Search Results (39)

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

In this paper, we propose a linear factor model that incorporates investor preferences toward sustainability to analyze indirect effects that climate concerns may have on asset prices. Our approach is based on the relationship between environmental, social, and governance (ESG) investing and climate change considerations by investors. We use ESG scores as a part of the information set used by investors to determine the unconditional version of the conditional capital asset pricing model (CAPM). Our results show that the ESG score allows the linearized version of the conditional CAPM to greatly outperform the classic CAPM and the Fama–French three-factor model for different sorts of stock portfolios, contributing significantly to reducing pricing errors. Furthermore, we find a negative price of risk for stocks that covary positively with ESG growth, which suggests that green assets may perform better than brown ones if ESG concerns suddenly become more pressing over time. Thus, our paper constitutes a step forward in the attempt to shed light on how climate change is priced regardless of the climate risk measure used. 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

Financial economists have long studied factors related to risk premiums, pricing biases, and diversification impediments. This study examines the relationship between a firm’s commitment to environmental, social, and governance principles (ESGs) and asset market returns. We incorporate an algorithmic protocol to identify three nonobservable but pervasive E, S, and G time-series factors to meet the study’s objectives. The novel factors were tested for information content by constructing a six-factor Fama and French model following the imposition of the isolation and disentanglement algorithm. Realizing that nonlinear relationships characterize models incorporating both observable and nonobservable factors, the Fama and French model statement was estimated using an enhanced shallow-learning neural network. Finally, as a post hoc measure, we integrated explainable AI (XAI) to simplify the machine learning outputs. Our study extends the literature on the disentanglement of investment factors across two dimensions. We first identify new time-series-based E, S, and G factors. Second, we demonstrate how machine learning can be used to model asset returns, considering the complex interconnectedness of sustainability factors. Our approach is further supported by comparing neural-network-estimated E, S, and G weights with London Stock Exchange ESG ratings. Full article

The global financial landscape has witnessed a significant shift towards Exchange-Traded Funds (ETFs), with their market capitalization surpassing USD 10 trillion in 2023, due to advantages such as low management fees, high liquidity, and broad market exposure. As ETFs become increasingly central to investment strategies, accurately forecasting their performance has become crucial. This study addresses this need by comparing the efficacy of deep learning models against the traditional Fama-French three-factor model in predicting daily ETF returns. The methodology employs eight artificial neural network architectures, including ANN, LSTM, GRU, CNN, and their variants, implemented in Python and applied to data ranging from 2010 to 2020, while also exploring the impact of additional factors on forecast accuracy. Empirical results reveal that LSTM and the Fama-French three-factor model exhibit a superior performance in ETF return prediction. This study contributes to the literature on financial forecasting and offers practical insights into investment decision making. By leveraging advanced artificial intelligence techniques, this study aims to enhance the toolkit available for ETF performance analysis, potentially improving investment strategies in this dynamic market segment. 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 paper examined a large sample of equity SMAs (separately managed accounts, hereafter) from 1999 to 2023. This paper found that separate accounts have much higher expenses than ETFs and may outperform or underperform ETFs in terms of gross return and net return depending on their investment styles. However, this paper found that separate accounts consistently outperform ETFs in terms of risk-adjusted gross and net return alphas across different investment styles using the Fama and French Three Factor Model. Additionally, this paper found no significant evidence that tax is proactively managed within separate accounts. Lastly, this paper found that on average SMAs’ risk-adjusted alphas do not persist over time. Full article

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. 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

This paper explores the implications of consumption heterogeneity between domestic and foreign investors on the cross-section of stock returns in a host country. We argue that foreign investors in a small open economy integrated into global financial markets may face consumption risk, which could result in risk premia being reflected in stock returns. To account for the potential influence of foreign investors on asset prices in a host country, we develop a two-country durable consumption model under market incompleteness, which extends the one-country durable consumption model. The proposed model includes both domestic and foreign pricing factors. We investigate the empirical performance of our model with Fama–French portfolios for Korea, taking U.S. investors as representative foreign investors. The empirical results advocate the two-country durable consumption model, confirming the significant role of foreign factors in the cross-section of domestic stock returns. Additionally, $R^2$ tests conducted with different sets of test assets show that the explanatory power of our model is comparable to that of the Fama–French three-factor model. Full article

In recent years, the rapid and significant development of emerging markets has globally led to insight from potential investors and academicians seeking to assess these markets in terms of risk inheritance. Therefore, this study aims to explore the validity and applicability of the capital asset pricing model (henceforth CAPM) and multi-factor models, namely Fama–French models, in Pakistan’s stock market for the period of June 2010–June 2020. This study collects data on 173 non-financial firms listed on the Pakistan stock exchange, namely the KSE-100 index, and follows Fama-MacBeth’s regression methodology for empirical estimation. The empirical findings of this study conclude that small portfolios (small-size companies) earn considerably higher returns than big portfolios (large-size companies). Ultimately, the risk associated with portfolio returns is reported to be higher for small portfolios (small-size companies) than for big portfolios (large-size companies). According to the regression output, the CAPM was found to be valid for explaining the market risk premium above the risk-free rate. Similarly, the FF three-factor model was found to be valid for explaining time-series variation in excess portfolio returns. Later, we added human capital into FF three- and five-factor models. This study found that the human capital base six-factor model outperformed the other competing asset pricing models. The findings of this study indicate that small portfolios (small-size companies) earn more returns than big portfolios (large-size companies) to reward the investor for taking extra risks. Investors may benefit by timing their investments to maximize stock returns. Company investment in human capital adds reliable information, replicates the value of the company and, in the long term, helps investors make rational decisions. Full article

This paper examines the use of the return on gold instead of treasury bills in empirical asset pricing models for the US equity market. It builds upon previous research on the safe-haven, hedging, and zero-beta characteristics of gold in developed markets and the close relationship between interest rates, stock, and gold returns. In particular, we extend this research by showing that using gold as a zero-beta asset helps to improve the time-series performance of asset pricing models when pricing US equities and industries between 1981 and 2015. The performance of gold zero-beta models is also compared with traditional empirical factor models using the 1-month Treasury bill rate as the risk-free rate. Our results indicate that using gold as a zero-beta asset leads to higher R-squared values, lower Sharpe ratios of alphas, and fewer significant pricing errors in the time-series analysis. Similarly, the pricing of small stock and industry portfolios is improved. In cross-section, we also find improved results, with fewer cross-sectional pricing errors and more economically meaningful pricing of risk factors. We also find that a zero-beta gold factor constructed to be orthogonal to the Carhart four factors is significant in cross-section and helps to improve factor model performance on momentum portfolios. Furthermore, the Fama–French three- and five-factor asset pricing models and the Carhart model are all improved by these means, particularly on test assets which have been poorly priced by the traditional versions. Our results have salient implications for policymakers, governments, central bank rate-setting decisions, and investors. Full article

This study empirically tests and compares the performances of three famous financial asset valuation models in the Moroccan stock exchange: CAPM, the Fama and French three-factor model, and the Fama and French five-factor model. Our sample considers monthly data covering the sample period of July 2002 to June 2020. The main findings reveal that the GRS test typically rejects each of the examined model. On the basis of our analysis, we find that the value effect is more pronounced than the size effect. However, profitability and investment effects are almost absent. Regarding the factor spanning tests, the results show that the value factor was not redundant. Beyond this, the size and investment factors are the redundant factors. In Morocco, the market factor is the most powerful factor, perhaps assisted by value and profitability factors. Although the CAPM performs poorly in capturing the variation in Moroccan returns, the market factor continues to play an important role, even after adding other factors. Overall, all the tested models were improved slightly, but leave part of the variation in Moroccan stock returns unexplained. Full article