This article presents a comprehensive examination of technical trading rules in the Chinese yuan-based foreign exchange market. The investigation employs daily data spanning seven years for 14 developed and 10 emerging market currencies. The analysis encompasses a vast universe of 41,660 trading rules, representing a significant expansion over the previous studies. The stepwise tests, which was employed to address the data-snooping bias, discover excess profitability in at least half of the developed and emerging currencies, implying the heterogeneous market efficiency across currencies. Our results are robust to sub-sample analysis and different parameter values of the stepwise tests.

We find that investors react more favorably to corporate announcements of share repurchases, SEOs, earnings, dividend changes, and acquisitions if the announcement is made immediately prior to or on holidays. These announcements are associated with more positive reactions for favorable events and less negative reactions for unfavorable events. This effect is robust to controls for market conditions and a selection bias, is accompanied by subsequent reversals, and is present in several international markets. Our findings suggest that predictable individual mood changes can cause biases in market reactions to firm-specific news.

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.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese public news articles to predict stock returns. Based on representativeness and influences, we consider seven LLMs: BERT, RoBERTa, FinBERT, Baichuan, ChatGLM, InternLM, and their ensemble model. We show that news tones and return forecasts extracted by LLMs from Chinese news significantly predict future returns. The value-weighted long-minus-short portfolios yield annualized returns between 35% and 67%, depending on the model. Building on the return predictive power of LLM signals, we further investigate its implications for information efficiency. The LLM signals contain firm fundamental information, and it takes two days for LLM signals to be incorporated into stock prices. The predictive power of the LLM signals is stronger for firms with more information frictions, more retail holdings and for more complex news. Interestingly, many investors trade in opposite directions of LLM signals upon news releases, and can benefit from the LLM signals. These findings suggest LLMs can be helpful in processing public news, and thus contribute to overall market efficiency.

This paper utilizes the COVID-19 pandemic as an exogenous shock to investor uncertainty and examines the effect of uncertainty on stock liquidity. Analyzing data from Chinese listed firms, we find that stock liquidity dries up significantly in response to an increase in uncertainty resulting from regional pandemic exposure. The underlying reason for the decline in stock liquidity during the pandemic is a combination of earnings and information uncertainty. Funding constraints, market panic, risk aversion, inattention rationales, and macroeconomics factors are considered in our study. Our findings corroborate the substantial impact of uncertainty on market efficiency, and also add to the discussions on the pandemic effect on financial markets.

This study examines the influence of ESG report textual similarity on stock price synchronicity within the Chinese A-share market. Using advanced textual analysis methods, including TF-IDF and LDA, we measure the textual similarity of ESG reports among industry peers. Our results reveal a positive association between ESG report textual similarity and stock price synchronicity, suggesting that ESG reports with high textual resemblance may not convey distinct market information. This research underscores the importance of textual distinctiveness in ESG reports and offers a fresh perspective on the role of non-financial information, particularly related to CSR, in stock pricing dynamics. By emphasizing the significance of ESG report textual distinctiveness, we contribute to the broader discourse on ESG disclosure behaviors and their implications for capital market efficiency.

This study examines the influence of ESG report textual similarity on stock price synchronicity within the Chinese A-share market. Using advanced textual analysis methods, including TF-IDF and LDA, we measure the textual similarity of ESG reports among industry peers. Our results reveal a positive association between ESG report textual similarity and stock price synchronicity, suggesting that ESG reports with high textual resemblance may not convey distinct market information. This research underscores the importance of textual distinctiveness in ESG reports and offers a fresh perspective on the role of non-financial information, particularly related to CSR, in stock pricing dynamics. By emphasizing the significance of ESG report textual distinctiveness, we contribute to the broader discourse on ESG disclosure behaviors and their implications for capital market efficiency.

Taking the national carbon market and seven local carbon markets in China, we use DEA model to measure market efficiency, and then classify them by hierarchical cluster method. Efficiency of the national carbon market and local carbon markets of Beijing, Shenzhen, Hubei and Shanghai are leading, while Guangdong is in the middle; Chongqing and Tianjin are left behind. Room for improvement and scale returns are further analyzed, and suggestions for each carbon market are proposed finally.

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.