French Five-factor Research Articles

Feature importance in linear models with ensemble machine learning: A study of the Fama and French five-factor model

This study explores key considerations for interpreting feature influence and importance in Machine Learning (ML) for financial models that commonly assume linearity. Simulations demonstrate that ML techniques, including Random Forest, XGBoost, and CatBoost, may produce misleading feature importance ranks when the underlying model is linear. We empirically examine the Fama–French five-factor model using U.S. monthly data from July 1964 to June 2024. While the most important factors are consistently identified, the ranks of moderately important factors vary depending on the estimation method. These results highlight the need for a critical application of ML in financial modeling when the purpose is interpretability.

Empirical insights into the distress risk anomaly: evidence from India

Purpose This paper aims to examine the relationship between financial distress risk and stock returns in the Indian context. Design/methodology/approach This is an empirical study wherein the Altman-Z score is used to identify the distressed and the non-distressed firms listed on Nifty 500. The author uses the Fama–French five-factor model to study the relationship between stock returns and distress risk. The study analyses the differences in the factor loadings among the portfolios sorted by distress. It evaluates if incorporating distress risk factors in conventional pricing models enhances the goodness of fit. Findings The study reported a positive relationship between the distress risk factor and stock returns in the distressed portfolios, signifying that distress risk is a systematic risk only for distressed portfolios. Furthermore, after including the financial distress risk premium, the observed fluctuations in the small-minus-big (SMB), high-minus-low (HML), RMW and CMA coefficients indicate a common association with distress risk-related information. Originality/value This study tests the Fama–French five factors for distress risk and examines its nature in asset pricing for emerging markets like India. The study examined the performance of the augmented Fama–French five-factor model across different sets of portfolios sorted based on distress.

The Impact of COVID-19 on the Fama-French Five-Factor Model: Unmasking Industry Dynamics

This analysis investigates the performance and underlying dynamics of the Fama–French Five-Factor Model (FF5M) in the context of the COVID-19 pandemic, exploring its implications on the U.S. stock market across 30 industries. Our findings reveal marked shifts in the significance of factors. The SMB (size) gained in strength, while the HML (value) factor rose and fell in response to shifting flight-to-quality, liquidity, and inflation concerns. Both the RMW (profitability) and CMA (investment) factors saw a decline in their overall significance during the pandemic. Our results illustrate the oscillation of investor preferences from 2018 to 2023, capturing three distinct periods: pre-COVID-19, COVID-19, and post-COVID-19.

Energy-Related Uncertainty and Idiosyncratic Return Volatility: Implications for Sustainable Investment Strategies in Chinese Firms

This study examines the impact of energy-related uncertainty on idiosyncratic volatility (IVOL) in Chinese firms, leveraging data from the Shanghai and Shenzhen stock exchanges between 2007 and 2022. Utilizing the Energy-Related Uncertainty Index (EUI) and the Fama–French five-factor model, we analyze a comprehensive dataset of 20,998 firm-year observations to understand how macroeconomic uncertainties specific to the energy sector influence firm-specific risk. Our findings reveal that a one-unit increase in the EUI is associated with a 5.1% rise in idiosyncratic volatility across all firms, underscoring the significant impact of energy-related uncertainty on firm-specific risks. The effect is more pronounced in energy-related firms, where a one-unit increase in the EUI leads to a 6.4% increase in IVOL, compared to a 3.7% increase in non-energy-related firms. By incorporating industry-wise, heterogeneity, and phase-based analyses, our findings reveal significant variations in the EUI’s impact across energy and non-energy sectors. State-owned enterprises, firms with high ownership concentration, and smaller firms are more vulnerable to energy uncertainties. Additionally, the effect of the EUI on IVOL is more pronounced during periods of high uncertainty. These insights have important implications for sustainable investment strategies, risk management, and policymaking, providing a deeper understanding of the intricate dynamics of energy markets in fostering sustainable economic growth and development.

Investigate The Influence Of The Fama and French Five-Factor Model On The SRI-KEHATI Stock Index

The capital market functions as an intermediary for the flow of funds between investors and companies, in accordance with the general principle that the greater the risk, the greater the potential profit. In estimating returns, there are several methods, one of which is the Fama French five factor model which was developed from the CAPM, APT and Fama French three factor models. This research aims to evaluate the relationship between the five-factor Fama French model and excess returns on the SRI-KEHATI stock index from 2015 - 2022. This research method adopts quantitative analysis with an associative causality approach and uses a purposive sampling technique to select 9 companies as samples. Data analysis used by Eviews 12. Study findings show that market factors have a significant influence on excess returns, while size, value, profitability and investment factors are not significant on excess returns. Overall, the Fama-French model's five factors collectively influence excess returns. This finding confirms that market conditions will influence the excess returns obtained by investors.

Reliability and validity of the French Five-Factor Narcissism Inventory–Short Form.

Reliability and validity of the French Five-Factor Narcissism Inventory–Short Form.

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.

The five-factor model analysed by machine learning classification techniques

ABSTRACT This study compares the classification performance of traditional factor models and ML to explore the assumption of linearity in risk premiums. We apply ML techniques to the variables used in the Fama – French five-factor (FF5) model to classify the top 30% and bottom 30% of stocks based on whether their prices are expected to increase (winners) or decrease (losers), respectively, over a specified time period. The analysis showed that the ML model’s classification Accuracy outperforms the FF5 classification, which suggests that the higher returns obtained using the FF5 model can be attributed to its effectiveness in classifying stocks with exceptionally high or low returns, rather than the comprehensive use of its variables for overall classification purposes.

Fama and French Five- Factor Study of Stock Market in Indonesia

In general, contemporary finance theories agree that the Fama and French Five-Factor model provides a more comprehensive explanation for average stock returns compared to its predecessors. Previous research on the five-factor model in Indonesia has yielded inconclusive results, and none of the studies has attempted to compare the significance of the five factors over shorter and longer periods, or even within shorter periods. As a result, the researchers of this study endeavor to ascertain the importance of the five elements – the profitability (RMW), market, size (SMB), profitability (RMW), book-to-market (HML), and investment (CMA) factors – to an excess return on the portfolio over shorter and longer periods. The findings indicate that SMB and CMA factors exhibit statistically insignificant, significantly negative, and significantly positive correlations with excess portfolio return, respectively, over the three shorter periods and the longer period. A significant negative correlation is observed between the HML factor and excess portfolio return over the longer period, while the relationship is deemed insignificant over the three shorter periods. No significant relationship was found between the RMW factor and excess portfolio return over the (2005-2019) period and two (2010-2014, 2015-2019) periods, but one shorter period is significantly positive.

Industry momentum and trading volume: evidence from China

PurposeAs the world's largest emerging market, the evidence of momentum effect in China is also mixed. Meanwhile, prior studies mainly examined individual stock momentum in China, with little concern for industry momentum and its relationship with trading volume. The motivation of this study is to investigate industry momentum in China and examine whether trading volume can enhance its profitability.Design/methodology/approachFirstly, the authors test the existence of industry momentum in China; secondly, the authors test the correlation between trading volume and momentum returns using the double ranking method; finally, the authors test whether trading volume enhances the momentum returns using Fama–French five-factor model.FindingsThe authors find that there is a significant industry momentum effect in China, and the momentum returns jointly come from winner and loser portfolios. The intervals between the formation and holding periods have an impact on the performance of momentum portfolios. In terms of trading volume, the authors find that high-volume industries have industry momentum effects while low-volume industries do not. The industry momentum strategies achieve higher excess returns in high-volume industries.Practical implicationsPrior literature found higher momentum returns in low-volume stocks in China, but the research in this study suggests that implementing an industry momentum strategy in low-volume industries will miss out on higher returns or even bring losses, and instead the investors should invest in high-volume industries to get the best performance.Originality/valueThis study extends existing research by focusing on industry momentum and its relationship with trading volume in the Chinese stock market and finds an interesting relationship between industry momentum returns and trading volume, which is different from related studies.

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.

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.

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.

A Five-Factor Asset Pricing Model of Shariah Compliant Firms in the United States

Shariah compliant firms operating in an environment with little to no access to a robust Islamic capital market (such as in the United States (US) stock market) will exhibit a consistent bias towards certain corporate financial behaviour. Does this bias subsequently lead to a skewed asset pricing behaviour? To answer this question, this paper investigates the asset pricing behaviour of multiple samples of Shariah compliant firms listed in the US as compared to an overall conventional sample by employing the Fama & French Five-Factor Model. By applying contemporary Shariah stock screening methodology on a sample of all stocks listed in the NYSE, NASDAQ and the IEX from January 2000 to December 2019, this paper shows that asset pricing behaviour differs not only between Shariah compliant and conventional samples, but also amongst Shariah compliant samples themselves. Ultimately, this paper shows that when deriving the appropriate expected return for Shariah compliant portfolios in the US, there are evidence to suggest that the Fama & French Five-Factor model is more suitable compared to the traditional Capital Asset Pricing Model (CAPM) since the additional risk premiums show consistent significance across groups of Shariah compliant firms.

Disseminating information across connected firms — Analyst site visits can help

When information is disseminated across fundamentally connected firms at a fast pace, limited investor attention should be mitigated. We investigate whether analyst site visits to public firms have any impact on the lead–lag effect, a well-known phenomenon induced by limited investor attention. We find that analyst site visits to a focal firm’s connected firms almost eliminate the lead–lag effect between the visited connected firms and the focal firm at weekly frequency. The Fama–French five-factor alpha drops from 0.365% (t-statistic=3.26) per week for the strategy based on lagged returns of a firm’s non-visited connected firms to -0.082% (t-statistic=-0.97) per week for the strategy based on lagged returns of visited connected firms. We further provide evidence that analyst site visits to connected firms help improve forecast accuracy for the forecasted firm. These results indicate that analyst site visits help disseminate information across connected firms.

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.

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.

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.

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.