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We examine the incremental cross-sectional role of several common risk factors related to size, book-to-market, and momentum in size-and-momentum-sorted portfolios. Unlike the existing literature, which focuses on the conditional mean specification only, we evaluate the common risk factors’ incremental explanatory power in the cross-sectional characterization of both average return and conditional volatility. We also investigate the role of ex-ante market risk in the cross-section. The empirical results demonstrate that the size-and-momentum-based risk factors explain a significant portion of the cross-sectional average returns and cross-sectional conditional volatility of the benchmark equity portfolios. We find that the Fama–French (1993) factors and the ex-ante market risk are priced in the cross-sectional conditional volatility. We conclude that the size-and-momentum-based factors provide a source of risk that is independent of the Fama–French factors as well as ex-post and ex-ante market risk. Our results bolster the risk-based explanation of the size and momentum effects.

This study examines the five-factor model of Fama and French (2015) on the French stock market by comparing it to the Fama and French (1993)’s base model. The new Fama and French five-factor model directed at capturing two new factors, profitability and investment in addition to the market, size and book to market premiums. The pricing models are tested using a time-series regression and the Fama and Macbeth (1973) methodology. The regularities in the factor’s behavior related to market conditions and to the sovereign debt crisis in Europe are also examined. The findings of Fama and French (2015) for the US market are confirmed on the Paris Bourse. The results show that both models help to explain some of the stock returns. However, the five-factor model is better since it has a marginal improvement over the widely used three-factor model of Fama and French (1993). In addition, the investment risk premium seems to be better priced in the French stock market than the profitability factor. The results are robust to the Fama and Macbeth (1973) methodology. Moreover, profitability and investment premiums are not affected by market conditions and the European sovereign debt crisis.

Previous studies have shown that stock returns bear a premium for downside risk versus upside potential. We develop a new risk measure which scales the traditional CAPM beta by the ratio of the upside beta to the downside beta, thereby incorporating the effects of both upside potential and downside risk. This “modified” beta has substantial explanatory power in standard asset pricing tests, outperforming existing measures, and it is robust to various alternative modeling and estimation techniques.

Environmental, social, and governance (ESG) issues have recently received much attention. This research investigates the daily performance of socially responsible investment (SRI). To do that, the authors construct portfolios consisting of the SRI, non-SRI, and matched non-SRI. The portfolios can be compared with the market benchmark based on α adjusted asset pricing models. Due to using high-frequency data, the authors use ARCH/GARCH to deal with time-varying volatility. Moreover, the authors also utilized Fama–MacBeth pooled regression to confront the SRI stocks and the non-SRI counterpart. In sum, the findings of this study confirm the superior performance of the value-weighted (VW) SRI portfolio against the market. On a head-to-head basis, the SRI yields a higher return than the non-SRI. The results are robust in the quarterly analysis. It is essential for investors that put their money in socially responsible (SR) portfolios to either promote sustainable development or chase a return on it.

This paper explores the relationship between size, book-to-market, beta, and expected stock returns in the U.S. Information Technology sector over the July 1990–June 2001 period. Two models, the multivariate model and the three-factor model, are employed to test these relationships. The risk-return tests confirm the relationship between size, book-to-market, beta and stock returns in IT stocks is different from that in other non-financial stocks. However, the sub-period results (the periods before and after the technology crash in April 2000) show that the nature of the relationship between stock returns, size, book-to-market, and market factors, or the magnitude of the size, book-to-market, and market premiums, is on average unchanged for both sub-periods. This result suggests the technology stock crash in April 2000 was not a correction of stock prices.

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

In this paper, we explore an extensive panel data set covering more than 4,000 listed firms in 16 European countries to study the effects of shareholder protection on ownership structure and firm performance. We document a negative firm-level correlation between shareholder protection and ownership concentration. Differentiating between shareholder types, we find that this pattern is mainly driven by strategic investors. In contrast, we find a positive correlation between shareholder protection and block ownership of institutional investors, in particular when we restrict the analysis to independent institutional investors. Finally, we find that independent institutional investors are positively associated with firm valuation as measured by Tobin’s Q. The opposite applies for strategic investors. Overall, our results are consistent with the view that (i) high shareholder protection and (ii) limited ownership by strategic investors make small investors and investors interested in security returns more confident in their investments.

This research studies private information from extreme price movements or jumps. The authors calculate the private information using a reduced form model from the stochastic volatility jump process and use several statistical robustness tests as well as several frequencies to improve our consistency. This study reveals that private information is significant in explain the existence of jumps in capital markets in Southeast Asia, whereas macroeconomic events cannot explain them. The authors determine empirically that private information in Malaysia, Singapore, Thailand, and Indonesia are not persistent and its value gradually decreases when we use the lower frequency. Based on the Fama–Macbeth regression, this study shows that private information in the capital market has a strong positive relationship with individual returns in Indonesia’s capital market and Thailand’s capital market for all frequencies.

The authors consider a variety of cryptocurrency and equity risk factors as potential forces that drive cryptocurrency returns and carry risk premiums. In a cross-section of 2,000 biggest cryptocurrencies during 2014–2020, only downside market risk, cryptocurrency size and cryptocurrency policy uncertainty factors are systematically priced with significant premiums. Cryptocurrencies, which have greater exposures to these factors, yield higher returns subsequently. Equity market risk, particularly equity downside market risk, appears to be more important than cryptocurrency market risk, suggesting greater linkages between cryptocurrency and equity markets than we used to think. Global and the US equity factors are more relevant for the cryptocurrency market than local factors from other markets. However, there is no evidence that exposure to momentum, volatility and Fama–French factors is compensated by higher returns.