We study how politicians' activities affect the stock market and firm performance. Using hand-collected data on China's national leaders' corporate visits, we investigate the industry-wide implications of these visits. We find that over the six days surrounding a visit, an average industry peer's value increases by 2\% of its total assets. This result reflects investors' favourable interpretation of leaders' visits as a signal of more government support for the entire industry. However, the industry peer's profitability plummets by more than 15\% in the next three years. Further analysis reveals that after the visits, industry peers increase their investments, presumably in anticipation of additional government subsidies and credits. However, these resources are insufficient, and the profitability of these firms suffers. Our findings suggest that national leaders' visits do not help boost the targeted industries, and firms should carefully interpret the politicians' activities.

Facing external shocks, maintaining and stabilizing imports is a major practical issue for many developing countries. We first document that sourcing market switching (SMS) is widespread for Chinese firms (For 2000-2016, SMS firms account for 76.29% of all import firms and 96.30% of total import value). Then we use Chinese firm-level data to show that SMS can significantly mitigate the negative impacts of international uncertainty on imports, which further stabilizes firm employment and innovation, leading to increases in national and even world welfare. Possible motivations for SMS include stabilizing import supply, lowering import tariffs, raising the real exchange rate, and increasing product switching. We also find that the effects of SMS vary by the type of uncertainty, firm ownership, productivity, credit constraints, trade mode, and product features.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that tones and return forecasts extracted by LLMs from news significantly predict future returns. The equal- and value-weighted long minus short portfolios yield annualized returns of 90% and 69% on average for the ensemble model. Given that these news articles are public information, the predictive power lasts about two days. More interestingly, the signals extracted by LLMs contain information about firm fundamentals, and can predict the aggressiveness of future trades. The predictive power is noticeably stronger for firms with less efficient information environment, such as firms with lower market cap, shorting volume, institutional and state ownership. These results suggest that LLMs are helpful in capturing under-processed information in public news, for firms with less efficient information environment, and thus contribute to overall market efficiency.

Recent research highlights the significant impact of managerial industry tournament incentives on internal firm decisions. However, their potential impact on external stakeholders-in the context of evolving product market relationships-has received scant attention. To address this gap, we examine the effect of customer aspiration, incentivized by CEO industry tournaments (CITIs), on supplier innovation. Utilizing customer-supplier pair-level data from 1992 to 2018, we establish that customer CITIs enhance supplier innovation, both in quantity and quality. Additionally, we identify that CITIs positively impact the relationship-specific innovation and market valuation for suppliers. The effect of CITIs is more pronounced when customers are larger, geographically closer, socially connected, and have long-standing relationships with their suppliers. The results remain robust to alternative specifications and considering potential endogeneity issues. Our study highlights the bright side of executives’ industry tournament incentives, which not only drive innovation within the sector but can also positively influence related sectors within the supply chain.

We study how financial markets integrate news about pollution abatement costs into firm values. Using China’s automation of pollution monitoring, we find that firms with factories in bad-news cities---cities that used to report much lower pollution than the automated reading---see significant declines in stock prices. This is consistent with the view that investors expect firms in high-pollution cities to pay significant adjustment and abatement costs to become “greener.” However, the efficiency with which such information is incorporated into prices varies widely---while the market reaction is quick in the Hong Kong stock market, it is considerably delayed in the mainland ones, resulting in a drift. The equity markets expect most of these abatement costs to be paid by private firms and not by state-owned enterprises, and by brown firms and not by green firms.

This paper revisits the default-crash risk relation in the context of China. We find that firms with higher default risk have lower stock price crash risk both in monthly and yearly frequencies. To identify the causal effect, we use the first-ever default event in China’s onshore bond market in 2014 as an exogenous shock to the strength of implicit guarantees. The negative relation arises from the active involvement of the government before 2014 and creditors after 2014 in corporate governance. Consistent with the external scrutiny mechanism, the impact of default risk on stock price crashes is stronger in situations in which creditors are more likely to engage in active monitoring (i.e., firms with higher liquidation costs, lower liquidation value, and higher levels of information asymmetry), with these effects primarily observed in the post-2014 period. Overall, our study highlights the role of the “invisible hand” in the absence of the “visible hand.”

A second-tier patent system with relatively low protectability standards has been adopted by many countries, but empirical evidence on how it is used by firms israre. Using Chinese patent data, we exploit “parallel filings” – where a second-tierpatent is filed simultaneously with an invention patent – to shed light on its usein practice. The data indicate that while parallel filings appear to be inventionswith a narrower scope, they are cited more frequently in the early years and morelikely to be licensed or transferred compared to inventions protected by standardpatents. We provide evidence that parallel filing is likely a strategic choice forshort-life-cycle technologies that achieve high value early in their lifetime but decayfast. The rapid issuance of the second-tier patent facilitates knowledge diffusionand technology transfer, thereby helping the patentees capitalize on the value of fast-moving technologies. This study provides some much-needed empirical evidenceon how the quick procedure of the second-tier patent system serves short life-cycletechnologies.

How much value has been lost in the Chinese bankruptcy system due to excessive liquidation of companies whose going concern value is greater than the liquidation value? I compile new judiciary bankruptcy auction data covering all bankruptcy asset sales from 2017 to 2022 in China. I estimate the valuation of the asset for both the final buyer and creditor through the revealed preference method using an auction model. On average, excessive liquidation results in a 13.5% welfare loss. However, solely considering the liquidation process, an 8% welfare gain is derived from selling the asset without transferring it to the creditors. Firms that are (1) larger in total asset size, (2) have less information disclosure, (3) have less access to the financial market, and (4) possess a higher fraction of intangible assets are more vulnerable to such welfare loss. Overall, this paper suggests that policies promoting bankruptcy reorganization by introducing distressed investors who target larger bankruptcy firms suffering more from information asymmetry will significantly enhance welfare in the Chinese bankruptcy market.

This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

This study investigates how abnormally hot temperatures affect firms’ environmental behavior in China. We find that firms exposed to abnormally hot temperatures participate in more environmental engagement. We also find that this improvement effect is driven mainly by environmental concerns, including public concerns, CEOs, and governments. Our results remain intact after an array of robustness tests. Further analysis shows that the effect of abnormally hot temperatures on corporate environmental engagement is more pronounced in SOEs, heavily polluting firms, and firms located closer to local environmental protection agencies. Moreover, the positive impact of environmental engagement on firm value is stronger when firms are exposed to abnormally hot temperatures. Overall, this study sheds light on the potential stimulation of firms’ environmental actions by global warming, which is yet to be fully understood.