Search Results (37)

We develop an empirical application on a large dataset of European stock returns in order to estimate the risk premia. While traditional factor models often struggle with high levels of pricing errors and noisy proxies in fragmented markets, we show that the Three-Pass Estimation Method (3PEM) serves as both a robust estimator and a diagnostic tool for factor purification. By assuming the Fama–French five-factor model as the baseline model, we first show that the 3PEM yields risk premium estimates for the European market that are more economically plausible and statistically robust than those obtained using the traditional two-pass estimation method (2PEM). Moreover, our results show that the 3PEM is able to detect noise in tradable factors. Furthermore, the 3PEM is used to denoise the observed factors, providing purified versions that better capture the systematic components of risk. We also identify both noisy factors and denoised factor series that improve the estimation of stock-level exposures and expected returns. Full article

This study examines the extent to which Indian technology equities generate sufficient returns relative to their inherent volatility and assesses whether intra-sector diversification can improve outcomes in this dynamic, high-risk sector. Drawing on data from January 2020 to April 2025, ten leading firms are analyzed using an integrated approach that incorporates traditional risk-adjusted indicators, downside-sensitive metrics, and a six-factor model featuring momentum. The results show clear heterogeneity in performance. Mid-cap innovators such as Persistent Systems and Coforge deliver positive and, in some cases, statistically significant alphas, while large-cap stocks including Infosys, Tata Consultancy Services (TCS), and Wipro provide stability but limited excess returns. At the portfolio level, an equally weighted allocation improves downside protection. However, factor-model analysis finds no statistically significant portfolio alpha once systematic exposures are accounted for. These findings highlight the importance of active firm-level selection within the Indian technology sector, while also underscoring the role of intra-sector diversification in mitigating extreme losses. Full article

Sustainable finance and carbon risk have attracted substantial interest from both practitioners and scholars. This paper integrates the income-based environmental responsibility framework with financial asset pricing models to investigate how carbon transition risk propagates along value chains and impacts asset returns. By utilizing the Ghosh supply-driven input–output model to quantify downstream value chain carbon emissions as a proxy for the dependence of a company’s revenue streams on high-carbon downstream clients, we construct a novel downstream carbon risk factor (DMC) by sorting stocks into portfolios based on this exposure and forming a factor mimicking long short portfolio. We then integrate this DMC factor into the Fama–French five-factor framework to propose a six-factor model capable of capturing value chain risk transmission. Empirical results of Chinese A-share listed companies demonstrate that firms with high DMC exposure, being vulnerable to carbon transition shocks such as carbon pricing, offer a significant risk premium even after controlling for traditional financial characteristics. This finding provides robust evidence for the carbon premium hypothesis in the world’s largest emerging market and contributes a theoretically grounded and empirically implementable framework for integrating value chain carbon risk into asset pricing analysis. Full article

A longstanding debate in finance concerns the impact of social responsibility actions on firms’ long-term profitability. This study provides a broad analysis on the relationship between ESG, its components, and stock returns. Using a dataset that spans from December 2014 to December 2023, this research analyzes an annual average of around 2260 publicly traded companies from Europe and the United States. The findings consistently show a negative link between ESG ratings, their components, and stock returns, a result that is possibly explainable by the mixed effect of a reduction of risk (lower risk premium) from social responsibility, and lower profitability from associated costs. The coefficients for ESG and its pillars in explaining stock returns are generally consistent, with a few exceptions for the environmental and governance components. The environmental pillar has a stronger influence in Europe, across firm sizes, while in the US, the effect is limited to larger companies. For governance, variations align with differing ownership structures across regions and changing investor priorities as firms grow, with stronger influence in Midcaps of both regions and in U.S. Large Caps. The effects of overall ESG scores and individual pillars on stock returns across regions, firm sizes, and their interaction, provide a more comprehensive perspective on their relationship. Full article

Accurately predicting asset returns remains a central challenge in finance, with significant implications for portfolio optimization and risk management. In response to the challenge, this study evaluates the predictive performance of machine learning algorithms in estimating excess returns of U.S. industry portfolios, within the out-of-sample prediction framework of the Fama–French three-, four-, five- and six-factor asset pricing models. In the analysis, Support Vector Regression, Multilayer Perceptron, Linear Regression, and k-Nearest Neighbor were employed using monthly return data from 1992 to 2022, covering 5-, 10-, 12-, 17-, 30-, 38-, 48-, and 49-portfolio configurations composed of NYSE, AMEX, and NASDAQ-listed firms. The findings reveal that support vector regression achieved the highest number of top-ranked results, producing the most successful outcomes in 305 out of 836 model–portfolio combinations. However, multilayer perceptron achieved the best fit in the largest number of portfolios, ranking first in all groups except the 5-industry configuration. Furthermore, the Fama–French five-factor model outperformed other specifications across all groupings, confirming the value of incorporating profitability and investment information. Predictive performance also varied by industry, as wholesale and manufacturing sectors exhibited strong alignment, whereas utilities and energy-related sectors, likely constrained by structural or regulatory features, remained less responsive and exposed to long-term risks. Full article

This study investigates whether publicly listed cannabis shares provide enough risk-adjusted returns to warrant their incorporation into diversified portfolios. An equally weighted portfolio of cannabis companies is constructed using monthly data from January 2015 to December 2024. Risk-adjusted performance is assessed using the Sharpe, Sortino, and Omega ratios and compared to the Russell 3000 Index and the FTSE All-World ex-US Index. In addition, we estimate both unconditional and conditional Fama–French five-factor model enhanced by momentum. The findings indicate that cannabis stocks persistently underperform U.S. and global benchmarks in both absolute and risk-adjusted metrics. Downside risk is elevated because cannabis portfolios exhibit much higher value at risk (VaR) and conditional value at risk (CVaR) than broad indices, especially after COVID-19. The findings show that cannabis stocks are quite volatile and fail to generate significant returns on a risk-adjusted basis. The study highlights the sector’s structural vulnerabilities and cautions investors, portfolio managers, and regulators against treating cannabis shares as dependable long-term investments. Full article

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model’s performance on thirty-two portfolios of non-financial firms sorted by size, value, profitability, investment, and labor income growth in the Indian market over the period July 2010 to June 2023. Moreover, the current study extends the Fama and French five-factor model by incorporating a human capital proxy by labor income growth as an additional factor thereby proposing an augmented six-factor asset pricing model (HC6FM). The Fama and MacBeth two-step estimation methodology is employed for the empirical analysis. The results reveal that small-cap portfolios yield significantly higher returns than large-cap portfolios. Moreover, all six factors significantly explain the time-series variation in excess portfolio returns. Our findings reveal that the Indian stock market experienced heightened volatility during the COVID-19 pandemic, leading to a decline in the six-factor model’s efficiency in explaining returns. Furthermore, Gibbons, Ross, and Shanken (GRS) test results reveal mispricing of portfolio returns during COVID-19, with a stronger rejection of portfolio efficiency across models. However, the HC6FM consistently shows lower pricing errors and better performance, specifically during and after the pandemic era. Overall, the results offer important insights for policymakers, investors, and portfolio managers in optimizing portfolio selection, particularly during periods of heightened market uncertainty. Full article

This study comparatively evaluated the Capital Asset Pricing Model (CAPM), the Fama and French three-factor model (FF3), and the Fama and French five-factor model (FF5) in key US market sectors (finance, energy, and utilities). The goals were to optimize financial decisions and reduce valuation errors. The historical daily returns of ten-stock portfolios, selected from sectors with the highest trading volume in the S&P 500 Index between 2020 and 2024, were analyzed. Companies with the lowest beta were prioritized. Models were compared based on the metrics of the root mean square error (RMSE) and mean absolute error (MAE). The results demonstrate the superiority of the multifactor models (FF3 and FF5) over the CAPM in explaining returns in the analyzed sectors. Specifically, the FF3 model was the most accurate in the financial sector; the FF5 model was the most accurate in the energy and utilities sectors; and the FF4 model, with the SMB factor eliminated in the adjustment of the FF5 model, was the least error-prone. The CAPM’s consistent inferiority highlights the need to consider factors beyond market risk. In conclusion, selecting the most appropriate asset valuation model for the US market depends on each sector’s inherent characteristics, favoring multifactor models. Full article

This study examines the explainability, validity, and applicability of multi-factor models in explaining the returns of Green (eco-friendly), Grey (neutral), and Red (environmentally harmful) EU securities. We apply the Fama–French three-factor and five-factor models, along with the Carhart four-factor model, to analyze changes in risk exposures and adjusted abnormal returns (alphas) before and after the 2009 global financial crisis (GFC). Green and Grey securities exhibit positive SMB loadings, while Grey’s HML shifts from negative to positive over time. Both Green and Red securities show positive SMB and HML factors but negative alphas in the second period, indicating systematic underperformance. Additionally, for Red assets, momentum (MOM), profitability (RMW), and investment (CMA) factors are positive and significant in the first period but become insignificant or negative later. These findings highlight structural shifts in factor exposures and contribute to the ongoing debate on the most suitable classical asset pricing framework for environmentally classified assets, offering insights into the effectiveness of traditional factor models in different classes of environmental assets in finance. Lastly, the three-factor model better captures the common variation in Green and Grey asset returns. Specifically, the 4-factor model and the HML Devil factor prove to be more effective in explaining returns for Red securities. Full article

Property stocks are an attractive alternative investment for investors who want passive income. Investors’ decisions focus not only on maximizing returns but also on reducing risk. This study examines the extent to which macroeconomic factors affect stock performance by comparing the effectiveness of the Fama–French five-factor model (5FF) and Fama–French seven-factor model (7FF) in estimating returns. This study also verifies Fisher’s theory in the context of property and real estate stocks. The research data used are property and real estate stocks in the Indonesian capital market. The data are processed using the OLS estimation method, and Akaike’s Information Criterion (AIC) is used to choose the optimal model. The results show that property and real estate stocks in Indonesia with negative profitability at all quantiles can hedge inflation and interest rates. However, the interest rates are not the only factor affecting the market risk. The 7FF model is better at explaining the variability of stock portfolio returns. This research makes an essential contribution to the financial literature in Indonesia, particularly in the context of portfolio management in the property and real estate sector. Full article

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. Full article

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. Full article

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. Full article

Socially responsible investments, also referred to as ethical or sustainable investments, have experienced rapid global growth in recent years. They represent an investment approach that incorporates social, environmental, and ethical considerations into decision-making processes. Consequently, the significance of socially responsible investments has captured the attention of academics, prompting inquiries into the impact of integrating social criteria on portfolio performance. The primary objective of this work was to conduct a comparative study of the performance between socially responsible and non-socially responsible investment funds, using funds domiciled in Portugal and Spain. Various multi-factor models, including the three-factor model of Fama and French, the four-factor model of Carhart, and the five-factor model of Fama and French, were employed to assess performance. The sample comprised 125 investment funds, with 43 identified as socially responsible and 82 as non-socially responsible. The study’s findings indicate that there are no significant differences between socially responsible funds and their conventional counterparts. The majority of funds experience performance alterations during periods of crisis compared to crisis-free periods. Additionally, when comparing non-conditional models with conditional models, an improvement in the explanatory power of the latter is observed. This suggests that the inclusion of the dummy variable enhances the quality of fit for the models. Full article

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. Full article