Fama French Research Articles

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.

Time-varying aggregate tail risk and cross-section of stock returns: Indian evidence

This article examines whether the time-varying aggregate tail risk is priced in the cross-section of average returns in the Indian equity market. The results show that the tail risk beta sorted portfolio returns increase monotonically with an increase in their tail risk sensitivity. More importantly, the Indian stocks provide economically and statistically significant risk premium for the tail beta risk factor. The findings remain robust after controlling for the Fama–French and momentum risk factors, and other individual characteristics governing tail behaviour of stocks. The study also provides evidence that institutional ownership and firm size affect tail risk premium.

Prioritization of the IT Sector in the CEE Stock Markets: Investment Policies, Trends and Hidden Gems

Objective: The main purpose of the research is to show that the Central and Eastern European (CEE) market is an essential component of the global stock market. It displays similar patterns to developed countries, and there is a special emphasis on information technology. Research Design & Methods: The study is supported by various machine learning methods and economic analysis, in addition to using Python and R packages. In order to gain a more comprehensive insight into the developments in CEE-region trends, the paper considers a comparative portfolio that focuses on German business, and there is a simulation of the incorporation of the Ukrainian IT sector. Constructing a business strategy in accordance with the Fama–French approach is boosted by incorporating neural networks. Findings: An examination of the performance of Czech, Polish, and Hungarian firms on the stock exchanges suggests that the investment policy oriented towards software is a reasonable choice. The results yielded by the IT companies unmistakably reveal the substantial benefits of their stock market ventures, as well as indicating the ongoing trend of investor reliance. The problem of IT business valuation is highlighted as one of the pitfalls of investing. Implications & Recommendations: The research proposes that maintaining a priority focus on IT, even in challenging circumstances, ensures steady regional advancement. Contribution & Value added: This research brings a unique emphasis to the status and outlook of the IT sector, contributing to the existing toolkit, even in the face of repeated crises.

Is the Fama French model robust in the Chinese stock market?

Many previous studies have analyzed the different stock return models in various segments of the Chinese stock market. This essay analyzes the Fama-French five factors and three factors model in the main board, the second board, and STAR market. It finds that the three-factor models are efficient in all three markets expect for the STAR market, while the five-factor model is inefficient in testing the second board market alone and overall efficient in testing the mixed market and the STAR market. Furthermore, it is also found that the MKT factors are the most significant contributors to the portfolio excess return in all six situations. The HML and SML factors also exhibit constant relations with the portfolio return. Besides, it is found that HML and SMB factors are not significantly correlated with the stock’s performance on the STAR market. Overall, the relative efficiency of Fama French's three and five-factor model in explaining these markets is proved. Investors can use these two models to analyze the stock return and earn profit in the next period, and policymakers can also employ this model to understand the market and decide suitable fiscal and monetary policies.

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.

Product network and origin of common equity factor risks

This paper extends the multisector asset pricing model based on production networks, achieving direct pricing of a firm’s equity assets at a more microscopic level within the product network. An Equilibrium-Aligned PageRank (EA-PR) centrality algorithm is proposed, and it is theoretically proven that EA-PR centrality can reflect the relative value of products and further derive the value of firms. A new equity asset pricing factor is constructed based on the EA-PR centrality of firms. The key findings can be summarized as follows: Firstly, the EA-PR centrality of firms shows a significant positive linear predictability for equity returns, and the returns associated with the EA-PR centrality factor cannot be fully explained by the Fama–French 6 (FF6) factors. Secondly, the EA-PR centrality factor demonstrates significant explanatory power for the FF6 factors and, to some extent, exhibits predictive ability for future returns of the FF6 factors. Thirdly, the product network model can effectively reflect the market competitiveness and growth potential of firms, and firms with high EA-PR centrality have stronger value capture capabilities, improving gross profit margin, cash flow, and investment growth in the future. Finally, this study empirically demonstrates the consistency between the product network model and the pricing theory of the q5 model, which incorporates the expected growth factor.

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.

Expanding the Fama–French Factor Model with the Industry Beta

Expanding the Fama–French Factor Model with the Industry Beta

Risk Premium and Fear of Investors in Crisis’ Periods: An Empirical Approach Based on Fama–French and Carhart Factor Models

This study aims to answer the question about the interactions between “investors’ fear”, two factors proposed by Fama & French, the Carhart momentum factor, andthe risk premium, and how these interactions were affected by two financial crises, the Dot-Com and Sub-Prime crises. This paper is the first empirical study that considers the effects of these financial crises. It is of critical importance as it changes the specificity of the empirical models for different periods, significantly affecting the results compared to previous research work. The main findings include a general negative change in fear over all of the sub-periods. Secondly, no consistent positive trend was observed in any of the risk premiums over time. After each crisis, the relationships between the endogenous variables had significant changes. More specifically, investors’ fear, on the first day of the week, appears to be systematically higher across all sub-periods except during the Sub-Prime crisis. Finally, after the Sub-Prime financial crisis, there is an almost complete loss of the explanatory power of the VAR models. Although fear does not seem to affect risk premiums or momentum, it was nevertheless found that the results are sensitive to the specification of the models.

Stock return prediction with multiple measures using neural network models

In the field of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction effects have led to the increasing popularity of machine learning (ML) methods. This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context. Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables. However, the inclusion of macroeconomic factors from the financial market, real economic activities, and investor sentiment leads to substantial improvements in the model performance. Notably, the degree of improvement varies with the specific measures of stock returns under consideration. Furthermore, our analysis indicates that, after the inclusion of macroeconomic factors, there is a dissimilarity in model performance, variable importance, and interaction effects among macroeconomic and firm-specific variables, particularly concerning abnormal returns derived from the Fama–French three- and five-factor models compared with excess returns. This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables. These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models, stock returns, and macroeconomic conditions in the domain of empirical asset pricing.

The Effect of ESG Scores on Portfolio Performance. Evidence from Europe

Abstract The purpose of this study is to examine the performance of eight portfolios constructed based on environmental, social and governance scores. The main question I explore is whether portfolios that integrate ESG values generate a significant additional return. The sample consists of 1095 European stocks from 23 countries, and the period analyzed is 2021-2022. To form portfolios, all stocks are first ranked according to one-year lagged ESG scores (Total ESG score, Environmental score, Social score and Governance score) in a decreasing way. Then 10% of the highest-scoring stocks are included in the Top portfolio, while 10% of the lowest-scoring stocks are included in the Bottom portfolio. Portfolio evaluation was performed using two techniques: CAPM and Fama French five-factor regression. In addition, the Sharpe ratios of the portfolios have been calculated. The Sharpe ratio is positive for the top portfolio, showing that investors are compensated more per unit of risk. The Top Environmental portfolio is found to be the best performer for positive values. In contrast, the Sharpe ratio is negative for the Bottom portfolios. This means that these portfolios underperform the risk-free rate. The CAPM results show that all portfolios using ESG-based scores underperformed the market. The results of the Fama-French five-factor model are similar to those of the CAPM. The general findings of this study indicate that using ESG-based scores in the selection investment process does not add a significant additional return. These findings provide additional evidence to the literature on the relationship between ESG and portfolio performance in the recent European context.

Intangible Capital in Factor Models

The transition from a traditional manufacturing-based economy to a knowledge- and service-based economy over recent decades resulted in a considerable rise in intangible capital, most of which is not reported on companies’ balance sheets. As a result, balance sheet-based valuation ratios, investment measures, and other firm characteristics that do not incorporate off-balance sheet (OBS) intangible capital suffer from significant measurement error problems. We incorporate a new measure of OBS intangible capital into firm characteristics, such as book to market, investment, and profitability, to address these measurement errors. These OBS intangible adjustments improve the performance of the Fama–French three- and five-factor models and the q-factor model, especially during recent decades. We further find that the value factor is no longer redundant in these empirical factor models. This paper was accepted by Lukas Schmid, finance. Funding: The authors are grateful for financial support from the 2019 EDHEC Scientific Beta “Advanced ESG & Factor Investing” Research Chair. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01261 .

Comprehensive Asset Pricing Tests in the Korean Stock Market

AbstractWe examine the empirical performance in the Korean stock market of three new asset pricing factor models: the Stambaugh–Yuan (2017) mispricing factor model, the Daniel et al. (2020) three‐factor model, Barillas‐Shanken (2018) six‐factor model and the Hou et al. (2021) q5‐factor model. We find that all factors in these factor models have significantly positive risk premiums and are not explained by the Fama–French six‐factor model. Compared to other prevalent models, the q5 model shows the highest maximum Sharpe ratio, mainly due to its profitability and expected growth factors. Further, the q5 model exhibits superior performance in explaining the returns of 97 anomaly portfolios in the Korean market.

Do socially responsible indices outperform conventional indices? Evidence from before and after the onset of Covid‐19

AbstractAccording to traditional portfolio theories constraints, restrictions, and screens applied in portfolio selection reduces the diversification opportunities which can impact financial performance. Is this true in the case of socially responsible investment (SRI)? To answer this, present study analyzes the performance of Socially Responsible (SR) indices in comparison to conventional indices in an emerging economy. The uniqueness of the study is that it analyses the performance of Shariah, ESG, and thematic indices in a single study. Further, the study measures the impact of Covid‐19 on them. Comparative performance evaluation was conducted by using absolute return analysis and risk‐adjusted measures namely, Sharpe ratio, Treynor ratio, tracking error, information ratio, capital asset pricing model (CAPM), Fama–French three‐factor, and Carhart's four‐factor models. The Structural break was identified, hence analysis was conducted for the total period (January 2017–March 2023) and two sub‐periods, that is, pre and post‐Covid‐19 period. No significant difference was found between the returns of SR indices and conventional indices as against the benchmark index on the basis of absolute return analysis. Sharpe ratio and Treynor ratio both were having negative values for all the SR and conventional indices. Tracking error for all the SR and Conventional indices were very low. The CAPM and both multi‐factor models univocally pointed toward the underperformance of all the SR (except S&P BSE 100 ESG index which had equal performance) and both conventional indices against the benchmark index. Noteworthy point is that only Shariah indices gave the highest returns during post‐Covid period. This research will help in deepening the SRI in the capital market. Companies should increase their ESG scores and make efforts to be listed on the SR indices. Policymakers should announce some kind of rebates, or recognition for star‐performing companies in the field of sustainability to encourage other companies to adopt SR practices in their business operations. The novelty of the current study is that it adds to the socially responsible literature by analyzing the performance of Shariah, ESG, and Thematic indices and conventional indices in a single study in the fastest‐growing economy of India and analyses the impact of Covid‐19 on this performance.

Financial Integration of the European, North America, Asiatic and Japanese stock markets from 2003 to present times

We apply an integration/segmentation analysis between the European (EU) market and the North America stock market (US and Canada), the Asian Stock Market (AS) and the Japanese (JP) market. The analysis is carried out from 2003 until the present time. We apply the Jorion and Schwartz (1986) methodology and extend the work of Brooks et al. (2009) using a simpler Capital Asset Price Model (CAPM) and the Market return downloaded from the Fama French website for the time period analysed. Our results in this empirical study show integration between the European portfolios and the US stock market and the Asian Portfolios and the US stock market in the full time period analysed. Although the methods applied in this paper have been already introduced in the literature, this is the first time that they are applied systematically to compare the integration and segmentation between different economies and a given portfolio set. This systematic approach helps to establish the conclusiveness of their forecasts.

Understanding co-movements based on heterogeneous information associations

Both systematic and idiosyncratic information dissemination contribute to assets co-movement. To proxy for co-movement based on heterogeneous information diffusion, we construct two-layer network structures in terms of the correlations of systematic and idiosyncratic returns for Fama–French 49 industry portfolios. We further delve into the co-movement structures by studying their dynamics and interactions in terms of topological properties at both the system and individual levels. Our findings reveal the following: (1) Co-movement structures exhibit temporal changes and are closely associated with major risk events. (2) At the system level, there is time-varying mutual Granger causality between co-movement structures, particularly during or after recession periods. (3) At the individual level, we identify statistically significant but economically insignificant interplay between the two co-movement structures. Moreover, the co-movement structure based on systematic information diffusion is mainly influenced by industry-level characteristics, including idiosyncratic momentum, idiosyncratic volatility, and market beta. These results deepen our understanding of asset co-movement structures and bear significance for asset allocation and risk management practices.

The impact of innovation orientation on strategic R&D amidst macroeconomic shocks – an event study approach

PurposeThis research investigates the impact of strategic research and development (R&D) (one led by a firm’s innovation orientation) on stock market performance during the economic disruption caused by the 2016 demonetization of high-value currency notes in India. It shows how firms’ strategic focus on innovation and integrated R&D initiatives can help mitigate shareholders’ losses and protect market value during negative macroeconomic shocks.Design/methodology/approachWe analyzed financial and administrative data from firms listed in the Bombay Stock Exchange (BSE) 500 index and used the Fama French market model with appropriate instruments accounting for possible endogeneity to identify the impact. To ensure the reliability of our findings, we conducted robustness checks with alternate event windows, estimation methods, and variable measurements.FindingsStrategic R&D plays a crucial role in building resilience against macroeconomic shocks. It effectively mitigated shareholders’ losses in the immediate aftermath of the shock, with an elasticity of abnormal returns of 7.65% on day zero, 13.1% during the first five days and 10.5% after the first fortnight. We also find that firms that are business-to-business (B2B), as well as those that are older and less leveraged, are better able to combat such a shock.Research limitations/implicationsThe study looked at one shock, namely demonetization. Future research is needed to demonstrate the generalizability of results during other macroeconomic shocks, like the COVID-19 pandemic. The study focuses on relatively near-term impacts, leaving the long-term value-creation effects of strategic R&D unexplored.Practical implicationsInnovation orientation acts as a structural enabler, allowing firms to make strategic R&D investments that mitigate losses during macroeconomic shocks. It explains that managers should avoid myopically managing R&D investments and align them with the firm’s innovation focus to enhance value creation.Social implicationsWhile the currency demonetization was widely considered to be detrimental for firms as an unannounced negative monetary shock, our research shows that firms with high levels of strategic R&D were successfully able to counteract such a shock.Originality/valueThis is the first study to examine the short-term loss mitigation impact of firms’ focus on innovation and strategic R&D. It emphasizes the role of innovation-focused strategies during economic crises.

Renewable Energy Stocks’ Performance and Climate Risk: An Empirical Analysis

This article studies the relationship between renewable energy stocks’ performance and climate risk. It shows that publicly held renewable energy stocks underperform as a reaction to climate policy information releases, modeled by feed-in tariff (FIT) legislation announcements. The study examined stock price behaviors 2 days before and 30 days after FIT policy announcements. The stock sample used in the study has 3702 firm-day combinations, which included 180 cleantech firms and 32 events from 2007 to 2017. Based on the residual analysis of the sample’s abnormal return, it indicated that the FIT announcements are associated with significant declines in returns. The cumulative abnormal return until Day 18 was a significant −0.83%, while the average abnormal return on the day was −0.16% at normal levels. The study partially excluded the likelihood of a transitory result by varying the measurement horizon. It also adopted both the market model and the Fama–French three-factor models to rule out model misspecification when estimating abnormal returns and thus increased the robustness. In fact, the results were stable to changes in estimating the model’s specifications. In addition, the study compared the portfolio’s performance with mimicking portfolios in terms of size, book-to-market equity (BE/ME), and the firms’ geographic location. It demonstrated that the documented anomaly of the portfolio of renewable energy companies is robust.

Volatility and Herding Bias on ESG Leaders’ Portfolios Performance

We here analyze the factor loadings given by the CAPM, the Fama–French three (FF3), and the five-factor model (FF5), and test the performance and the validity of adding two more factors (volatility and dispersion of returns) to the FF5 factor model of European index-based ESG leaders’ portfolios. Our ESG leaders’ portfolios generated significant negative alphas during 2012–2022, corroborating the literature’s negative argument. The negative abnormal returns of ESG leaders’ portfolios are homogeneous across the three ESG pillars. We conclude that European ESG leaders’ portfolios are biased toward large cap and value stocks with robust operating profitability and against aggressive investments. As robustness tests, we examine Global ESG leaders’ index-based portfolios, producing the same results but with reduced importance in some loading factors like profitability and investment strategy. Furthermore, we deduced that European and Global ESG leaders’ portfolios tilt towards volatility and herding bias.

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.