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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. [ABSTRACT FROM AUTHOR]

Technical analyses are popular in investment practice in the Chinese stock market. However, with the in-depth study of technical indicators, trend strategies constructed based on a single window period have been proven to be highly volatile in terms of returns. And also, there is a wide range of academic views on the implementation of trend strategies to achieve excess returns in the Chinese market. Based on these, this paper constructs a trend factor using a moving average model, employs Chinese A-share data from 2010-2023 to verify the effectiveness of the trend factor for single-factor stock selection in the Chinese stock market through empirical analyses, respectively, and combines the trend factor with a traditional asset pricing model to validate the feasibility of the improved Fama-French five-factor model stock selection strategy by adding the trend factor and the pricing ability of the model, and the robustness of the findings is confirmed by robustness tests. The conclusions of the study provide targeted recommendations for individual and institutional investors and have certain theoretical and practical significance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: In asset pricing models, it was traditionally assumed that there is a linear relationship between return and explanatory variables. Therefore, estimating the coefficient in a nonlinear setting would be inconsistent and bias-oriented. In this study, the predictive power of the nonlinear and linear Fama-French Five Factor Model was estimated in the period from March 2010 to March 2020. Methods: The semi-parametric method was used to estimate the nonlinear expected return in FF Five Factor Model. The expected return was also calculated based on the linear FF Five Factor Model. Results: The estimated return was compared with the realized returns. Then the mean absolute percentage error was used to measure the predictive power of research models. The results show that mean of the mean absolute percentage error in the semi-parametric model is lower than the linear model. Conclusion: Despite the lower error of the semi-parametric FF Five-Factor model compared to the linear model, no significant difference was observed between the predictive power of these two models. Therefore, the estimating methods will not have a significant impact on the predictive power of the Five Factor Model.

This study empirically analyzes return data from select energy companies in developed and emerging markets using the Fama-French three- and five-factor asset-pricing models in crisis settings. It researches whether these models are suitable to produce meaningful return data in challenging economic circumstances. We use panel data covering 12 of the largest globally-operating energy companies from Russia, China, the US, the EU, and Saudi Arabia, covering a period between 2000 and 2022. The results undermine the general notion that the usage of available multi-factor asset-pricing models automatically yields meaningful data in all economic situations. The study reiterates the need to reconsider the assumption that the addition of more company-specific factors to regression models automatically yields better results. This study contributes to the existing literature by broadening this research area. It is the first study to specifically analyze the performance of companies from the energy sector in a crisis like the COVID-19 pandemic with the help of the Fama-French three- and five-factor models. [ABSTRACT FROM AUTHOR]

Purpose-The basic purpose of the study is to examine whether Tobin-q, liquidity and momentum risk-premium contributes the explanatory power in terms of explaining portfolio returns in PSX. Design/Methodology-The Weighted Least Square (WLS) regression technique is empirically used to examine the nexus between risk-factor and portfolio returns using PSX dataset. The models provide useful tools for making efficient strategies in the jurisdiction of investments and portfolio constructions. Findings-The study reveals that multidimensional liquidity exhibits weak significant results while Tobin-q and momentum risk-factors demonstrate statistically significant determinants for PSX. Furthermore, WLS regression produces robust coefficient results than OLS regression as except liquidity all the factors exhibit substantially improved results. Practical Implications-The study findings would be useful for stocks and portfolio managers constructing optimal and diversified portfolios while investing in PSX. [ABSTRACT FROM AUTHOR]

Recognising and investigating stock return behaviour has always been one of the most critical issues in scientific and investment communities. In recent years, factor models have been used in many studies related to stock return prediction. This research is based on a six-factor model, including the Fama-French five-factor model plus the market fragility factor. The explanatory power of this model has been examined in the Tehran securities market from 2009 to 2018 for 117 companies. The results show that the explanatory power of the six-factor model is better than the Fama-French five-factor model in the Iranian capital market. The results also suggest that market fragility has a significant negative relationship with stock returns. Policymakers can consider this result in financial and investment issues and people interested in this issue.

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.

Understanding risk-adjusted returns in real estate investment are crucial, but little is known about the risk-adjusted returns for direct real estate. This paper examines risk-adjusted total returns by developing an extended capital asset pricing model (CAPM) to investigate whether direct real estate returns compensate for their risk levels. Based on a panel dataset of the residential property transaction in 62 Territorial Authorities of New Zealand from 2002Q1 to 2018Q4, a direct real estate portfolio performance in the single-factor CAPM model is compared with the national housing markets stock markets and REITs markets in New Zealand before the pandemic. The results demonstrate that the direct real estate returns outperform the market returns with a significant positive alpha and beta smaller than one but positive. The alpha is further evaluated by the five-factor CAPM model, which includes the factors of liquidity risk, value risk, time risk, credit-rating risk, and currency risk. The assessment shows that most of the excess return (alpha) can be attributed to direct real estate market risks. [ABSTRACT FROM AUTHOR]

Objective: In asset pricing models, it was traditionally assumed that there is a linear relationship between return and explanatory variables. Therefore, estimating the coefficient in a nonlinear setting would be inconsistent and biasoriented. In this study, the predictive power of the nonlinear and linear FamaFrench Five Factor Model was estimated in the period from March 2010 to March 2020. Methods: The semi-parametric method was used to estimate the nonlinear expected return in FF Five Factor Model. The expected return was also calculated based on the linear FF Five Factor Model. Results: The estimated return was compared with the realized returns. Then the mean absolute percentage error was used to measure the predictive power of research models. The results show that mean of the mean absolute percentage error in the semi-parametric model is lower than the linear model.Conclusion: Despite the lower error of the semi-parametric FF Five-Factor model compared to the linear model, no significant difference was observed between the predictive power of these two models. Therefore, the estimating methods will not have a significant impact on the predictive power of the Five Factor Model. [ABSTRACT FROM AUTHOR]

Recognising and investigating stock return behaviour has always been one of the most critical issues in scientific and investment communities. In recent years, factor models have been used in many studies related to stock return prediction. This research is based on a six-factor model, including the Fama-French five-factor model plus the market fragility factor. The explanatory power of this model has been examined in the Tehran securities market from 2009 to 2018 for 117 companies. The results show that the explanatory power of the six-factor model is better than the Fama-French five-factor model in the Iranian capital market. The results also suggest that market fragility has a significant negative relationship with stock returns. Policymakers can consider this result in financial and investment issues and people interested in this issue. [ABSTRACT FROM AUTHOR]

This paper aims to analyze whether the liquidity risk is priced in Latin-American emerging stock markets. For that, we test the performance of the liquidity augmented version of Fama-French three and five factor models and Carhart four factor model since there is not yet a consensus about their suitability for these markets. Two versions of a liquidity factor were constructed based on two proxies that consider different dimensions of liquidity and are more appropriate for low frequency data. The GRS statistics showed Latin American average returns are better explained by the liquidity augmented Fama-French five-factor model. When estimated by GMM-IVd, due to the possible endogenous problems caused by liquidity, the results of the models did not significantly change. The results were robust to the January Effect. Furthermore, when the sample period was divided into two subperiods, both were statistically significant, although the explanatory power was greater in the second subperiod. [ABSTRACT FROM AUTHOR]

The article deals with evaluating the securities portfolios in the process of transition from the one-factor CAPM model to the Fama-French five-factor model (FF5F). It identifies the advantages of the latter and discusses the controversial issues regarding its use by portfolio investors in different countries, given the anomalies inherent in asset pricing. Besides, the peculiarities of the statistical stratification method used in the FF5F model to group stock portfolios are revealed, and attention is drawn to some of the debating points of the five-factor model. The proposals have been formulated, which offer broader avenues for taking advantage of the FF5F model and increase the validity of the portfolio analysis results. The article also gives recommendations on modifying the approaches to analyzing small-size portfolios versus big-size portfolios based on partial changes in RMW and CMA factors, threshold proportions, and the use of STARR for asymmetric portfolios. The study substantiates the use of these approaches in testing the Fama-French five-factor model with portfolios composed of blue chips.

This study empirically analyzes return data from select energy companies in developed and emerging markets using the Fama-French three- and five-factor asset-pricing models in crisis settings. It researches whether these models are suitable to produce meaningful return data in challenging economic circumstances. We use panel data covering 12 of the largest globally-operating energy companies from Russia, China, the US, the EU, and Saudi Arabia, covering a period between 2000 and 2022. The results undermine the general notion that the usage of available multi-factor asset-pricing models automatically yields meaningful data in all economic situations. The study reiterates the need to reconsider the assumption that the addition of more company-specific factors to regression models automatically yields better results. This study contributes to the existing literature by broadening this research area. It is the first study to specifically analyze the performance of companies from the energy sector in a crisis like the COVID-19 pandemic with the help of the Fama-French three- and five-factor models.

A sales decline period disrupts the time series of earnings and, consequently, reduces their predictability. Such a situation can lead to inappropriate decisions by investors. Therefore, managers need to respond appropriately to negative news resulting from sales decline. Manager response is related to forecasting future sales situations, which could affect risk to the firm. Accordingly, the purpose of this study is to investigate the effect of managers' forecasts of future sales on the risk of companies that have experienced sales decline. In this study, the ratio of the changes in operating profit margin was used to compare companies with optimistic and pessimistic managers. To investigate the research hypotheses, the Fama-French five-factor model was used to depict a period of 11 years, from 2007 to 2017, for the companies that are accepted in the Tehran Stock Exchange. It should be noted that the market beta of the Fama-French five-factor model is distinguished by upside potential and downside risk factors, making it possible to study them individually. The findings imply that in companies with optimistic managers, the upside potential is more than the downside risk, but in companies with pessimistic managers, there is no significant difference between the upside potential and the downside risk. [ABSTRACT FROM AUTHOR]

The fundamental research question associated with the asset pricing framework relates to the risk and return relationship in return predictability. We introduce the human capital component to the Fama–French five‐factor model and derive an equilibrium six‐factor asset pricing model in an intertemporal framework. The study comprises the Japanese monthly time‐series dataset for 24 years spanning November 1990 to December 2017. The Generalized method of moments estimation and Gibbons‐Ross‐Shanken test results confirm that the six‐factor model yields better estimates and equally outperforms the Fama–French three‐factor, Carhart four‐factor, and Fama–French five‐factor models in explaining the variation in excess return on Fama–French variant portfolios. The core results and findings hold when we use labor income growth as an alternate measure of human capital in the six‐factor asset pricing model. [ABSTRACT FROM AUTHOR]

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The asset-pricing models have been a fundamental area of research in finance due to its applicability in corporate finance and stock analysis. The present research attempted to evaluate the three popular asset-pricing models namely the capital asset-pricing model, the Fama-French three-factor model, and the Fama-French five-factor model in the Indian equity market for the period of January 2009 to November 2018. The study also tested the role of the size, profitability, value, investment, and market factors in explaining the average equity returns at the Indian bourses. The empirical results indicate the inferior performance of single market factor in describing the variations in average stock returns in comparison of the FF3FM and FF5FM. Furthermore, the size and value factors added to CAPM yields a vital improvement in explaining the variation in average returns of sample stocks.

We implement the Fama-French five-factor model and enhance it with a momentum factor for the German market using recent monthly data from 2002 to 2019. We construct the factors associated with the market, size, value, profitability, investment, and momentum for the CDAX constituents and examine to what extent this six-factor model captures the return premia in the German market. Our preliminary analysis does not document any significant evidence on the profitability or investment premium. The results on the six-factor model compared with the three-factor model reveal that the additional factors do not add significant explanatory power to the analysis. We conclude that the relevance of the profitability and investment factors within the context of international asset pricing studies cannot be transferred to the country- specific case of the German market. [ABSTRACT FROM AUTHOR]

The asset-pricing models have been a fundamental area of research in finance due to its applicability in corporate finance and stock analysis. The present research attempted to evaluate the three popular asset-pricing models namely the capital asset-pricing model, the Fama-French three-factor model, and the Fama-French five-factor model in the Indian equity market for the period of January 2009 to November 2018. The study also tested the role of the size, profitability, value, investment, and market factors in explaining the average equity returns at the Indian bourses. The empirical results indicate the inferior performance of single market factor in describing the variations in average stock returns in comparison of the FF3FM and FF5FM. Furthermore, the size and value factors added to CAPM yields a vital improvement in explaining the variation in average returns of sample stocks. [ABSTRACT FROM AUTHOR]

This study tests the effectiveness of the Fama and French (2015) five-factor model in explaining returns on the Johannesburg Securities Exchange (JSE). The five-factor model is compared to the traditional Fama-French three-factor model as well as other factor combinations. The results show that the size-value and size-profitability three-factor models best describe time-series returns when comparing models. The five-factor model best explains the cross-section of returns and, overall, the results identify a significant inverse size premium and negative relationship between beta and returns but find a significant value premium. The additional factors of profitability and investment contribute to explaining the returns on the JSE; however, profitability is more consistent than investment. [ABSTRACT FROM AUTHOR]

To sin, or not to sin: that has been the question for many people for a long time, and nowadays that question has moved to the financial markets. The existence of studies that show that investing in vice sectors such as the alcohol, tobacco, and gambling industries, collectively known as the “triumvirate of Sin”, is profitable has created some uncertainty for investors who wonder whether or not to be socially responsible. We show that by implementing an investment strategy based on the Fama–French five-factor model, “saint” investors obtain better portfolio performance, even when transaction costs are taken into consideration, and therefore they are the ones chosen to knock on the door of portfolio performance heaven.

Abstract This paper evaluates whether the new Fama-French five-factor model is able to offer a better description of emerging market equity returns than the three-factor model. Using an extensive sample of 18 countries from three different regions, the paper is the first to test the performance of the five-factor model across a broad range of emerging markets. The five-factor model consistently outperforms the three-factor model in Eastern Europe and Latin America. However, a profitability or investment premium cannot be distinguished in the Asian factors and the five-factor model fails to provide an improved description of equity returns in the region. Highlights • Three- and five-factor models in three emerging markets regions. • All three regions exhibit a strong value premium. • Strong profitability premiums in Eastern Europe and Latin America, but not Asia. • Five-factor model is successful in Eastern Europe and Latin America. • Five-factor does not offer an improvement over the three-factor model in Asia. [ABSTRACT FROM AUTHOR]

This paper evaluates whether the new Fama-French five-factor model is able to offer an improved method for pricing investment risk in UK equity returns. The paper extends previous studies by testing alternative specifications of the profitability factor. The initial tests indicate that a respecified five-factor model—using gross profit rather than operating profit—provides an improved description of UK equity returns. However, the factors tested perform inconsistently when evaluated against different test portfolios and neither the value nor investment premiums are consistently priced. As well as highlighting the importance of testing factor models using an array of different test portfolios, the results show that both the three- and five-factor models are unable to offer a convincing description of UK equity returns and therefore cannot be considered a reliable measure of financial risk. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the risk-adjusted performance of US sector portfolios and sector rotation strategy using the alphas from the Fama-French five-factor model. We find that five-factor model fits better the returns of US sector portfolios than the three-factor model, but that significant alphas are still present in all the sectors at some point in time. In the full sample period, 50% of sectors generate significant five-factor alpha. We test whether such alpha signifies a true sector out/underperformance by applying simple long-only and long-short sector rotation strategies. Our long-only sector rotation strategy that buys a sector with a positive five-factor alpha generates four times higher Sharpe ratio than the S&P 500 buy-and-hold. If the strategy is adjusted to switch to the risk-free asset in recessions, the Sharpe ratio achieved is tenfold that of the buy-and-hold. The long-short strategy fares less well. [ABSTRACT FROM AUTHOR]

Fama and French (FF, 2015) propose a new five-factor asset pricing model that adds profitability and investment patterns to the market, size and value variables used in FF (1992). Our purpose is to investigate this new model using an improved generalized method of moments (GMM)-based robust instrumental variables technique in a fixed-effects panel data framework. To test for measurement errors, we use a modified Hausman artificial regression. We also examine an augmented FF six-factor model that includes the Pástor–Stambaugh (PS, 2003) liquidity factor. Using the FF dataset, our GMM-based panel data approach leads us to conclude that the only consistently significant factor is the market factor. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to evaluate and compare the performances of the Fama-French three- (FF3) and five-factor (FF5) models in the Indonesia stock market. This study also examines whether book-to-market factor (HML) is redundant in explaining the portfolio excess returns in Indonesia. This study employs asset pricing factor of the 2 x 3 sorts and excess returns of 25 Size-B/M, 25 Size-OP, dan 25 Size-Inv portfolios as dependent variables. This study employs Ordinary Least Square (OLS) with monthly time-series data from 2000 to 2015. Based on the average adjusted R2 from the two models, FF5 explains portfolio excess return variations better than FF3, although the profitability and investment factors only display weak effect on the excess returns. If we refer to Merton’s (1973) zero-intercept criterion, the both models are not valid in Indonesia, because most intercepts are significant in each set of 25 portfolios. We also find that book-to-market factor is redundant in describing the variation of returns in Indonesia. The test of intercept difference between Indonesia and The US indicates that there are differences of abnormal return and market efficiency in both countries.

This study examines the oil price risk exposure of U.S. financial and non-financial industries over the period of January 1983 to March 2015. We include the oil price risk factor into the Fama and French [17] five-factor asset pricing model and identify the structural breaks in the equity returns using the test created by Bai and Perron [4]. The oil price risk exposures of financial and non-financial industries are estimated at the subsector level. The results show that the degree of oil price sensitivity differs noticeably across subsectors and over time. The magnitude of oil prices' impact on the financial subsectors is considerably lower than the magnitude of its impact on the non-financial subsectors. Among the financial subsectors, Mortgage Finance and Real Estate Services have the largest negative and positive exposures to oil price risk, respectively. Among the non-financial subsectors, Airlines and Oil Equipment Services have the largest negative and positive oil price risk exposures, respectively. [ABSTRACT FROM AUTHOR]

Fama and French (FF, 2015) propose a five-factor asset pricing model that captures size, value, profitability and investment patterns. The primary purpose here is to further investigate this new model using an improved GMM-based robust instrumental variables technique. A further purpose is to explore the relationship among the FF factors and the Pástor–Stambaugh (PS, 2003) liquidity factor. We conclude that except for the market factor, all of the factors including liquidity are not significant at even the 5% level using our GMM approach for almost all of the FF 12 sectors. [ABSTRACT FROM PUBLISHER]