We construct six monthly abnormal weather indicators by employing physical weather data of temperature, humidity, wind speed, precipitation, sunshine hours and sky coverage in China. We compute the abnormal weather betas to proxy for abnormal weather risks and investigate the pricing effects of these risks in the Chinese stock market. We find a low abnormal weather beta premium in the cross-section, i.e., stocks with high betas to changes in the abnormal weather index significantly underperform those with low betas by 0.10% to 0.37% per month on a risk-adjusted basis. In particular, the low abnormal weather beta premiums of temperature, sunshine hours, and sky coverage are significant over the subsequent six months. The further analyses suggest that the abnormal weather betas exhibit persistent long-term predictive power and have significant explanatory power for size, value, and long-term reversal anomalies. We further explore several potential explanations for the low abnormal weather beta premiums from various aspects, including firm characteristics, industry heterogeneity, firm performance, investor behavior and institutional holdings. The results suggest that neither firm-level characteristics nor industry heterogeneity can adequately explain the low abnormal weather beta premiums. The anomalies related to abnormal weather sensitivity are likely driven by the interaction of investor behavioral bias and arbitrage barriers, rather than compensation for fundamental risk.

This study investigates how mutual funds impact the stock market by ana-lyzing the relationship between mutual fund investment behaviours (holding and trading) and stock returns and realized volatility in the Chinese market. It is found that stocks widely held or bought by mutual funds can earn higher excess returns, and more importantly, the trading measures out-perform the holding measures, which is evident by the portfolio analysis and Fama-MacBeth regressions. Moreover, the proportional holding, pro-portional trading and shares trading measures positively and significantly predict future realized volatility. Meanwhile, a weak asymmetric effect in the share-trade measure is found.

In view of the divergence of existing research results on the relationship between ESG and investment returns, this paper constructs an S-score metric, which comprehensively measures corporate sustainability performance. It further tests the applicability of a sustainability-based investment strategy using this metric in China's A-share market. Using Shanghai and Shenzhen A-shares from May 2016 to April 2024 as the research sample, the S-score is constructed across five dimensions: Profitability, Growth Opportunities, Investment Efficiency, Risk Mitigation, and ESG Performance. The S-score is calculated using Z-score standardization and entropy weighted. Strategy effectiveness was tested through univariate grouping, bivariate grouping, and Fama-Macbeth regression, further examining strategy performance under varying market conditions, holding periods, and information environments. The study finds that the S-score demonstrates significant discriminative power for cross-sectional stock returns. The hedge portfolio based on this metric achieved an annualized excess return of 7.943% after adjusting for the China three-factor (CH-3) model. Its predictive power remains robust after controlling for variables such as market capitalization and book-to-market ratio, delivering significant positive returns across bull and bear markets, extreme pandemic conditions, and holding periods of up to eight years. From a behavioral finance perspective, this paper reveals that explanations such as the gradual diffusion of information and investors' limited attention span help elucidate the profitability of the S-score strategy. The findings demonstrate the effectiveness of Sustainable Investing strategies in China's A-share market, indicating that ESG-integrated factor investing can optimize resource allocation. This research contributes empirical evidence on Sustainable Investing in emerging markets, providing insights for policy formulation and practical implementation while supporting the virtuous cycle between Sustainable Investing and long-termism.

Against the backdrop of potential irrational trading behaviours in financial markets, this study investigates the V-shaped disposition effect in the selling activities of portfolios managed by securities investment funds in China. Utilising quarterly holdings data (2018–2024) of Chinese securities investment funds, alongside daily turnover rates and closing prices of their fund-heavy stocks listed in China's A-share market, a Fama-MacBeth regression analysis is conducted. The empirical results provide robust evidence of a significant V-shaped disposition effect in these fund investments, primarily driven by speculative trading. Moreover, this effect significantly and positively predicts future stock returns of Chinese A-shares. This study enhances understanding of institutional investors' trading behaviours—particularly mutual funds in China—and their decision-making processes in financial markets.

We conduct a comprehensive study on momentum spillovers in the Chinese stock market using varioustypes of economic linkages. We find that the news co-mention momentum spillover is signiffcantly strongercompared to other forms of momentum spillovers. Using spanning tests and Fama-MacBeth regressions,we further show that the news co-mention momentum spillover uniffes all different forms of momentum spillover effects in the Chinese stock market. Notably, the analyst co-coverage momentum spillover effect, which is the dominant species in the US stock market, is subsumed by the news co-mention momentum spillover effect in the Chinese stock market. We further explore the differences in the information content of links implied by news co-mentioning and other proxies. We suggest that the dominance of news co-mention momentum spillover over others can be attributed to two primary factors: comprehensive information and prompt updates.

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.