This paper investigates the sales seasonality premium (Gustavo et al., 2020) in the Chinese stock market. We document a significant sales seasonality premium in the cross section of stocks listed on Chinese market. A long-short strategy of buying low-sales stocks and shorting high-sales season stocks generate a monthly return of 0.84% with a Newey-West t statistic of 2.94. The return spreads between low-sales season stocks and high-sales season stocks are robust to well-known anomalies and are larger in magnitude among large-cap companies. We also find that the return spreads are more pronounced within industries with relatively fixed patterns in product demand. The sales seasonality premium in the Chinese stock market is found to be a combined result of investor rationality and irrationality.

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.

This paper examines the effects of superstitious psychology on investors’ decision making in the context of Mercury retrograde, a special astronomical phenomenon meaning “everything going wrong”. Using natural experiments in the Chinese stock market, we find a significant decline in stock prices, approximately -3.14% in the vicinity of Mercury retrogrades, with a subsequent reversal following these periods. The Mercury effect is robust after considering seasonality, the calendar effect, and well-known firm-level characteristics. Our mechanism tests are consistent with model-implied conjectures that stocks covered by higher investor attention are more influenced by superstitious psychology in the extensive and intensive channels. A superstitious hedge strategy motivated by our findings can generate an average annualized market-adjusted return of 8.73%.

This paper examines the effects of superstitious psychology on investors’ decision making in the context of Mercury retrograde, a special astronomical phenomenon meaning “everything going wrong”. Using natural experiments in the Chinese stock market, we find a significant decline in stock prices, approximately -3.14% in the vicinity of Mercury retrogrades, with a subsequent reversal following these periods. The Mercury effect is robust after considering seasonality, the calendar effect, and well-known firm-level characteristics. Our mechanism tests are consistent with model-implied conjectures that stocks covered by higher investor attention are more influenced by superstitious psychology in the extensive and intensive channels. A superstitious hedge strategy motivated by our findings can generate an average annualized market-adjusted return of 8.73%.

Although China has the largest trading volume of commodity futures, limited studies have been devoted to the term structure of Chinese commodity futures. This paper takes the tools in functional data analysis to understand the term structure of commodity futures and forecast its dynamic changes at both short and long horizons. Functional ANOVA has been applied to examine the calendar e_ect of term structure in level and _nd the seasonality in the commodity futures of coking coal and polypropylene. We use an h-step functional autoregressive (1) model to forecast the dynamic change of term structure. Comparing with native predictor, in-sample and out-of-sample forecasting performance indicate that additional forecasting power is gained by using the functional autoregressive structure. Although the dynamic change at short horizons is not predictable, the forecasts appear much accurate at long horizons due to the stronger temporal dependence. The predictive factor method has a better in-sample _tting, but it cannot outperform the estimated kernel method for out-of-sample testing, except for 1-quarter-ahead forecasting.