We investigate the unintended real effects of regulator-led minority shareholder activism on corporate innovation. We use manually collected data from the China Securities Investor Services Center (CSISC), a novel regulatory investor protection institution controlled by the China Securities Regulatory Commission (CSRC) that holds 100 shares of every listed firm. We find that by exercising its shareholder rights, the CSISC substantially curtails the innovation output of targeted firms. This effect is amplified in cases involving a high level of myopic pressure and few innovation incentives. We further observe variation in the real effects of different intervention methods. Textual analysis reveals that CSISC intervention with a myopic topic and negative tone contributes to a decrease in innovation. The results of a mechanism analysis support the hypothesis that regulator-led minority shareholder activism induces managerial myopia and financial constraints, impeding corporate innovation. Furthermore, CSISC intervention not only diminishes innovation output but also undermines innovation efficiency. In summary, our findings suggest that regulator-led minority shareholder activism exacerbates managerial myopia to cater to investors and financial constraints, ultimately stifling corporate innovation.

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 study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

This study explores the role of sentiment in predicting future stock return volatility in the Chinese A-share market. Specifically, we conduct a composite sentiment index capturing both investor and manager sentiment. The former is measured by overnight returns, and the latter is measured by a textual tone based on the information in the Management Discussion and Analysis section of the annual reports. Empirically, we find that the composite index is positively associated with subsequent stock realized volatility and the result remains robust after controlling for a set of firm characteristics and state ownership. Besides, the result also shows that investor attention can help dissect the sentiment—volatility relation.

We examine the impact of firm-investor communication on ESG issues through investor interactive platforms in Chinese stock exchanges from 2010 to 2022. Our regression analysis finds that increased ESG-based questions from investors and firms’ responses lead to increased stock liquidity, suggesting that investor-firm dialogues beyond financial aspects to include ESG-related themes contribute to greater information transparency. We posit that investors use such communication as a “voice” strategy, advocating firms for enhanced ESG disclosures and performance. This strategy yields a two-fold benefit: it aligns with investors’ ESG objectives and, alternatively, facilitates their exit through improved stock liquidity. Our robustness tests suggest a probable causal relationship between investor engagement on ESG issues and stock liquidity. Moreover, we find that a positive tone in ESG-based communications strengthens this relationship, prompting managers to enhance ESG disclosure transparency in response to investor pressure.

This study examines the effect of retail investor-firm communications (RIFC) on corporate ESG performance. Exploiting the unique setting of Chinese investor interactive platforms which enable retail investors to pose questions and require firm answers, we show that RIFC significantly improves corporate ESG performance. The consistent evidence is obtained by employing the difference-indifference estimation, Oster’s test and alternative indictors, strengthening our confidence in the causal link between RIFC and corporate ESG performance. Furthermore, we identify two potential economic channels underlying our results: strengthening monitoring pressure and alleviating financial constraints. Our finding further reveals that RIFC drives genuine improvements in ESG performance rather than greenwashing practices. Collectively, this study advances our understanding of the interplay between retail investors and corporate ESG performance, providing a stepping stone toward effective solutions to corporate sustainable development.

Volatility in the financial markets is commonplace and it comes with a cost. One of these costs is abrupt and huge drop in stock price that is known as stock price crash. To model this, we propose a new machine-learning based stock crash risk measure using minimum covariance determinant (MCD) to detect stock price crash. Using this proposed dependent variable, we try to predict stock price crash using cross-sectional regression. The findings confirm that the method properly capture the stock price crash and our proposed model performs well in terms of statistical significance and financial impact. Moreover, using newly introduced firm-specific investor sentiment index, it is identified that stock price crash and firm-specific investor sentiment are positively correlated. That is, higher sentiment leads to an increase with stock price crash risk, a relation that remains robust even when different firm sizes and detoned firm-specific investor sentiment index are considered.

This article aims to provide an in-depth and comprehensive analysis of PRC Securities Law 2020 which overhauls China’s securities regulatory framework to construct more efficient and transparent capital markets with enhanced investor protection and market integrity. The law constrains regulators’ administrative powers in deciding the outcome of IPOs as well as streamline the securities offering procedure. This article pays attention to key reform initiatives proposed by PRC Securities Law 2020, such as the registration-based IPO system, the enhanced investor protection and compensation regime, the cross-border supervision, and the harsher punishments for securities frauds. It also discusses the latest enforcement cases relating to high-profile financial frauds like the Luckin Coffee scandal which resulted in Luckin Coffee being delisted from NASDAQ in 2020. The analysis in the article is accompanied by relevant US securities law in the same area to offer a comparative angle, which is of interest to practitioners, academics and policymakers in major financial centres.

We explore whether Management Discussion and Analysis (MD&A) can provide incremental information to peers. Using Chinese stock market data, we find that positive peers' MD&A tone encourages firms to hold more cash, particularly for industries with fewer institutional investors' site visits. Moreover, this association is moderated by predation risk and decision-making environment. Specifically, this effect is more pronounced for firms which are market followers or financial constrained, and it is also stronger for firms operating under higher economic policy uncertainty or solely in domestic market. Overall, our findings enrich the information channels of peer effects in cash policy.

Using daily news tone data between 2017 and 2020, we examine whether news tones can predict stock returns in Chinese A-share market. We first document that the news tones significantly and positively predict the cross-sectional stock returns over next day and over the next 12-weeks. When we separate the news into online news and paper news, the online news exhibit strong predictive power for future returns, while the printed news only displays marginal predictive power. We hypothesize that the online news is more related to firm fundamentals, while the paper news is more linked to political aspects of firm information. Our results using earnings surprises and SOE subsamples provide supportive evidence for the hypothesis.