Fama-French Five-factor Model Research Articles

Media sentiment and stock returns

Based on 35,344 news articles published in the Financial Times that cover 40 companies that have been included in the Dow Jones Industrial Average, we find that a negative media sentiment in the form of a negative language tone in news articles is a priced factor in five of nine asset-pricing models that aim to explain the cross-section of stock returns. In particular, the sentiment factor is a priced factor in the market model augmented with the sentiment factor in all three samples—the 2005–09 subsample, the 2010–18 subsample, and the 2005–18 full sample—and in the Fama-French three- and five-factor models augmented with the sentiment factor in the 2010–18 subsample. In addition, factor-spanning regressions with the Fama-French five-factor model as the right-hand-side model confirm that the sentiment factor contributes to the model’s explanation of the stocks’ mean excess returns in the 2005–09 subsample and the 2005–18 full sample.

The sustainability factor in asset pricing: Empirical evidence from the Indian market

This study investigates the feasibility of including the sustainability performance of firms in the asset pricing problem. The data of 500 firms from the NIFTY 500 index are used for this study. The stock prices and financial data are downloaded from the CIME database. The sustainability factor is computed using the ESG scores from the Bloomberg database. In order to test the influence of the sustainability factor, the Fama–French Five-Factor model is extended by including the sustainability factor as an additional factor. The dependent variables are the excess returns on 36 size and book-to-market ratio sorted portfolios, 36 size and operating profitability sorted portfolios, and 36 size and investment sorted portfolios. The impact of the sustainability factor on excess portfolio return is tested using the Fama–MacBeth two-pass regression and the Fama–French methodology. The results show that the price of ESG risk (or ESG risk premium) is positive, indicating that firms with lower ESG performance yield more returns than those with higher ESG performance. About one-third of the portfolios witness the significant impact of the sustainability factor on their returns. However, the insignificant relationship in two third of the portfolios between the sustainability factor and excess portfolio returns conveys that in the Indian market, corporate investors have the flexibility to decide on ESG investment. Smaller firms are exposed to a higher ESG risk, and Firms which do not integrate environmental and social costs into their strategies may bear a higher cost of equity.

Lottery factor and stock returns: Evidence from India

This study examines important aspects of lottery behavior in India using data from December 2001 to March 2021. We experiment with a new measure of lottery along with well-established measures. Our lottery measure focusing on more recent information is found to be appropriate for India. MAX has a unique role in predicting returns that is not subsumed by other risk measures. We find MAX, skewness, tail risk, and idiosyncratic volatility as relevant characteristics of lottery stocks. Using these, we construct a lottery factor representing investors’ risk-seeking behavior. This behavior is an outcome of mis-weighing probability of extreme gains or losses and leads to overreaction to attention-catching events. We augment the Fama-French five-factor model with our lottery factor. Our six-factor behavioral asset pricing framework is found to be an appropriate performance benchmark. Lottery behavior is mainly the result of retail investor actions and is linked to several behavioral biases.

Comparison Between Different Pricing Models: Evidence from the Technology Industry

The primary goal of this study is to investigate the applicability and analytical effectiveness of three models, namely the Capital Asset Pricing Model (CAPM), the Fama-French Three-Factor Model, and the Fama-French Five-Factor Model, within the technology industry. The study focuses on a sample of six representative technology companies, employing monthly data spanning a period of five years from 2018 to 2023. Empirical tests and regression analyses are conducted to empirically assess the performance of these three models. The results demonstrate that the Fama-French Three-Factor Model exhibits superior fitting performance compared to the CAPM model, as initially hypothesized. However, when comparing the fitting performance of the Fama-French Five-Factor Model to the Fama-French Three-Factor Model, the study reveals that the former does not exhibit a significant improvement and even shows a slight decrease in fitting effectiveness. For the technology industry, the Three-Factor Model effectively captures systematic risk and accounts for a sizable proportion of the variation in regressions. Additional factors in the Five-Factor Model might introduce additional complexity without significantly enhancing the model's explaining extent. Overall, this study underscores the importance of model selection and raises awareness about the trade-off between model complexity and explanatory capability, particularly in the technology industry.

Testing multifactor asset pricing models in the stock market

lthough the superiority of Fama-French (FF) five-factor model in capturing the United States (US) equity returns, this model performs poorly in other stock markets (Fama & French, 2017). Using the monthly data of nearly 600 Vietnamese published firms from 2008 to 2022, the primary purpose of this paper is to analyze and examine the performance of four famous multifactor asset pricing models: the capital asset pricing model (CAPM), the Carhart four factor model, and the FF three-factor and five-factor models. We document the preference for the Carhart four-factor model over other models in producing a precise description to Vietnamese stock returns. The CAPM cannot give a reasonable explanation to the variation of Vietnamese stock returns, implying that market risk only accounts for a small proportion of the risk of holding Vietnamese stocks. Furthermore, adding the profitability and investment factors does not improve the explanatory power of asset pricing models in Vietnam, inconsistent with the result reported in the US stock market (Fama & French, 2015, 2020).

An Analysis of the Applicability of Pricing Models in the Chinese Stock Market

The stock market in China has an important position in the world, but it has not been able to find a pricing model suitable for the market. This paper reviews the history and development of various pricing models, analyzes relevant research on their application in the Chinese stock market, and discusses the applicability of various pricing models. It is found that the applicability of the capital asset pricing model (CAPM) in China is low; the applicability of the Fama-French three-factor model and the Fama-French five-factor model is better than CAPM, but there is significant controversy over the advantages and disadvantages of the two. It is also found that the applicability of the five-factor model may have regional differences in different markets, and there may be differences in redundant factors in different markets. It is proposed that future research should obtain more accurate market data, explore new influencing factors, and build a pricing model more suitable for the stock market in China by combining the characteristics based on more advanced data analysis technology.

A two-dimensional innovation activity factor and stock pricing: Evidence from the Chinese stock market

This paper confirms that innovation activities can bring about excess stock returns in the Chinese stock market. Using monthly data on A-share stocks from May 2007 to December 2022, we find a significant innovation premium in the Chinese stock market by constructing a composite indicator that includes both innovation input and output. Then, we propose a six-factor model by incorporating the innovation factor into the Fama-French five-factor model. Time-series regressions show that the proposed model works well in explaining the variation of excess stock returns, and the innovation factor makes a significant marginal contribution to the explanatory power. Further tests show that the results remain robust for value-weighted returns, individual stocks, and different boards. The proposed model effectively reduces the root mean square errors compared to traditional pricing models. Our study adds significant evidence verifying the value of innovation in financial markets, and also provides practical implication for better enterprise innovation managment and public services.

What difference do new factor models make in portfolio allocation?

This paper compares the Hou-Xue-Zhang four-factor model with the Fama-French five-factor model from an investing perspective both in- and out-of-sample. Without margin requirements and model uncertainty, the Hou-Xue-Zhang model outperforms the Fama-French model. However, the outperformance could become negligible if an investor is subject to margin requirements and model uncertainty. The Hou-Xue-Zhang model shows similar power as the Fama-French model in describing the covariance matrix of asset returns. Overall, the two models do not make a difference for investing in a realistic setting.

Using the Capital Asset Pricing Model and the Fama–French Three-Factor and Five-Factor Models to Manage Stock and Bond Portfolios: Evidence from Timor-Leste

Timor-Leste is a new country still in the process of economic development and does not yet have a capital market for stock and bond investments. These two asset classes have been invested in international capital markets such as the US, the UK, Japan, and Europe. We examine the performance of the capital asset pricing model (CAPM) and the Fama–French three-factor and five-factor models on the excess returns of Timor-Leste’s equity and bond investments in the international market over the period 2006 to 2019. Our empirical results show that the market factor (MKT) is positively and significantly associated with the excess returns of the CAPM and the Fama–French three-factor and five-factor models. Moreover, the two variables Small Minus Big (SMB) as a size factor and High Minus Low (HML) as a value factor have a negative and significant effect on the excess returns in the Fama–French three-factor model and five-factor model. Further analysis revealed that the explanatory power of the Fama–French five-factor model is that the Robust Minus Weak (RMW) factor as a profitability factor is positively and significantly associated with excess returns, while the Conservative Minus Aggressive (CMA) factor as an investment factor is insignificant.

Impact of H1N1, H7N9, ASFV, dengue virus and COVID-19 on pharmaceutical manufacturing firms' R&D investments and economic consequences: Evidence from China

Objective: To determine the impact of major disease epidemics on pharmaceutical manufacturing firms' Research & Development (R&D) investments and economic consequences. Methods: The sample consists of 1 582 firm-year observations from 2009 to 2022 in China, of which, 26.6% of pharmaceutical companies are involved in the diagnosis and treatment of prevalent diseases. Linear models using R&D investments, patent applications, operating performances and stock returns as dependent variables are constructed separately to examine the response of pharmaceutical companies to disease epidemics and the resulting economic consequences. Results: The prevalence of five major diseases led to a 17.5% increase in the amount of R&D investment and an 87.8% rise in the ratio of R&D investment to total assets by disease-related pharmaceutical companies, compared to unrelated pharmaceutical companies. Further evidence indicated that the patent applications for disease-related firms increased by 44.3% relative to unrelated firms after the epidemics. Though the impacts of the epidemics on firms' operating performances were insignificant in the short term, a major disease epidemic was associated with an increase in stock returns of 67.4% and 44.6%, respectively, as measured by the capital asset pricing model and Fama-French five-factor model. Additional analysis revealed that the impacts of the epidemics on R&D investments and patent applications were more pronounced for non-state-owned enterprises than state-owned enterprises. Conclusions: This study demonstrates that disease-related pharmaceutical firms respond to the disease epidemics through increasing R&D investment. More patent applications and higher market value are the main gains from the firms' increased investments in R&D following the epidemic, rather than the improvements of short-term operating performances.

Empirical Study and Reflection on the Impact of COVID-19 Pandemic on Chinese A-share Market--Based on Fama French Five-factor Model

The COVID-19 pandemic has caused severe imbalances in the global economy and intensified stock market turmoil worldwide. Based on Fama-French five-factor model, this paper studies Chinas A-share market changes before and after the epidemic. The stocks treated by ST (special treatment) and * ST (delisting warning) are screened, excluding companies and banking stocks with shortlisting time. The equities are finally divided into groups using the two-by-three grouping approach. According to the median of the total market value of stocks, the entire sample is separated into two categories: modest market value (S) and large market value (B). The samples are classified into three categories based on the book-to-market ratio's 30% and 70% quantiles: high (H), medium (N), and low (L). Comparing the different times of the impact of the five factors on stock earnings is conducive to explaining the changes in the factors affecting the stock price of listed companies before and after the epidemic. It is possible to analyze which factors have declined and fluctuated after the epidemic, resulting in a decline in stock prices, a slowdown in the growth of listed companies, or even a recession. The analysis of the five-factor model can clarify which factors have led to the decline in stock prices after the beginning of the epidemic, which is conducive to the formulation of related policies and plans by listed companies and makes up for the decline in stock prices and the slowdown in company growth caused by the corresponding decline, which has apparent practical value.

An Empirical Investigation of the Fama-French Five-factor Model in North America before and during COVID-19

This study takes an investigation in the North American financial market before and dur-ing the epidemic using a five-factor model to compare the performance of the regression models and the significance of the factors before and during the epidemic and to analyze the changes in the North American financial market before and after the epidemic. The study selects representative companies in each sector from the top ten companies in the North American financial market. It performs regressions on each company before and af-ter the epidemic to compare the differences in the significance of the factors and the rea-sons for the differences between the companies in different sectors.

Moments and momentum in the returns of securitized real estate: A cross-country study of risk factors driving real estate investment trusts before and during COVID-19

A real estate investment trust (REIT) is a company running a funding pool that allows people to invest in real estate without physical purchase. Since REITs are stock market-traded real estate assets, there is debate as to whether their returns are driven by stock market risk factors. In this regard, this paper examines the impact of the well-established equity market risk factors of momentum, skewness, and kurtosis on the returns of different types of REITs, including mortgage REITs (MREIT), equity REITs (EREIT), and hybrid REITs (HREIT), across five countries—Australia, the UK, the US, Japan, and Canada—during the period 2000–2022, controlling for other well-established factors in the asset pricing literature. The study first adds the skewness and kurtosis to analyze cross-national REIT returns via the Fama–French five-factor model. Next, the cross-national REIT dataset is built for the different periods and then tested for the robustness of the effect of the factors during the COVID-19 period. Findings indicate that the influence of momentum on the return of the REITs is consistently positive across countries and different types of REITs. However, the significance of momentum for different REITs in different countries varies. These results were robust during the COVID-19 period, providing further confirmation that REITs behave less like stocks rather than real estate investments, with significant implications for investors.

The Fama–French Five-Factor Model with Hurst Exponents Compared with Machine Learning Methods

Scholars and investors have been interested in factor models for a long time. This paper builds models using the monthly data of the A-share market. We construct a seven-factor model by adding the Hurst exponent factor and the momentum factor to a Fama–French five-factor model and find that there is a 7% improvement in the average R–squared. Then, we compare five machine learning algorithms with ordinary least squares (OLS) in one representative stock and all A-Share stocks. We find that regularization algorithms, such as lasso and ridge, have worse performance than OLS. SVM and random forests have a good improvement in fitting power, while the neural network is not always better than OLS, depending on the data, frequency, period, etc.

Influencing factors of stock returns based on Fama–French model and intelligent algorithm

Due to the large number of stock companies, complex stock categories and inconsistent evaluation standards of market value, it is not conducive to the choice of investors. Based on the theory and practice of traditional stock performance evaluation model, this paper integrates the algorithm thought structure of Fama–French five-factor model and proposes a machine learning algorithm model for stock performance research. In addition, this paper also builds a model that can evaluate the style and timing ability of fund managers to improve the fund performance evaluation system to a greater extent. With the help of the performance evaluation updating function of the model, it provides a new experience material in the empirical research composition of the fund performance field. In the system test module, the former prediction data and the actual experimental data are integrated, and the two are sorted out and compared, which proves the feasibility and effectiveness of the proposed algorithm model. The final experimental results verify the usefulness of the model in stock return prediction and analysis of influencing factors.

Challenging Hedge Funds by Mimicking Their Strategies

Since the launching of the first hedge fund, its ability to have a greater return with less volatility has attracted more and more people to get involved in this investment tool. Numbers of people either invest in hedge funds, or simply simulate the hedge fund strategy in order to obtain a higher return. This paper studies whether people can beat hedge funds by simply mimicking hedge funds strategies. The results are shown by comparing data from Goldman Sachs Hedge Industry VIP ETF, which stands for GVIP, and the long-short hedge fund index from November 2016 to July 2022. According to yahoo finance, GVIP seeks to invest 80% of its assets in its underlying index, depositary receipts representing securities included in its underlying index, and in underlying stocks in turns of depositary receipts included in its underlying index. First of all, the statistical return of GVIP and the long-short hedge fund index are analyzed in terms of their mean, standard deviation, and sharpe ratio. The number of months that GVIP outperforms hedge funds, and the maximum return gap are also concluded by using cross-sectional comparison. Secondly, some regressions are applied to obtain the preliminary conclusions of this paper. Thirdly, the FamaFrench five-factor model is applied to improve the accuracy of the model, so as to further analyze and verify the previous conclusions. Based on the statistical analysis, we can conclude that investors cannot beat hedge funds by mimicking their strategies.

Analysis of an event study using the Fama–French five-factor model: teaching approaches including spreadsheets and the R programming language

The current financial education framework has an increasing need to introduce tools that facilitate the application of theoretical models to real-world data and contexts. However, only a limited number of free tools are available for this purpose. Given this lack of tools, the present study provides two approaches to facilitate the implementation of an event study. The first approach consists of a set of MS Excel files based on the Fama–French five-factor model, which allows the application of the event study methodology in a semi-automatic manner. The second approach is an open-source R-programmed tool through which results can be obtained in the context of an event study without the need for programming knowledge. This tool widens the calculus possibilities provided by the first approach and offers the option to apply not only the Fama–French five-factor model but also other models that are common in the financial literature. It is a user-friendly tool that enables reproducibility of the analysis and ensures that the calculations are free of manipulation errors. Both approaches are freely available and ready-to-use.

The beta anomaly and the quality effect in international stock markets

We investigate the beta anomaly and its relationship with stock quality in international stock markets. The beta anomaly exists in three aggregates (Europe, Pacific, and Global) and fourteen of the twenty-two country portfolios. We further demonstrate that stock quality explains the beta anomaly in international markets. The beta anomaly is statistically significant among junk (low-quality) stocks, and it does not exist among quality (high-quality) stocks. The results are robust in portfolio and regression analyses, both before and after controls. Finally, we show that the alphas of the beta anomaly estimated using the Fama–French–Carhart factor as well as Fama–French five-factor models disappear when augmented by the quality-minus-junk (QMJ) factor.

Nonlinear asset pricing in Chinese stock market: A deep learning approach

The redesign of asset pricing models failed to integrate the frequent financial phenomenon that stock markets exhibit a non-linear long- and short-term memory structure. The difficulty lies in developing a nonlinear pricing structure capable of depicting the memory influence of the pricing variable. This paper presents a Long- and Short-Term Memory Neural Network Model (LSTM) to capture the non-linear pricing structure among five elements in the Chinese stock market, including market portfolio return, market capitalisation, book-to-market ratio, earnings factor, and investment factor. The long–short-term memory structure implies that the autocorrelation function of the stock return series decays slowly and has a long-term characteristic. The LSTM model surpasses the standard Fama–French five-factor model in terms of out-of-sample goodness-of-fit and long–short strategy performance. The empirical findings indicate that the LSTM nonlinear model properly represents the nonlinear relationships between the five components.

Analysis of the COVID-19 Pandemic on FAAMG Companies Performances Under the Fama-French Five-Factor Model

The COVID-19 pandemic has led the U.S. stock market to tumble during the last two years. To further discover the effect of the pandemic on specific companies, this paper utilizes the Fama-French five-factor model to analyze the performance changes for FAMMG companies before and after the COVID-19 outbreak. OLS method is applied to the FAAMG companies stock returns to examine the fitness of the model, and the fitness is strengthened after the outbreak. The pandemic causes significant changes in betas of the Fama-French five-factor model, specifically Robust minus Week (RMW) and Conservative minus Aggressive (CMA).