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Lai, Li, Conover, and Wu (2010) propose a four-factor financial distress model to explain stock returns in the U.S. and Japanese markets. We examine this model in the stock markets of Australia, and six Asian markets (Hong Kong, Indonesia, Korea, Malaysia, Singapore, and Thailand). We find broad empirical support for the four-factor financial distress risk asset-pricing model in those markets. The four-factor financial distress asset pricing model improves explanatory power beyond the Fama–French (1993) three-factor asset pricing model in six of the seven Asian-Pacific markets (12 of 14 portfolio groupings), while the Carhart (1997) momentum-based asset pricing model only improves explanatory power beyond the Fama–French model in three of the seven markets (4 of 14 portfolio groupings).

This study aims to investigate whether the zero-investment portfolio strategy generates higher excess returns for all listed companies in the Stock Exchange of Thailand (SET) or ESG100 stocks. The study period is from January 2016 to December 2020, a total of 60 months. The dividend yield is employed for categorizing the stock into value and growth stocks. The strategy of buying value stocks and short-selling growth stocks is then applied. The results show that investing using the zero-investment portfolio strategy can generate higher returns in an investment portfolio that consists of ESG100 stocks than in an investment portfolio that consists of all stocks in the SET. The optimal holding periods for investing in portfolios that consist of stocks in the SET are 6 months, 9 months, and 12 months, and the optimal holding periods for a portfolio that consists of ESG100 stocks is 6 months. To explain excess returns of stocks in the SET, the Fama and French (2015) five-factor model is employed. There is no relation between risk factors and excess returns for the holding period of 6 months and 12 months. However, excess return is found to have a negative relation with the market risk premium factor for a 9-month holding period. The excess returns of ESG100 stocks are also inversely correlated with investment factors for a holding period of 6 months.

This chapter examines a multifactor model for stock returns in nine Asian markets (Japan, China, South Korea, Hong Kong, Taiwan, Singapore, Indonesia, Malaysia, and Thailand). The authors develop a model using the market risk premium, size, book-to-market, profitability, investment, momentum, price-to-earnings ratio, and dividend yield factors for each market. The empirical results suggest that this eight-factor model can better explain the variations of stock returns than the original Fama–French three-factor model. Factor-based models using local data outperform those using data from US markets. In addition, the evidence suggests that the eight-factor model can better explain stock returns when the market is under stress.

In the build-up of an investment decision, the existence of both active and passive investment vehicles triggers a puzzle for investors. Indeed the confrontation between active and index replication equity funds in terms of risk-adjusted performance and alpha generation has been a bone of contention since the inception of these investment structures. Accordingly, the objective of this chapter is to distinctly underscore whether an investor should be concerned in choosing between active and diverse passive investment structures.

The survivorship bias-free dataset consists of 776 equity funds which are domiciled either in America or Europe, and are likewise exposed to the equity markets of the same regions. In addition to geographical segmentation, equity funds are also categorised by structure and management type, specifically actively managed mutual funds, index mutual funds and passive exchange traded funds (‘ETFs’). This classification leads to the analysis of monthly net asset values (‘NAV’) of 12 distinct equally weighted portfolios, with a time horizon ranging from January 2004 to December 2014. Accordingly, the risk-adjusted performance of the equally weighted equity funds’ portfolios is examined by the application of mainstream single-factor and multi-factor asset pricing models namely Capital Asset Pricing Model (Fama, 1968; Fama & Macbeth, 1973; Lintner, 1965; Mossin, 1966; Sharpe, 1964; Treynor, 1961), Fama French Three-Factor (1993) and Carhart Four-Factor (1997).

Solely examination of monthly NAVs for a 10-year horizon suggests that active management is equivalent to index replication in terms of risk-adjusted returns. This prompts investors to be neutral gross of fees, yet when considering all transaction costs it is a distinct story. The relatively heftier fees charged by active management, predominantly initial fees, appear to revoke any outperformance in excess of the market portfolio, ensuing in a Fool’s Errand Hypothesis. Moreover, both active and index mutual funds’ performance may indeed be lower if financial advisors or distributors of equity funds charge additional fees over and above the fund houses’ expense ratios, putting the latter investment vehicles at a significant handicap vis-à-vis passive low-cost ETFs. This chapter urges investors to concentrate on expense ratios and other transaction costs rather than solely past returns, by accessing the cheapest available vehicle for each investment objective. Put simply, the general investor should retreat from portfolio management and instead access the market portfolio using low-cost index replication structures via an execution-only approach.

The battle among actively managed and index replication equity funds in terms of risk-adjusted performance and alpha generation has been a grey area since the inception of mutual funds. The interest in the subject constantly lightens up as fresh instruments infiltrate financial markets. Indeed the mutual fund puzzle (Gruber, 1996) together with the enhanced growth of ETFs has again rejuvenated the active versus passive debate, making it worth a detailed analysis especially for the benefit of investors who confront a dilemma in choosing between the two management styles.

We revisit the performance of seasoned equity offerings (SEOs) in Japan against the backdrop of the Tokyo Stock Exchange's historical nine-year run up from 1980 to 1989, with the time period chosen for the purpose of comparison to previous studies. We analyze the long-run performance of 427 issues or 387 Japanese firms that conducted SEOs from 1980–1990. Initial results indicate that SEOs firms underperform standard benchmarks over subsequent 3- and 5-year periods after issuing. The results from value-weighted portfolios, however, show that SEOs outperform three out of five benchmarks. The results from the Fama-French three factor model show that all of the 16 SEO portfolios (formed by size and book-to-market quartiles) have positively significant intercepts, and most loadings are significant. The size loadings from time series three-factor model of value-weighted portfolio show that SEO sample firm returns exhibit characteristics of large firms as opposed to those of small firms under equally weighted portfolios. Our results support the arguments that (1) the returns of issuing firms are not idiosyncratic, but rather covary with the common factors of nonissuing firms and that (2) the underperformance of SEOs is sensitive to the precise test specifications.

Good market timing skills can be an important factor contributing to hedge funds’ outperformance. In this chapter, we use a unique semiparametric panel data model capable of providing consistent short period estimates of the return correlations with three market factors for a sample of Long/Short equity hedge funds. We find evidence of significant market timing ability by fund managers around market crisis periods. Studying the behavior of individual fund managers, we show that at the 10% significance level, 17.12% of funds exhibit good linear timing skills and 21.32% of funds possess some level of good nonlinear market timing skills. Further, we find that market timing strategies of hedge funds are different in good and bad markets, and that a significant number of managers behave more conservatively when the market return is expected to be far above average and more aggressively when the market return is expected to be far below average. We find that good market timers are also likely to possess good stock selection skills.

We form portfolios based on return and liquidity and examine the effects of liquidity and other risk factors on asset pricing in the Chinese stock market. Our results show that the past loser-and-illiquid stock portfolios tend to outperform the past winner-and-liquid stock portfolios in the 1–12 months holding period. The excess return is significantly associated with the market-wide liquidity factor even when we control the three Fama-French and momentum factors. Cross-sectionally, the liquidity beta significantly affects the excess return even with control of other risk betas and other traditional liquidity proxies.

This study examines several aspects of active portfolio management by equity hedge funds between 1996 and 2013. Consistent with the idea that cross-sectional return dispersion is a proxy for the market’s available alpha, our results show that equity hedge funds achieve their strongest performance during periods of elevated dispersion. The performance advantage is robust to numerous risk adjustments. Portfolio managers may use the current month’s dispersion to plan the extent to which the following month’s investment approach will be active or passive. We also estimate the active share for equity hedge funds and find an average of 53%. We further document the average annual expense ratio for managing hedge funds’ active share to be about 7%. This figure is remarkably close to active expense ratios reported previously for equity mutual funds, which may be interpreted as evidence of uniform pricing for active portfolio management services.

We identify a novel concept of discretionary idiosyncratic volatility proxied by the idiosyncratic volatility component not related to the non-systematic industry volatility as a source of agency problems that have implications for firms’ cash holdings and their investment decisions. We find that firms with low discretionary idiosyncratic volatility, which likely captures discretionary effort and risk-taking by managers, have smaller cash reserves. Moreover, while high discretionary idiosyncratic volatility firms spend cash internally (internal capital building), low discretionary idiosyncratic volatility firms use it for external acquisitions, consistent with the “quiet life” hypothesis. Our findings thus indicate a need for reinforcement of existing regulations and corporate laws to control for agency costs, which could in turn reduce firm risk and the probability of financial meltdown at the aggregate level.

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.