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.

In recent years, the phenomenon of ‘greenwashing’ of corporate environmental, social and governance (ESG) reports has been on the rise, seriously interfering with normal capital investment behaviour. This paper explores the relationship between investor concerns and the ‘greenwashing’ of corporate ESG reports, using Chinese A-share listed companies from 2014 to 2021 as a sample. The results show that green finance policies significantly contribute to the ‘greenwashing’ of ESG reports of heavily polluting companies. Under the pressure of green finance policies, heavily polluting companies have more incentives to ‘greenwash’ their ESG reports to relieve financing pressure. This paper’s findings suggest that green finance policies that promote enterprises’ green transformation may negatively induce enterprises to make false ESG disclosures.

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.

We explore the value premium in the Chinese stock market and how to exploit it using a new investor sentiment index. We extensively discuss the performance of BM, CFP, EP and SP factors in China. Consistent with the experience of other countries, BM generates more of a value premium in small cap performance, while EP generates more of a value premium in large cap stocks in the Chinese stock market. First, we construct a novel value factor based on BM, EP and SP. We obtain the loading weights of each value indicator in each market value by partial least squares. The novel value factor outperformed all other value factors. Second, we explore the relationship between value premium and investor sentiment. Different from evidence from most developed countries, the value stocks perform better than growth stocks in the bull market in China. Our evidence suggests investing in value stocks can get more profit when market sentiment is low.

We analyze the relationship between insider trading density and the future stock returns in Chinese listed companies. We introduce a new aspect of the trading pattern, insider trading density, to investigate the information advantage held by insiders. Insiders who trade at a low density during their tenure are less likely to be expected to trade than high trading density insiders. The expectedness of trading patterns reflects insiders’ trading incentives and conveys valuable information to predict future stock prices. Controlling for company, deal, and insider-specific characteristics, we find that low trading density insiders earn higher excess returns than high trading density insiders in a portfolio mimicking long strong purchases and short strong sales. In addition, we show that the insider’s position is a source of information advantage: prominent officers such as CEOs and CFOs are more likely to be low trading density insiders, while non-executive directors and supervisors are more likely to be high trading density insiders.

In forecast surveys of aggregate macroeconomic and financial variables, the correlation between forecast errors and forecast revisions is positive at the consensus level, but negative at the individual level. Past literature has interpreted this discrepancy as evidence of underreaction to news at the aggregate level and overreaction at the individual level. In this paper, I challenge this view by arguing that noise in predictive judgment can account for the difference. Using a stylized model, I examine how introducing judgment noise at the individual level changes the interpretation of the correlation coefficients. First, a negative coefficient at the individual level no longer necessarily means overreaction. Second, the coefficient at the consensus level underestimates the degree of underreaction. Using forecast survey data, I provide evidence that judgment noise is large enough to reconcile the difference between the two coefficients. The structural parameter measuring over-/underreaction mainly points to underreaction, regardless of whether the model matches correlation coefficients at the individual or aggregate level.

Understanding the impacts of new technology and innovations on the banking sector is important and of growing interest. However, there is limited research on the detailed channels of the impacts, and consequently, the evaluations for the aggregate welfare impacts. We contribute both empirically and quantitatively. We construct a new data set for Chinese banks. We ffnd banking innovations can improve efficiency, and mostly reduce non-interest costs but not so much on deposit rates. We show the ffnding is quite robust under a battery of checks. In a new structural, quantitative model, banks have heterogeneous capital, decide innovation investment and also risky lending, face regulations on the capital requirement and have limited liability. When aggregate new technology improves, it can reduce financial intermediation costs and social deadweight loss; however, it will also change the bank’s risk consideration and increases moral hazard when the cost is largely reduced. We also find several other new implications for R&D investment credit policy and Capital Requirement policy (CAR).

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.

This paper uses the enactment of China’s 2007 Property Law (the Law), which reduces the risk of expropriation by local governments, as the setting to investigate the importance of property rights protection for private firm investment. Using propensity score matching and a difference-in-differences design, we find that firms facing weaker property rights protection prior to the Law significantly increase their investment and investment efficiency after the Law. Cross-sectional analyses document evidence consistent with a decrease in firms’ perceived expropriation risk as the main mechanism underlying the Law’s effect. Finally, we show that the Law improves local economic outcomes and employment.