Existing research has found cross-sectional seasonality of stock returns—the periodic out- performance of certain stocks during the same calendar months or weekdays. We hypoth- esize that assets’ different sensitivities to investor mood explain these effects and imply other seasonalities. Consistent with our hypotheses, relative performance across individ- ual stocks or portfolios during past high or low mood months and weekdays tends to recur in periods with congruent mood and reverse in periods with noncongruent mood. Furthermore, assets with higher sensitivities to aggregate mood—higher mood betas— subsequently earn higher returns during ascending mood periods and earn lower returns during descending mood periods.

Testing the household Euler equation with consumption data faces econometric challenges caused by large measurement errors in the data and a short time span. We adopt a framework to test the Euler equation with stock market data to alleviate the measurement error and short time span issues. Utilizing a data-driven group panel data method, we identify a heterogeneous pattern of Euler equation failure among different groups of listed firms. The identified degree of Euler equation failure is significantly related to firm characteristics that are associated with famous stock anomalies. We show that the correlations between the degree of Euler equation failure and firm characteristics provide a new set of stylized facts that can help us distinguish between different economic theories on Euler equation failures and asset pricing anomalies, and identify challenges facing current theories.

We apply various test portfolios and alternative statistical methodologies to evaluate the performance of eleven prominent asset pricing models. To compile the test portfolios, we construct 105 anomalies in China and apply the 23 significant anomalies as test assets for model comparison. The results indicate that in the time-series test and anomalies explanation, the Hou et al. (2019) five-factor q model exhibits the best overall performance. The pairwise cross-sectional R^2s and the multiple model comparison tests affirm that the Hou et al. (2019) five-factor q model, the Fama and French (2018) six-factor (FF6) model and the Kelly et al. (2019) five-factor Instrumented Principal Component Analysis (IPCA5) model stand out as the top performers. Notably, the performance of the five-factor q model is insensitive to variations in experimental design.

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.