The “Internet celebrity economy” is a business model aimed at capitalizing on online traffic based on the purchasing power of users on social media in which “influencers”—highly influential individuals—exercise their marketing power to create a fandom. China has witnessed an abrupt outbreak in its “Wanghong” (internet celebrity) economy since 2016, eventually leading to consecutive high closes for related stocks from around 2020. The empirical findings are as follows: First, investors’ attention to Wanghong stocks and cumulative abnormal returns (CARs) are significantly positively associated. However, operational results and CARs are weakly linked, implying that the economic impact of intense influencer marketing is short-lived, and abnormal returns constitute an anomaly. Second, the positive abnormal returns of Wanghong stocks last approximately six months, which overlaps with the boom period of the Wanghong index based on influencer news articles.

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market. We document pronounced existence of factor MAX anomaly in the United States and other G7 countries.

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market.

This paper studies the Level, Slope, and Curve factor model under different tests in the Chinese stock market. Empirical asset pricing tests reveal that the slope factor in the model represents either reversal or momentum effect for the Chinese stocks. Further tests on individual stocks demonstrate that the Level, Slope, and Curve model using effective predictor variables outperforms other common factor models, thus a filter in virtue of multiple hypothesis testing is designed to identify the effective predictor variables. In the filter models, the cross-section anomaly factors perform better than the time-series anomaly factors under different tests, and trading frictions, momentum, and growth categories are potential drivers of Chinese stock returns.

Drifting in the direction of earnings surprises for a prolonged period is a decades-puzzling financial anomaly, i.e., the “post-earnings-announcement drift” (PEAD). This paper provided a new simple measure of earnings surprise called ORJ. Based on ORJ, not only is the medium effect of investors’ attention on the relationship between earnings surprises and PEAD analyzed, but a tractable and profitable investing strategy is provided. Through comprehensive empirical analysis of the Chinese stock market, we found that i) both earnings surprises and investor attention can increase the degree of PEAD; ii) “good” (bad) earnings surprises strengthen (weaken) the degree of drift by attracting (decreasing) investor attention; it is asymmetric that the positive effects of “good” earnings surprises are stronger than that of “bad” earnings surprises on PEAD; and iii) the strategy obtains an average 6.78% return per quarter in excess of the market and only longs dozens of stocks . iv) Typical pricing factors such as the Fama-French three factors, illiquidity and company characteristics have little explanatory power for the returns of the strategy. This paper strongly shows the importance of monitoring overnight returns of earnings announcements to digging the unexpected information, reveals one mechanism of earnings surprises on PEAD and demonstrates the potential profitability of PEAD in the Chinese 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.

This paper is the first study to systematically discuss the predictive power of crosssectional asset pricing anomalies on aggregate market excess return time series in the Chinese A-share market. The paper summarizes the anomalies and uses linear methods with different shrinkage techniques to extract predictive information from highdimensional long-short anomaly portfolio returns datasets. We find that long-short anomaly portfolio returns show highly significant out-of-sample predictive power of aggregate market excess returns, both statistically and economically. Unlike similar studies on U.S. stocks, the predictive power stems from stronger limits of arbitrage in the short-leg when using bid-ask spread as a proxy but from stronger limits of arbitrage in the long-leg when idiosyncratic volatility or market capitalization is used as proxies.

This study examines whether sell-side analysts have the ability to disseminate information consistent with anomaly prescriptions in China. I adopt 192 trading-based and accounting-based anomaly signals to identify undervalued and overvalued stocks. Analysts tend to give more (less) favorable recommendations and earnings forecasts to undervalued (overvalued) stocks. On analyzing the information content, I find that analyst recommendations and earnings forecasts are consistent with accounting-based information rather than trading-based information. Analysts make recommendations and earnings forecasts consistent with anomalies, especially when firms experience relatively bad firm-level information. Additionally, undervalued (overvalued) stocks are associated with high (low) analyst coverage. The results indicate that analysts may contribute to mitigating anomaly mispricing and improving market efficiency in China.

This paper dissects the lottery-like anomaly in Chinese A-share stocks by decomposing total stock returns into overnight and intraday returns. Our findings indicate that the negative overnight returns are concentrated among lottery-like stocks, and the lottery-like anomaly is mainly driven by the overnight returns component. Considering the unique Chinese institutional features, our mechanism analysis reveals that the overnight returns induced lottery-like anomaly is more pronounced in stocks with high retail investors' gambling preference and high limits of arbitrage. Overall, our results suggest that investors optimism and trading constraints have a substantial impact on market efficiency in China.

Research into asset pricing anomalies in the China A-share market is hampered given the short time series of available returns. Even when average excess returns on candidate factor portfolios are economically sizeable, conventional portfolio sorting methods lack statistical power. We apply an efficient sorting procedure that combines firm characteristics with the covariance matrix. For the China A-share market, we find that the efficient sorting procedure doubles the t-statistics compared to conventional portfolio sorts, leading to nine instead of three significant anomalies over the postreform period from 2008 to 2020. We find significant size, value, low-risk, and returns-based anomalies. While portfolio characteristics differ between sorting methods, we find that efficient sorting portfolios highly correlate with equally weighted portfolios and capture the same underlying anomaly.