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 show that attention constraints of decision makers create barriers to financial inclusion. Using administrative data on retail loan screening processes, we find that attention-constrained loan officers exert less effort reviewing applicants from lower social or economic status (SES) backgrounds and reject them more frequently. More importantly, when externally imposed increases in workload tighten attention constraints, loan officers are even more prone to quickly rejecting low SES applicants but quickly accepting very high SES applicants without careful review, further widening the approval rate gap between high and low SES applicants — a unique prediction of the attention-based mechanism. Our findings suggest that decision makers’ attention constraints could amplify taste-based and statistical discrimination, which further exacerbates financial inclusion gaps, and that financial technologies that reduce information-processing costs may promote more balanced financial access.

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.

This paper examines how globalization process shapes the corporate ESG efforts in emerging markets. Using a staggered difference-in-difference model based on the gradual inclusion of China's A-shares in the MSCI index, we find that public companies improved their ESG performance and disclosure quality after being included. The results are robust to propensity score matched sample. Notably, the impact on ESG disclosure was significantly greater than on ESG performance, and the effect is more pronounced for non-SOEs and firms with weak governance. The inclusion also leads to significant increasesin foreign holdings, the proportion of women directors, and analyst attention, which have promoting effects on corporate ESG performance and disclosure ratings. This study sheds light on the macro-level determinants of corporate ESG engagement.