We empirically investigate the relation between anomaly portfolio returns and market return predictability in the Chinese stock market. Using 132 long-leg, short-leg, and long-short anomaly portfolio returns, we employ several shrinkage-based statistical learning methods to capture predictive...

We study which characteristics provide incremental predictive information for the cross-section of expected returns in the Chinese stock market. Our results provide empirical evidence for strong nonlinear relations between expected returns and selected characteristics, especially in the Trading...

In this paper, we find that the upside asymmetry calculated based on a new distribution-based asymmetry measure proposed by Jiang, Wu, Zhou, and Zhu (2020) is negatively related to average future returns in the crosssection of Chinese stock returns. By contrast, when using a conventional...

We study which characteristics provide incremental predictive information for the cross-section of expected returns in the Chinese stock market. Our results provide empirical evidence for strong nonlinear relations between expected returns and selected characteristics, especially in the trading...

We examine mutual fund market timing based on beta asymmetry from dynamic conditional correlation (DCC) model. We find significant timing using daily returns rather than monthly returns. The sensitivity of our findings to data frequency is consistent with funds altering their market exposure at...

In this paper, we propose two asymmetry measures for stock returns. Unlike the popular skewness measure, our measures are based on the distribution function of the data rather than just the third central moment. We present empirical evidence that greater upside asymmetries calculated using our...

We find that a large portion of U.S. equity mutual funds almost second-order stochastically dominates the market portfolio. Consistent with the canonical definition of second-order stochastic dominance, both fund investors and managers reveal their preference for funds with a higher degree of...