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The famous Capital Asset Pricing Model (CAPM), widely used in practice, takes into account only the business risk associated with investments in a specific company [not the entire market (or industry)]. In practice, most listing companies use debt financing and operate at a non-zero leverage level. This means that the financial risk associated with the use of debt financing, along with business risk, must be taken into account. The purpose of this paper is to simultaneously account for business and financial risk. We combined the CAPM theory and the Modigliani-Miller (MM) theory, which is the perpetual limit of the BFO (Brusov-Filatova-Orekhova) theory. The article shows that R. Hamada’s attempt to take into account both business and financial risks has proved unsustainable, and the formulas he obtained, widely used in practice, are incorrect. The paper outlines the correct formulae that made it possible to generalize CAPM for the first time, taking into account both business and financial risk. The application of the new CAPM 2.0 model to a number of companies is considered and the difference between the results obtained within the framework of CAPM 2.0 and CAPM is demonstrated. CAPM is one of the main models [along with APT (arbitrage pricing theory) and WACC] within the income approach to business valuation. This significantly increases the value of the developed CAPM 2.0 approach, which can significantly improve the accuracy of the assessment.

This study examines the net monthly returns of real estate exchange-traded funds (ETFs) through various performance evaluation models and market situations. The results reveal that these ETFs generated positive alphas and outperformed benchmark indices in absolute returns. However, their performance varied across market conditions, demonstrating both outperformance and underperformance compared to U.S. and global stocks. During the COVID-19 pandemic, real estate ETFs displayed a decline, trailing behind U.S. and global equities in both absolute returns and risk-adjusted performance. This emphasized their vulnerability during economic crises. Utilizing the Carhart four-factor model, significant exposure of real estate ETFs to the stock market was observed. Moreover, an assessment of ETF portfolio managers' skills indicated proficiency in security selection but limited capabilities in market timing. [ABSTRACT FROM AUTHOR]

Due to the large number of stock companies, complex stock categories and inconsistent evaluation standards of market value, it is not conducive to the choice of investors. Based on the theory and practice of traditional stock performance evaluation model, this paper integrates the algorithm thought structure of Fama–French five-factor model and proposes a machine learning algorithm model for stock performance research. In addition, this paper also builds a model that can evaluate the style and timing ability of fund managers to improve the fund performance evaluation system to a greater extent. With the help of the performance evaluation updating function of the model, it provides a new experience material in the empirical research composition of the fund performance field. In the system test module, the former prediction data and the actual experimental data are integrated, and the two are sorted out and compared, which proves the feasibility and effectiveness of the proposed algorithm model. The final experimental results verify the usefulness of the model in stock return prediction and analysis of influencing factors. [ABSTRACT FROM AUTHOR]

Despite numerous studies of the US and China trade war effect being published over the last seven years, this topic has remained equivocal. The portfolio formation model that underlies the arbitrage pricing theory is still relatively small, especially for the portfolio performance of the Fama-French five factors (5FF) model that examines the global economic crisis. Focusing on the trade war year, the study aims to examine the effect of accounting information on portfolio performance by using the 5FF model. This study employs a dummy panel least square regression model, the data are analyzed from Indonesia, US, and China stock markets with 864 portfolio monthly combinations ranging from 2016 to 2019. The result of the study shows that the US-China trade war strengthens the beneficial impact of market risk on portfolio performance in the Indonesian Stock Exchange. On the other hand, the war has no impact on the relationship between market risk and the performance of a portfolio of US stocks. However, it lowered the beneficial impact of size on portfolio performance for both the US and China stock markets. The robust 5FF of accounting information within the study is size and investment. According to the study, systematic risk and idiosyncratic risk should be given more consideration by both investors and companies to prevent unneeded economic losses and manage the risk that could have a greater impact on the stock market.

We model asset prices in Brazil using the five-factor asset pricing model. We show that Gaussian regression models fail to capture the full data dynamics. We then fit Generalized Additive Models for Location Scale and Shape with data-selected underlying laws. They are based on laws related to the Student t distribution, appear to be correctly specified and deliver good data fits. All estimated models are evaluated using performance measures that are standard in the literature. [ABSTRACT FROM AUTHOR]

• We investigate the influence of ESG disagreement on portfolio returns within the EuroStoxx 600 index, using ESG ratings from Morningstar, Refinitiv, and Bloomberg. • We apply the Fama-French-5-Factor model to calculate and compare portfolio alphas across different portfolio sorts. • Firms with higher ESG agreement yield significantly higher returns than firms with low ESG agreement. • This effect vanishes when introducing firm fundamentals such as sorting by market capitalization or Book-to-Market ratio. • Our results call for a cautious approach towards ESG investing and potential need for regulatory advances to reduce ESG rating heterogeneity. This study examines the impact of ESG rating disagreement on portfolio returns for EuroStoxx600 companies. Portfolios are analyzed using ESG data from Morningstar, Refinitiv, and Bloomberg for 458 firms from January 2016 to December 2022. The empirical findings reveal that high ESG agreement portfolios initially show higher returns. However, this effect vanishes when controlling for Book-to-Market ratio and market capitalization. These results challenge the simplistic view of ESG ratings influencing stock returns independently and highlight the need for a cautious approach to ESG investing. Policymakers and investors should consider the heterogeneity in ESG ratings for future regulations and investment decisions. [ABSTRACT FROM AUTHOR]

Purpose – Fama–French model (FFM) has been successful in helping to predict the financial markets, but investors have been interested in creating more sophisticated models to better predict the performance of the stock market. The objective of the extended version is to create a more robust econometric model to better predict the performance of the Mexican Stock Market. Design/methodology/approach – The study divides the Mexican Stock Market into six different portfolios. The criteria to build those portfolios are the same one used in Fama–French (1992). The study comprises 78 stocks listed in the Mexican Stock Market that are analyzed monthly during 1997–2018. The study analyzes the period before and after the 2008–2009 financial crisis to identify whether there are important changes. The estimation applies the traditional and an extended version of the FFM that include macroeconomic variables such as country risk, economic activity, inflation rate, and exchange rate and some financial variables recommended in the literature. Findings – Results indicate that classic FFM variables are statistically significant in most cases, but relevant macroeconomic variables such as the interest rate, exchange rate and country risk stand out for being weakly relevant in most of the portfolios. However, it is noticed that some of these macroeconomic variables became relevant for different portfolios only after the 2008–2009 crisis, especially in portfolios which include small market capitalization firms. Research limitations/implications – The study includes the stocks listed in the Mexican Stock Market. One limitation is the small number of stocks available, which reduces the possibility of creating well diversified portfolios. This study includes 78 stocks. The stocks removed from the sample are from firms that were not listed during six consecutive months or whose market capitalization did not change in the same period. Outlier data were removed from the sample to capture in better way the general performance of the stock market. Practical implications – The objective of the extended version is to create a more robust econometric model than the traditional model. It is expected that such estimations can be helpful to investors to make better decisions when they try to predict performance in the stock market. Social implications – An extended version of the FFM can be helpful to investors to make better decisions when they try to predict performance in the stock market. Originality/value – To the best of our knowledge there are no more studies in the literature of the Mexican financial market that apply the same methodology.

Abstract In this paper, we examine the presence of short-term and long-term momentum returns in Indian stock market. The study also tries to shed light on the power of asset pricing models and select macroeconomic variables in explaining momentum returns. The results confirm the presence of short-term and long-term momentum returns in Indian stock market. It is also found that Carhart four-factor model’s performance is relatively superior to other factor models such as one factor capital asset pricing model and Fama–French three-factor model in terms of capturing momentum returns. Finally, macroeconomic variables which are considered for analysis do not have any power to explain momentum returns.

This paper empirically characterises the value effect detected in the Italian stock market for the sample period 2000–2018 based on the value premium offered for the acquisition of a value stock. Bids on value stock (as opposed to bids on growth stocks) generate a large and statistically significant average return on the holding of the target in the deal window. Returns on target stocks for a bid make up to two‐thirds of the average return on the long side of the Fama and French high book‐to‐market minus low book‐to‐market (HML) portfolio. The other significant component of the average return of HML is due to short‐selling small‐growth stocks. As evidenced in previous literature, this is often difficult to implement from a practical point of view. [ABSTRACT FROM AUTHOR]

In the last decades, a large number of multifactor assetpricing models have emerged with the aim of correcting the estimated equity risk premiums for some well‐documented market anomalies. In any case, recent research on asset pricing shows how the higher liquidity resulting from the globalization of financial markets has significantly reduced returns tied to many strategies based on market anomalies. In this framework, questions arise about the possible renovated validity of classic assetpricing models. On this basis, in this paper we study to what extent the capital assetpricing model (CAPM) has recovered its past explanatory power. Specifically, we propose a time‐varying beta CAPM in order to control for the variable nature of beta risk to changes in the market liquidity, using the variation of the Amihud illiquidity measure to account for the degree of trading activity. We test both the time‐varying and constant beta models on different sets of anomaly portfolios for the UK equity market, and we compare their performance to that of the Fama–French three‐ and five‐factor models. Additionally, we test the constant beta model on a set of actively managed portfolios, formed according to the variation in market illiquidity in the previous year. Our results show that the pricing errors of the CAPM have significantly decreased with respect to those of previous literature. Furthermore, the time‐varying beta model performs similarly to the Fama–French models in most cases. These results are consistent with increased trading activity that reduces arbitrage opportunities and, therefore, enhances market efficiency. [ABSTRACT FROM AUTHOR]