We investigate the relationship between ESG rating divergence and stock returns from an investor’s perspective, to explore the impact of inconsistency among ESG rating agencies on the capital market. We construct ESG rating divergence data using ratings from three prominent ESG rating agencies in China. Our study is based on 54,679 company-quarter observations from 2018 to 2022, which covers 4,377 Chinese listed companies. Our findings demonstrate a significant negative impact of ESG rating divergence on stock returns, which we validate through a series of robustness tests and endogenous analyses. Notably, we find that investors’ expectations mediate the relationship between ESG rating divergence and stock returns. Further analyses show that only the divergence in social ratings have a significant inhibitory effect on stock returns. In addition, ESG rating divergence significantly impedes subsequent average ESG ratings. The adverse relationship between ESG rating divergence and stock returns is particularly pronounced in non-heavy pollution companies, non-state-owned companies, and companies with lower external attention.

Combining daily personnel records of an unlisted manufacturer with stock market data, we find that market overnight returns negatively predicts same-day worker output. The effect is greater on Mondays and extreme overnights. Analysis suggests that the stock market attracts (discourages) public attention when the overnight returns are extremely positive (negative), consistent with humans’ natural tendency of incorporating good news while discounting bad news. As a result, employees at work are disproportionally distracted by positive overnight returns, leading to reduced output. Additional evidence suggests that our results can hardly be explained with alternative distraction events or workers’ stock wealth concerns. This study reveals a novel channel through which the financial market shapes labor supply.

Despite the dominance of retail investors in the Chinese stock market, there’s a conspicuous absence of price momentum in weekly and monthly returns. This study uncovers the presence of price momentum in daily returns and, through a systematic analysis of trading heterogeneity among investors, links daily momentum to the attention and trading activities of new investors—a phenomenon particularly signiffcant in emerging stock markets. Furthermore, our ffndings indicate the existence of daily price momentum in various other emerging markets, contrasting with its relative scarcity in developed ones.

This study examines whether firms’ financial fraudulent behavior varies when local firms are led by nonlocal CEOs. Building on the social identity theory, we argue that nonlocal CEOs, due to their different location-based social identities, are perceived as outgroup leaders and face intergroup bias from stakeholders within local firms. Therefore, nonlocal CEOs are more likely to conform to laws and regulations and reduce corporate financial fraud to enhance their legitimacy in leading local firms. Using panel data on Chinese listed firms from 2007 to 2020, we find a significantly negative correlation between nonlocal CEOs and the likelihood of corporate financial fraud. Furthermore, our moderating analysis indicate that the negative effect of nonlocal CEOs on corporate financial fraud is stronger (a) for CEOs who have neverwon awards, (b) in firms with poor financial performance and (c) in regions with tight cultures. Additional mechanism tests indicate that nonlocal CEOs’ outgroup identity is more prominent in regions with low regional dialect diversity and local private-owned enterprises. Overall, these findings suggest that choosing a nonlocal CEO warrants attention from the firm’s top management teams and stakeholders.

Whilst there is increasing research attention on predicting financial distress, the existing literature is subject to two specific limitations. The first is that a firm can experience a financial distress event (e.g., loan default, bankruptcy) more than once, yet most studies that model corporate financial distress prediction treat financial distress as occurring only once. This approach leads to an inefficient use of data with all subsequent events being ignored and subsequently a decrease in statistical power. Second, to account for the lack of independence between observations of repeated event data, the extant research utilising hazard analysis either has a separate analysis for successive distressed events or relies upon robust standard errors. In addition to a much smaller sample, a separate analysis yields the models that can be used to predict the survival of a distressed firm rather than the survival of a firm generally. The method of robust standard errors, while innocuous to one-time event data, ignores the possible downward bias in coefficient estimates for repeated event data. To address these two limitations, we treat financial distress as repeated events and apply more advanced methods (generalised estimating equations, random effects, fixed effects, and a hybrid approach) to account for the lack of independence between observations in discrete time hazard analysis. These different approaches are applied to a sample of listed companies in China over the 2007‒2021 period. We find that variables that are not statistically significant in models based on one-time events data become statistically significant in the models based on repeated events data, and that coefficient estimates are larger in their magnitude with more advanced methods than with the method of robust standard errors. We also find that among the advanced methods, a hybrid approach achieves substantially better out-of-sample prediction, particularly over a long-term horizon than other approaches. Our results remain robust in tests of robustness.

This study examines the impact of Shenzhen Stock Exchange’s (SZSE) information disclosure ratings on investment efficiency in China. Based on a sample of Chinese A-share listed companies on the SZSE from 2001 to 2018, we discover that superior information disclosure ratings improve investment efficiency after controlling for various firm- and industry-level variables. Our findings remain valid after various robustness tests and using instrumental variables to address the endogeneity problem. Specifically, we find that improving information disclosure ratings help firms attract more investor attention, which leads to higher investment efficiency. In addition, this information disclosure effect is more pronounced for underinvestment firms and firms on the main board than for smaller firms on SEM (small- and medium-sized enterprise) and GEM (growth enterprise market) boards. Our evidence supports the idea that regulatory activities for information disclosure ratings of companies listed on China’s stock exchanges improve investment efficiency.

Fund managers’ ability to evaluate risk has important implications for their portfolio management and performance. We use a state-of-the-art deep learning model to measure fund managers’ forward-looking risk assessments from their narrative discussions. We validate that managers’ negative (positive) risk assessments lead to subsequent decreases (increases) in their portfolio risk-taking. However, only managers who identify negative risk generate superior risk-adjusted returns and higher Sharpe ratios, and have better intraquarter trading skills, suggesting that cautious, skilled managers are less subject to overconfidence biases. interestingly, only sophisticated investors respond to the narrative-based risk assessment measure, consistent with limited attention by retail investors.

We develop a machine learning (ML) approach to establish new insights into how memory affects ffnancial market participants’ belief formation processes in the field. Using analyst forecasts as proxies for market beliefs, we extract analysts’ mental contexts and recalls that shape forecasts by training an ML memory model. First, we find that long-term memories are salient in analysts’ recalls. However, compared to an ML benchmark trained to fit realized earnings, analysts pay more attention to distant episodes in regular times but less during crisis times, leading to recall distortions and therefore forecast errors. Second, we decompose analysts’ mental contexts and show that they are mainly shaped by past earnings and forecasting decisions instead of current firm fundamentals as indicated by the ML benchmark. This difference in contexts further explains the recall distortion. Third, our comprehensive memory model reveals the significance of specific memory features and channels in analysts’ belief formation, including the temporal contiguity effect and selective forgetting.

Exploiting ffnancial news stories data, we construct news-implied linkages and document a strong lead-lag effect of ffrms with shared news coverage in China’s stockmarket. The news-link momentum strategy generates a monthly return of 1.33% and a four-factor alpha (Liu et al., 2019) of 1.43%. While prior evidence on the attention dynamics among ffrms with joint news coverage is limited, we show that the momentum spillover of news-linked ffrms is largely driven by investor underreaction. The return predictability from news links is also robust to controlling for alternative economic linkages. The ffndings suggest that information diffuses sluggishly among news-connected ffrms, thereby providing new evidence on the implication of media coverage for pricing efffciency.

This study examines the relationship between analyst forecast dispersion or accuracy and supplier concentration of listed firms in China from 2008 to 2019. Our findings suggest that higher supplier concentration is associated with lower analyst forecast dispersion, which can be attributed to the increased attention it receives from analysts. Moreover, this effect is more pronounced when firms have less bargaining power and higher institutional ownership, indicating a greater reliance on the supply chain. Our study highlights the importance of disclosing supply chain information, which provides insight beyond traditional financial information.