This study investigates the relationship between investors’ prior gains or losses and their adoption of extrapolative beliefs. Our findings indicate that investors facing prior losses tend to rely on optimistic extrapolative beliefs, whereas those experiencing prior gains adopt pessimistic extrapolative beliefs. These results support the theory of motivated beliefs. The interaction between the capital gain overhang and extrapolative beliefs results in noteworthy mispricing, yielding monthly returns of approximately 1%. Motivated extrapolative beliefs comove with investors’ survey expectations and trading behavior, and help explain momentum anomalies. Additionally, households are susceptible to this belief distortion. Institutional investors can avoid overpriced stocks associated with motivated (over-)optimistic extrapolative beliefs.

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.

We construct a stock learning index in China A-share market, which is based on a theoretical model of information and investment choice. The higher the learning index value, the more thoroughly the individual stock is learned. Our study shows that a stock with a high learning index will have a lower expected future return compared to a stock with a low learning index. Additionally, decomposition of predictive power shows that the predictive power of the learning index mainly comes from the persistence of its own predictive power, while the rest cannot be explained by changes in the volume of news (proxy for information flow). Moreover, the learning index can explain many market anomalies in China 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.

This study investigates the relationship between investors’ prior gains or losses and their adoption of extrapolative beliefs. Our findings indicate that investors facing prior losses tend to rely on optimistic extrapolative beliefs, whereas those experiencing prior gains adopt pessimistic extrapolative beliefs. These results support the theory of motivated beliefs. The interaction between the capital gain overhang and extrapolative beliefs results in noteworthy mispricing, yielding monthly returns of approximately 1%. Motivated extrapolative beliefs comove with investors’ survey expectations and trading behavior, and help explain momentum anomalies. Additionally, households are susceptible to this belief distortion. Institutional investors can avoid overpriced stocks associated with motivated (over-)optimistic extrapolative beliefs.

We investigate the cross-sectional factors of Chinese corporate bond returns via the reducedrank regression analysis (RRA) proposed by He et al. (2022). We collect 37 individual bond characteristics in the extant literature using a new dataset and construct 40 factor portfolios. Empirically, we find that the four-factor models created by RRA outperform the traditional factor models, PCA, and PLS factor models, both in-sample and out-of-sample. Among the 40 factors, the bond market factor is the most substantial predictor of future bond returns. In contrast, other factors provide limited incremental information for the cross-sectional pricing. Therefore, it is necessary to find more new bond factors. We further find that stock market anomalies do not improve the explanatory power of the RRA factor models. In particular, stock market anomalies can only partially explain the systematic part of bond returns in the RRA framework and have almost no explanatory power for the idiosyncratic component.

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.

The aim of the study is to investigate the behaviour of the Chinese government bond yield curve before and during the COVID-19 pandemic. Its methodology comprises the techniques of time series analysis, correlation analysis and dimensionality reduction. The main empirical results show that in the pandemic period, the behaviour of the Chinese government bond yield curve differs significantly from that before the outbreak of COVID-19. This is evidenced by the weaker correlations among the analysed yields, the presence of anomalies, heterogeneous behaviour and probable arbitrage opportunities at the long-term end of the studied yield curve, as well as the significant changes in the main factors of its dynamics. The research also reveals that prior to the COVID-19 pandemic, portfolios composed of Chinese government bonds could be well protected against interest rate risk even by using traditional parallel shift immunization techniques. However, after the outbreak of the COVID-19 pandemic the use of such techniques would be relatively effective for portfolios of Chinese government bonds with maturities between 1 and 5 years, while portfolios that include Chinese government bonds with maturities greater than 7 years should be either hedged against all the three factors of the yield curve dynamics or be used only for arbitrage strategies.

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.