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.

Behavioral theories predict that firm valuation dispersion in the cross-section (‘‘dispersion’’) measures aggregate overpricing caused by investor overconfidence and should be negatively related to expected aggregate returns. This paper develops and tests these hypotheses. Consistent with the model predic- tions, I find that measures of dispersion are positively related to aggregate valuations, trading volume, idiosyncratic volatility, past market returns, and current and future investor sentiment indexes. Disper- sion is a strong negative predictor of subsequent short- and long-term market excess returns. Market beta is positively related to stock returns when the beginning-of-period dispersion is low and this rela- tionship reverses when initial dispersion is high. A simple forecast model based on dispersion signifi- cantly outperforms a naive model based on historical equity premium in out-of-sample tests and the predictability is stronger in economic downturns.

This study explores the role of sentiment in predicting future stock return volatility in the Chinese A-share market. Specifically, we conduct a composite sentiment index capturing both investor and manager sentiment. The former is measured by overnight returns, and the latter is measured by a textual tone based on the information in the Management Discussion and Analysis section of the annual reports. Empirically, we find that the composite index is positively associated with subsequent stock realized volatility and the result remains robust after controlling for a set of firm characteristics and state ownership. Besides, the result also shows that investor attention can help dissect the sentiment—volatility relation.

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 study focuses on the relationship between individual stocks’ systemic tail risk and future returns. Analyzing data from China's A-share market, we document an abnormal negative crosssectional relationship between stocks’ systemic tail risk and returns, which cannot be explained by firm-specific characteristics. We show that the joint effect of investor expectation of stock return persistence and investor sentiment contributes to the systemic tail risk anomaly. Investors tend to underestimate the loss persistence of stocks that have suffered large losses in the most recent period and overprice such stocks, leading to a strong negative relationship between stock systemic tail risk and return. In addition, constraints on short selling exacerbate individual stocks’ systemic tail risk and also explain the systemic tail risk anomaly.

Investor sentiment has a crucial impact on stock market pricing. Based on prospect theory and partial least squares, we innovatively construct an investor sentiment indicator and verify the validity of the indicator. Compared with other sentiment indices, our investor sentiment index is more effective in in-sample and out-of-sample forecasting. At the same time, from a cross-sectional perspective, both the portfolio analysis and the Fama-Macbeth regression show that the partial least squares results are a better indicator of returns than other indices. The driving force of the sentiment index we construct comes from investors’ perceptions of forecast cash ffow, discount rate, and volatility.

From an industry perspective, this paper applies the quantile regression to investigate the impact of investor sentiment (IS) and China’s/U.S. geopolitical risks (GPR) on Chinese stock market volatility. Considering the structural break of the stock market for theperiod2003/02-2021/10, we find that the impact of geopolitical risk on stock market volatility is highly heterogeneous, and its significance mostly appears in the upper and lower tails. At the market level, China’s and U.S. GPR/IS and their interaction effects have no significant impact on China’s stock market volatility. However, there has an asymmetric dependence between China’s and U.S. GPR/IS and stock market volatility, and the dependence structure is changing. At the industry level, the current and lagging effects of China’s and U.S. GPR on industry stock market volatility are heterogeneous. Second, for most industries, China’s and U.S. GPR/IS can exacerbate industry stock market volatility both in bullish and bearish markets. In addition, China’s and U.S.GPR/IS and their interaction effects are heterogeneous and asymmetric, and the effects changes with the break point. Finally, compared with China’s GPR, the U.S. GPR has a larger impact on the industry stock market. The interactive effects of the U.S. GPR and IS can influence more industry stock market volatility.

We track the fundamental informed traders' (FITs) behavior and show the fundamental momentum effect in the Chinese stock market. We train the deep learning model with a set of fundamental characteristics to extract fundamental implied component from realized returns. The fundamental part characterizes the price movement driven by FITs. Fundamental momentum differentiates from the fundamental trend and is not quality minus junk (QMJ) factor. Underreaction bias helps explain the strategy, as it generates stronger profit during periods of low investor sentiment and aggregate idiosyncratic volatility. Fundamental momentum is not sensitive to changing beta and robust in subsamples and machine learning models.

We study the role of Chinese government guidance funds (GGFs) in value creation for investee firms. Using a sample of 2,855 firms that went public during the period of 2010-2021, we show that GGF-backed IPO firms had higher initial returns than non-VC-backed IPO firms and nonGGF VC-backed IPO firms. After decomposing the initial return into IPO underpricing and market overvaluation, we find that GGF-backed firms enjoyed higher overvaluation and lower underpricing than other firms. Consistent with investor sentiment and information asymmetry hypothesis, our results indicate that public investors value the benefits of political resources more than the costs of government interference associated with GGF sponsoring. However, GGF-backed firms did not outperform other-VC-backed firms when post-IPO long-term stock, operating and innovation performance is assessed. The divergence in the effects of GGFs observed in the financial and product markets reveals the complexity in evaluating the role of GGFs in value creation.

We explore the value premium in the Chinese stock market and how to exploit it using a new investor sentiment index. We extensively discuss the performance of BM, CFP, EP and SP factors in China. Consistent with the experience of other countries, BM generates more of a value premium in small cap performance, while EP generates more of a value premium in large cap stocks in the Chinese stock market. First, we construct a novel value factor based on BM, EP and SP. We obtain the loading weights of each value indicator in each market value by partial least squares. The novel value factor outperformed all other value factors. Second, we explore the relationship between value premium and investor sentiment. Different from evidence from most developed countries, the value stocks perform better than growth stocks in the bull market in China. Our evidence suggests investing in value stocks can get more profit when market sentiment is low.