This study examines whether the revolving-door phenomenon at financial intermediaries, specifically institutions sponsoring initial public offerings (IPOs), affects fraud allegations against their IPO clients in the Chinese market. We find that when sponsor institutions employ former China Securities Regulatory Commission (CSRC) regulators as board members or top managers, the CSRC is less likely to allege fraud against their clients. Evidence shows that this effect is caused more by a reduced probability of fraud detection than by a lower propensity to commit fraud, and that former regulators' CSRC connections are more likely to drive this effect than their regulation expertise. Additional analyses show that former regulators also help sanctioned clients obtain lighter sanctions. Sponsor institutions benefit from employing former CSRC regulators by charging higher service fees, whereas the former regulators benefit from greater compensation. Finally, we find that while institutional investors see through the revolving-door phenomenon, retail investors do not.

This study utilizes the broadcast of the popular television series Game of Hunting in mainland China as a quasi-natural experiment to examine how investor attention impacts stock price volatility. The empirical findings reveal that the broadcast of Game of Hunting increased stock price volatility for headhunting companies. Notably, headhunting companies that attracted lower levels of investor attention before the broadcast exhibited greater stock price volatility. Conversely, headhunting companies with more complex business operations, which posed greater challenges for investors in processing information, experienced smaller fluctuations in stock prices. The broadcast of Game of Hunting drew retail investors’ attention to headhunting stocks, prompting them to purchase these stocks. This attention-driven buying then triggered arbitrage behavior by other retail investors, who sold the stocks, ultimately causing fluctuations in the share prices of headhunting companies. In contrast, institutional investors’ trading of these stocks was neither affected by the popularity of Game of Hunting nor influenced by the trading behavior of retail investors.

This paper investigates the utilization of live streaming in online communication with retail investors. Using the earnings communication conference in China, we find that firms with superior performance and higher costs associated with processing earnings information are more inclined to employ live streaming during these conferences. Moreover, in line with the social presence theory, robust evidence suggests that live streaming results in more positive retail investor sentiment following the conference. Further analysis shows that live streaming is positively related to investor attention and substitutes post-conference information acquisition with textual information presented during the conference. On the information effect, it enhances market reactions to textual information from managers. This paper sheds light on how multimedia can transform the process of communication with retail investors.

This paper examines how liquidity is priced in the Chinese corporate bond market. Unlike the pervasive liquidity premium across bonds traded in the interbank market, we document a puzzle in the liquidity-yield relation in the exchange market. Before the first significant credit event in March 2014, there was a "liquidity discount" in this market-the more illiquid a corporate bond, the lower its yield spread. This liquidity discount disappears after the first bond default event. The liquidity premium, i.e., a positive relation between illiquidity and yield, emerged when the credit condition for corporate bonds worsened after April 2018. We find that retail investors' yield-chasing behavior in the exchange market is the driving factor behind the liquidity discount effect. The liquidity discount is stronger for bonds favored by retail investors and during the period with a high intensity of yieldchasing behavior. Our empirical analysis further shows that our findings are not due to two alternative hypotheses based on the market microstructure effects of liquidity concentration and nonsynchronous trading.

This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm, I show that an everyday contextual cue—geographic location—triggers the recall of previously traded stocks: when an investor returns to a city she has visited before, her probability of retrading stocks associated with that city rises by 2.1 percentage points under the most saturated specification (up to 12.2 percentage points under less saturated ones). The effect displays the signatures of similarity-based recall: it is diluted by intervening trading experiences, decays with the age of the memory trace, survives when the city is not the investor's home, is not explained by mechanical recency, and is present for orders submitted via both smartphones and PCs. I then examine an extreme-event cue, COVID-19 quarantine, and find a smaller and less robust effect; the interference and decay signatures are absent precisely for these strongly encoded traces, as the memory account predicts. Memory-cued transactions underperform: stocks bought upon the cue earn about 50 basis points less per week than stocks bought for other reasons on the same day. The study provides causal field evidence for memory-based theories, particularly similarity-based recall, in real-world trading.

We investigate whether overnight news sentiment adds predictive value for overnight returns. We focus on the CSI300 Index, whose ETFs are widely held by Chinese retail investors. Sentiment indi-cators are constructed from minute-level overnight news using a fine-tuned RoBERTa model. These indicators are combined with market-based variables to predict overnight returns via regression and machine learning. Results show that while the sentiment alone has predictive value, its incremental contribution disappears once the A50 overnight return is included.

This study investigates the mean-reversion behavior of trading volume, using China’s A-share market as a representative setting characterized by dispersed retail investors, frequent public disclosures, and active policy interventions. We compare two competing interpretations:the stealth-trading hypothesis, in which persistent volume reflects order-splitting by informed investors, and the informational efficiency hypothesis, which links faster volume reversion to more effective information processing. Using the Ornstein–Uhlenbeck (OU) model, we estimate reversion speeds for over 3,000 stocks and relate these to firm- and industry-level characteristics. We find that trading volume is broadly mean-reverting, with over 98% of stocks exhibiting stationarity. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger firm size, greater analyst coverage, lower volatility, and higher liquidity. Notably, reversion speed increased after accounting reforms but declined following capital access liberalization, suggesting that regulatory policy can both enhance and impair informational efficiency. These findings position reversion speed as an observable proxy for market responsiveness and highlight trading volume as a central variable in empirical market microstructure research.

We examine price discovery after irregularly scheduled macroeconomic announce-ments. Exploiting time variation in Chinese macro announcements released outside regular trading hours, this paper isolates the role of elapsed non-trading time in facilitating investor learning and price discovery upon market reopening. We show that longer non-trading intervals generate more efficient post-announcement price discovery, reduce information asymmetry, and diminish subsequent intraday return reversals. The mechanism operates through enhanced retail investor learning: during non-trading hours, retail investors actively acquire information, subsequently trade more aggressively, earn higher profits, and face reduced informational disadvantages at market opening. Our findings highlight that retail investor learning during non-trading hours levels the informational playing field among heterogeneous investors and improves price quality around irregularly timed macroeconomic announcements. These results have broader implications for emerging markets, which similarly feature irregular announcement timing and large populations of uninformed retail investors.

We posit and demonstrate that, in China’s retail-dominated market, quantitative trading over-relies on non-fundamental signals, thereby crowding out fundamental information from stock prices and increasing crash risk. Using trading data from quantitative mutual funds and Chinese A-share firms during 2009-2023, we find that greater exposure to quantitative trading is associated with higher future crash risk. Mediation analysis further reveals that reduced information efficiency constitutes a key channel through which quantitative trading elevates crash risk. The effect is stronger for stocks with more retail investors, consistent with our proposed mechanism. Overall, we identify a novel potential risk of quantitative trading in underdeveloped emerging markets.

We document a novel "news extrapolation" behavior among investors, which distorts the market reaction to corporate news. Specifically, investors tend to extrapolate the value of past news in the immediate reaction to the newly arrived news. News extrapolation generates a biased price reaction to news, which is completely reversed afterwards. Furthermore, the tendency of news extrapolation is related to the recency, consistency, and value uncertainty of news. Investors extrapolate not only from news of the same category but also from news of different categories. By analyzing the trading behavior and sentiment of different investor groups, we find that retail investors tend to be news extrapolators, while institutional investors trade against the news extrapolators.