompasses multi-factor models categorized into macroeconomic, fundamental, and statistical models Macroeconomic models link security returns to employment, inflation, and interest rates, while fundamental models analyze the relationship between returns and a company's financials Statistical models assess returns based on measurable firm performance In this context, a firm's beta measures its systemic risk relative to the market, with a beta of 1 indicating equal volatility to the market, greater than 1 signifying higher volatility, and less than 1 indicating lower volatility The expected return of a stock is thus a function of various factors that represent systematic risk.
The expected return on asset i is determined by the risk-free interest rate from government bonds, the asset's beta which measures its sensitivity to various risk factors, and the risk premium associated with each factor The analysis considers multiple factors, denoted as k, where k ranges from 1 to n.
The comparison between the Arbitrage Pricing Theory (APT) and the Capital Asset Pricing Model (CAPM) highlights key differences in their approaches to asset pricing While CAPM is a simpler, single-factor model that relies on historical data, APT is a more complex, multi-factor model that does not require a market portfolio to establish return-beta relationships APT allows for individual stock mispricing and is applicable only to well-diversified portfolios, whereas CAPM is more reliable but may have limitations due to its assumptions Additionally, APT can be extended to various multifactor models, offering flexibility in identifying risk factors, though it also faces challenges in determining the correct variables Overall, APT incorporates a variety of macroeconomic elements, making it a forward-looking model in contrast to CAPM's historical perspective.
The Arbitrage Pricing Theory (APT) developed by Akwimbi William in 2003 effectively explains expected returns in the Nairobi Securities Exchange (NSE), with return indices being key variables in the time series of returns However, incorporating fundamental variables significantly enhances the understanding of these returns Additionally, Stefan Robert's study, "Empirical Testing of CPM and PT Models," highlights the advantages of using factor analysis as a relatively new tool for testing the APT While market return remains crucial, the complexity of securities' returns on the Bucharest Stock Exchange indicates that a single factor cannot fully account for their behavior.
2.1.5 The Fama French three-factor model
The Capital Asset Pricing Model (CAPM) has struggled as a historical asset pricing model, leading to Eugene Fama and Kenneth French's assertion that non-beta factors provide a clearer understanding of data Their 1992 research demonstrated that returns are influenced not only by market risk but also by additional factors According to Petros Messis (2006), the Fama-French model significantly outperforms the Arbitrage Pricing Theory (APT) in time-series regressions, while APT shows slight superiority in cross-sectional contexts Following the work of William Sharpe and Stephen Ross, the Fama-French three-factor model emerged, incorporating new factors derived from the CAPM and assessing beta coefficients from the APT This model includes firm size (SMB), the book-to-market ratio (HML), and evaluates stock betas Fama and MacBeth found that from 1963 to 1990, the correlation between beta and average stock returns was weak, prompting a search for alternative variables that could better explain stock returns than the single market beta.
Previous researches
2.2.1 Previous researches from developed countries
Research in US of Zhu (2016):
Zhu enhanced the Fama-French five-factor model by incorporating non-Normal error distributions using the Standardized Standard Asymmetric Exponential Power Distribution (SSAEPD) and GARCH-type volatility This innovative approach aims to improve the accuracy of asset return predictions.
The article explores a Factor Model based on SSAEPD error and GARCH-type volatility to determine if it can outperform the traditional Fama-French 5-factor model Analyzing monthly U.S stock returns from July 1963 to December 2013, the empirical findings indicate that the Fama-French 5 factors remain robust and relevant, even when accounting for GARCH-type volatilities and non-normal errors This research contributes to the existing asset pricing literature and serves as a valuable reference for investors.
Research in Australia of Chiah and partners (2016)
A study analyzing Australian companies over 31 years, from 1982 to 2013, employed a five-factor Fama French model using three portfolio types: 5x5 Size-BE/ME, 5x5 Size-OP, and 5x5 Size-Inv The findings reveal a correlation between market and size factors with rate of return, while value, profit, and investment factors exhibit a two-way impact that varies by portfolio Overall, in developed countries, the five-factor Fama French model provides superior explanatory power compared to both the three-factor Fama French model and the CAPM model.
Research in Japan of Keiichi Kubota and partner (2017)
Keiichi Kubota and Hitoshi Takehara analyzed stock pricing structures in Japan from 1978 to 2014, concluding that the Fama and French five-factor model is not the optimal benchmark for Japanese data Their findings indicate that the RMW and CMA factors are weak explanatory variables, as evidenced by generalized GMM tests using the Hansen–Jagannathan distance measure The Gibbons–Ross–Shaken test revealed a minimum statistic of 9.447 for the five-factor model, while the simpler three-factor model performed comparably at 9.491 Additionally, the four-factor model showed a higher statistic of 9.988, nearly matching the CAPM at 10.064 Despite the four and five-factor models being suitable for the US market, their effectiveness is diminished in Japan, highlighting the superiority of the three-factor model due to its simplicity and the significance of its coefficients.
2.2.2 Previous researches in developing countries
Research in China of Grace Xing Hu and partners (2018)
This study highlights a strong correlation between stock returns and firm size in the Chinese market from 1990 to 2016, revealing that small stocks consistently outperform large stocks A long-short portfolio strategy that goes long on the smallest stocks and short on the largest yields a statistically significant variance-adjusted average return of 1.23% Using the Fama-French methodology, the SMB factor generates a variance-adjusted average return of 0.61% per month, which is both economically substantial and statistically significant In contrast, average stock returns show no clear relationship with book-to-market (B/M) ratios, as evidenced by the HML factor's variance-adjusted average return of 0.23% per month, which is positive but not statistically significant Additionally, the market factor does not demonstrate a significant premium Notably, the SMB factor consistently outperforms both the market and HML factors in time series regressions and Fama-French cross-sectional tests, making it the most crucial factor for explaining cross-sectional variations in Chinese stock returns The findings indicate that early market volatility and the limited number of stocks contributed to extreme values, but these effects diminish with a longer sample period and actual volatility adjustments.
Research in India of Harshita and partners (2015)
This study employs hierarchical multiple regression analysis on data from companies in the CNX 500 index over a fifteen-year period, from October 1999 to September 2014 The findings indicate that the Indian stock market exhibits an inverse relationship between market capitalization, profitability, investment, and returns, while showing a direct correlation between the book-to-market (B/M) ratio and returns Additionally, the three-factor asset pricing model by Fama and French (1993) outperforms the capital asset pricing model (CAPM) across all portfolios, and the five-factor model introduced by Fama and French (2015) surpasses the three-factor model when analyzing portfolios based on profitability and investment Notably, the four-factor model demonstrates the highest explanatory power for portfolios not based on investment, whereas the five-factor model excels for investment-based portfolios.
Research in Turkey of Songul Kakilli Acaravci and partner (2017)
The study evaluates the Fama-French five-factor model's effectiveness in Borsa Istanbul (BIST) over a 132-month period from July 2005 to June 2016, utilizing excess returns from 14 distinct intersection portfolios based on size, B/M ratio, profitability, and investment factors The GRS-F test yielded a result of 1.00 (P 0.45), leading to the acceptance of the null hypothesis, indicating no pricing errors within the model Consequently, the five-factor model is deemed valid in BIST and effectively explains variations in excess portfolio returns, with an average explanatory value of 0.33.
Research of Truong Dong Loc and Duong Thi Hoang Trang (2014)
This study empirically validates the applicability of the Fama-French three-factor model to the HOSE stock market, using data from January 2006 to December 2012 The findings indicate a positive correlation between the profitability of listed companies on HOSE and factors such as market risk, company size, and the book-to-market (B/M) ratio Additionally, the market factor significantly influences the profitability across all six portfolios analyzed While the size factor positively affects smaller companies, it negatively impacts the returns of larger firms Furthermore, the high and medium B/M ratio portfolios show a positive correlation with the HML factor, whereas the low B/M ratio portfolios exhibit a negative correlation.
According to the results, we can confirm that the Fama-French three-factor model is appropriate in explaining the change of profitability listed on HOSE
Research of Vo Hong Duc và Mai Duy Tan (2014)
This study evaluates the Fama-French three-factor and five-factor models using a data sample of 281 companies listed on the Ho Chi Minh City Stock Exchange from January 2007 to December 2015, excluding financial institutions The analysis reveals that the market factor has the strongest and most consistent impact in the three-factor model, while the value factor provides better explanations but adds complexity to the model In the five-factor model, the market factor shows a positive expectation, although it is originally negative and statistically significant The size factor is positive, and while the value factor offers better explanations, it lacks statistical significance for some portfolios Among the profitability and investment factors, profitability emerges as the most significant Ultimately, the investment factor in the Fama-French five-factor model does not adequately account for expected returns in the Vietnamese stock market.
Research of Nguyen Thi Thuy Nhi (2016)
This study analyzes the Fama French five-factor model and the Q-factor model (four-factor model of Hou) using data from the HOSE and HNX stock exchanges between January 2009 and June 2015, employing three portfolio division methods The findings indicate that the market factor positively influences the model, with the SMB factor showing a positive correlation for small-cap stocks and a negative one for large-cap stocks Additionally, HML is positively associated with high book-to-market portfolios, RMW shows a positive relationship with high return on equity, and CMA is positively linked to low operating profit portfolios Overall, the model's explanatory power increased from 80% to 96%, leading to the conclusion that the Fama French five-factor model outperforms the Q four-factor model.
Research of Huynh Ngoc Minh Tram (2017)
The study reveals that the SMB factor, along with the MRP market return factor, significantly contributes to explaining the expected returns of stocks, with coefficient estimates that are statistically significant at the 5% level Notably, the SMB and MRP factors are expected to remain positive, while the HML factor approaches negativity, indicating that companies with smaller sizes and lower book-to-market ratios tend to achieve higher returns Conversely, other factors such as RMW and CMA are not statistically significant, suggesting that the Fama-French five-factor model does not fully capture the rate of return in the Vietnam stock market Nonetheless, the MRP, SMB, and HML factors demonstrate a positive relationship with stock profits in Vietnam.
DATA AND METHODOLOGY
Data construction and processing method
This study analyzes industrial companies listed on HOSE and HNX over a six-year period from January 2012 to January 2018, utilizing quarterly data To ensure the reliability of the sample, only companies listed before 2012 were selected, guaranteeing access to complete public information Following the methodology of Ferson and Locke (1998), which indicates that historical averages yield better market return forecasts than a 60-month average, the research employs the average factor return from the entire data period to estimate expected factor returns Ultimately, the study identifies 100 industrial companies, covering 24 quarters within the specified timeframe.
According to Trinh Minh Quang (2017), the study utilizes data from Thomson Reuters, with controlling and independent variables sourced from company annual reports and financial statements, as well as stock prices from Vietnam's official stock exchange websites for Ho Chi Minh and Hanoi The primary objective of this research is to test the Fama French five-factor asset pricing model among listed industrial companies in the Vietnamese stock market.
The data utilized in this analysis includes key model variables such as total outstanding shares, total assets, net profit after tax, daily stock closing prices, book value, market value, the VN-Index, and Treasury bill rates, all sourced from the Reserve Bank of Vietnam's website, with a focus on a three-month term.
During the initial phase of the research, a significant volume of raw data from industrial companies was collected, which included dead stock information and instances of missing data types and errors To ensure data accuracy and reliability, it is essential to follow a four-step process to eliminate these inaccuracies.
Using the database from Step 1, I calculate the rate of return for each stock, the market portfolio's return, the book-to-market (B/M) ratio, the size measure (calculated as Market Equity multiplied by total outstanding shares), the operating profit (Return on Equity), the investment trend (growth of total assets), and the risk-free interest rate based on Vietnam Treasury bills.
Data collecting Data filtering Data processing
Deviding and setting up portfolios
Carrying out the tests and running regression
Analysis of research results and give conclusion xxxv
Step 4: Dividing and setting up portfolios
Based on the 4 quotas including Size, B/M ratio, OP and Inv to divide all the selected stocks into 18 portfolios which will be shown details in the following part
Utilize Microsoft Excel to analyze and quantify five key factors in the model: SMB (Small minus Big), which measures the return difference between small and large stocks; HML (High minus Low), representing the return difference between high and low book-to-market ratio stocks; RMW (Robust minus Weak), indicating the return difference between robust and weak profitability stocks; and CMA (Conservative minus Aggressive), reflecting the return difference between conservative and aggressive investments Additionally, calculate the complements of these factors, conduct statistical descriptions, and investigate the correlations among them.
Step 6: Carrying out the tests and running the regression model
Utilize Stata software to import sorted portfolios from Excel, effectively manage multicollinearity, autocorrelation, and heteroscedasticity Execute regression models and assess the efficiency of these models across the selected range of companies.
Step 7: Analysis of research results and give conclusions
The regression analysis reveals the influence of various factors on the rate of return By compiling and comparing these regression results, we can identify the most suitable model and classification This assessment will ensure the model's appropriateness and provide valuable recommendations for investors and company stakeholders.
This study mainly uses Stata version 13 in conducting data analysis and providing regression results Microsoft Office Excel is used to organize and utilize the sample data xxxvi
Model
The model for time-series regression:
The expected return on portfolio i for period t is influenced by the risk-free interest rate of government bonds, the excess market return, and the Small minus Big (SMB) common risk factor introduced by Fama and French in 1993 The SMB factor captures the average return difference between small-cap and large-cap stocks Portfolios are constructed each quarter based on market capitalization, using accounting data from the fiscal quarter ending in the previous period.
The High minus Low (HML) factor, introduced by Fama and French in 1993, is a common risk factor that measures the return difference between high and low book-to-market ratio stocks This factor is calculated by forming portfolios each quarter based on the book-to-market ratios derived from accounting data for the fiscal quarter ending in the previous period.
The Robust minus Weak (RMW) factor, introduced by Fama and French in 2015, quantifies the return difference between high and low profitability stocks on day t This factor is calculated by forming portfolios each quarter based on profitability, which is assessed using accounting data from the fiscal quarter ending in t -1.
The Conservative minus Aggressive (CMA) risk factor, introduced by Fama and French in 2015, measures the return difference between portfolios of conservative and aggressive stocks These portfolios are created quarterly, using the growth of total assets from the previous fiscal quarter, divided by total assets at the end of that quarter.
The filtering process in this study was conducted in a manner similar the guidelines proposed by Ince and Porter (2006) in order to make the sample applicable for analysis
The process began by downloading static constituent list data from the Ho Chi Minh City (HCMC) and Hanoi stock exchange websites, ensuring that all relevant stock information was included Data was collected for all available stocks on the HSX and HNX in the Vietnamese stock market The HCMC stock exchange features around 101 industrial stocks, encompassing both active and inactive stocks, while the Hanoi stock exchange data complements this information.
The analysis began with a raw sample of 120 industrial stocks, detailed in Appendix A, which was subsequently refined by filtering static stock information from both stock markets The initial step eliminated investment companies and non-equity firms that derive returns from financing or financial statements of other companies Step 1 documented the number of stocks remaining after this filtering process, while Step 2 further excluded stocks with non-applicable time-series data due to failed download requests In Step 3, stocks exhibiting non-stationary data throughout the sample period were removed, ensuring the reliability of the analysis Finally, Step 4 presented the final research sample, consisting of 100 stocks after addressing multiple instances of the same stocks.
Table 2: Number of stocks in filtering stages
Constituent List RAW Step 1 Step 2 Step 3 Step 4
Details of sorting values cleaning:
To ensure the availability of comprehensive public information, only companies listed on the stock market from the beginning of 2012 or earlier are selected, as they must be listed for a minimum of six months (two quarters) before announcing capital assets and financial reports This criterion guarantees that the sample includes companies with complete data spanning six years (24 quarters) from 2012 to 2018.
Financial and insurance companies are excluded from the sample due to their distinct activities, financial policies, capital structures, and accounting document systems, which differ significantly from those of non-financial companies.
Companies that have gone through restructuring in the time period are also eliminated because their value and size can have significant change, as consequence, affect industrial companies business performances
The Fama and French methodology (1993, 2015) is utilized to define and compute key variables At the end of each quarter (quarter t), companies are sorted and assigned to portfolios based on four critical factors: market capitalization, book-to-market equity, profitability, and investment.
The stock return is calculated as the average rate of return 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 the previous quarter.
𝑡 quarterly rate of return of stocks is calculated as follows:
Market return, represented by the VN-Index, is calculated on a daily basis and averaged quarterly This metric reflects the average daily rate of return for the VN-Index in a given quarter, comparing it to the VN-Index from the previous quarter.
1, the daily rate of return is calculated as follows:
The risk-free rate of return, representing the "Market," refers to the principal interest rate of a 1-year Treasury bill issued by the Reserve Bank of Vietnam from January 2012 to December 2017 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:
Book-to-market equity, or B/M value, is a financial metric that compares a firm's book value—derived from its accounting records—with its market value, which is determined by its market capitalization in the stock market This ratio, calculated at the end of each quarter, provides insights into the relative valuation of a company's equity.
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:
Beginning in January 2012, this study initially categorizes stocks into five portfolios based on cutoffs established by Brailsford et al (2012) The subsequent phase involves constructing sorted portfolios to facilitate the calculation of Fama and French factor return series.