This paper improves the expected return variable and the corresponding trend factor documented by Han, Zhou, and Zhu (2016) and reveals the incremental predictability of this novel expected return measure on stock returns in the Chinese stock market. Portfolio analyses and firm-level cross-sectional regressions indicate a significantly positive relation between the improved expected return and future returns. These results are robust to the short-, intermediate-, and long-term price trends and other derived expected returns. Our improved trend factor also outperforms all trend factors constructed by other expected returns. Additionally, we observe that lottery demand, capital states, return synchronicity, investor sentiment and information uncertainty can help explain the superior performance of the improved expected return measure in the Chinese stock market.

This paper utilizes the COVID-19 pandemic as an exogenous shock to investor uncertainty and examines the effect of uncertainty on stock liquidity. Analyzing data from Chinese listed firms, we find that stock liquidity dries up significantly in response to an increase in uncertainty resulting from regional pandemic exposure. The underlying reason for the decline in stock liquidity during the pandemic is a combination of earnings and information uncertainty. Funding constraints, market panic, risk aversion, inattention rationales, and macroeconomics factors are considered in our study. Our findings corroborate the substantial impact of uncertainty on market efficiency, and also add to the discussions on the pandemic effect on financial markets.

This paper improves the expected return variable and the corresponding trend factor documented by Han, Zhou, and Zhu (2016) and reveals the incremental predictability of this novel expected return measure on stock returns in the Chinese stock market. Portfolio analyses and ffrm-level cross-sectional regressions indicate a signiffcantly positive relation between the improved expected return and future returns. These results are robust to the short-, intermediate-, and long-term price trends and other derived expected returns. Our improved trend factor also outperforms all trend factors constructed by other expected returns. Additionally, we observe that lottery demand, capital states, return synchronicity, investor sentiment and information uncertainty can help explain the superior performance of the improved expected return measure in the Chinese stock market.

This paper examines the impact of idiosyncratic risk on the cross-section of weekly stock returns from 1963 to 2006. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size e§ect, value premium, return momentum and short-term reversal to measure relative mispricing. I ?nd that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or ?rm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these ?ndings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost.

This paper examines the impact of idiosyncratic risk on the cross-section of weekly stock returns from 1963 to 2006. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size effect, value premium, return momentum and short-term reversal to measure relative mispricing. I ?find that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or ?rm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these ?findings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost.

This study examines the role of information uncertainty (IU) in predicting cross-sectional stock returns. We define IU in terms of "value ambiguity", or the precision with which firm value can be estimated by knowledgeable investors at reasonable cost. Using several different proxies for IU, we show that: (1) On average, High IU firms earn lower future resturns (the "mean" effect), and (2) Price and earnings momentum effects are much stronger among high IU firms (the "interaction" effect). These findings are consistent with theoretical models that feature investor overconfidence (Daniel et al. (1998)) and information cascades (Bikhchandani et al. (1992)). Specifically, our evidence indicates that high IU exacerbates investor overconfidence and limits rational arbitrage.