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.

We leverage machine learning to introduce belief dispersion measures to distinguish different opinion (DO) and information asymmetry (IA). Our measures align with the human-based measure and relate to economic outcomes in a manner consistent with theoretical prediction: DO positively relates to trading volume and negatively linked to bid-ask spread, whereas IA shows the opposite effects. Moreover, IA negatively predicts the cross-section of stock returns, while DO positively predicts returns for underpriced stocks and negatively for overpriced ones. Our findings reconcile conflicting disagree-return relations in the literature and are consistent with Atmaz and Basak (2018)’s model. We also show that the return predictability of DO and IA stems from their unique economic rationales, underscoring that components of disagreement can influence market equilibrium via distinct mechanisms.

We examine how mutual funds form stock market expectations and the implications of these beliefs for asset returns, using a novel text-based measure extracted from Chinese fund reports. Funds extrapolate from recent stock market and fund returns when forming expectations, with more recent returns receiving greater weight. This recency tendency is weaker among more experienced managers. At the aggregate level, consensus expectations positively predict short-term future market returns, both in and out of sample. At the fund level, expectations are positively related to subsequent fund performance in the time series. In the cross-section, however, superior performance arises only when funds accurately forecast market direction and adjust their portfolios accordingly. This effect is stronger for optimistic forecasts and among funds with greater exposure to liquid stocks. Our findings highlight the conditional nature of belief-driven performance, shaped jointly by forecasting skill and the ability to implement views in the presence of execution frictions such as short-selling and liquidity constraints.

This study shows how price limit rules, intended to stabilize markets, inadvertently distort price dynamics by fostering strategic speculation. Through a dynamic rational expectations model, we demonstrate that price limits induce post limit-up price jumps by impeding full information incorporation, enabling speculators to artificially push prices to upper bounds and exploit uninformed traders. The model predicts two distinct patterns: (1) stocks closing at price limits exhibit positive overnight returns followed by long-term reversals, and (2) stocks retreating from upper bounds suffer sharp reversals with partial recovery. Empirical analysis confirms these predictions. A natural experiment from China’s 2020 GEM reform —- which widened the price limit -— further provides causal evidence that relaxed limits mitigate speculative distortions.

This paper provides evidence that some seasoned equity offerings are motivated by public information. We test this channel in the supply chain setting, where supplier managers are more attentive than outside investors to customer news. We find that supplier firms are more likely to issue seasoned equity when their customer firms have negative earnings surprises. The results are mitigated when there is common scrutiny on the customer-supplier firm pairs by outside investors and analysts. Furthermore, long-run stock market performance appears to be worse for firms that issue seasoned equity following the negative earnings surprise of their customer firms.

We study the influence of investor attention on mutual fund investors' fund selection and fund managers' portfolio choice. Using the Google Search Volume Index to measure investor attention on individual stocks, we find fund investors tend to direct more capital to mutual funds holding more high-attention stocks; fund managers tend to perform window-dressing trading to increase the portfolio holdings of high-attention stocks displayed to investors. Our results suggest that funds, particularly those with strong incentives, strategically trade on stock attention to attract investor flows. This strategic trading behaviour is also associated with fund underperformance and leads to larger non-fundamental volatility of holding stocks.

We construct a novel risk-based similarity peer network by applying machine learning techniques to extract a comprehensive set of disclosed risk factors from firms' annual reports. We find that a firm's future returns can be significantly predicted by the past returns of its risk-similar peers, even after excluding firms within the same industry. A long-short portfolio, formed based on the returns of these risk-similar peers, generates an alpha of 84 basis points per month. This return predictability is particularly pronounced for negative-return stocks and those with limited investor attention, suggesting that the effect is driven by slow information diffusion across firms with similar risk exposures. Our findings highlight that the risk factors disclosed in 10-K filings contain valuable information that is often overlooked by investors.

We utilize the Bayesian model scan approach to examine the best performing models in a set of 15 factors discovered in the literature, plus principal components (PCs) of anomalies unexplained by the initial factors in the Chinese A-share market. The Bayesian comparison of approximately eight million models shows that HML, MOM, IA, EG, PEAD, SMB, VMG,PMO, plus the four PCs, PC1, PC6, PC7, PC8 are the best supported specification in terms of marginal likelihoods and posterior model probabilities. We also find that the best model outperforms existing factor models in terms of pricing tests and out-of-sample Sharpe ratio.

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.”

Purpose – This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experimentand investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aimsto provide some policy implications for corporate debt financing and further liberalization of the capital marketin China. Design/methodology/approach – Employing the exogenous event of Shanghai-Shenzhen-Hong Kong StockConnect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturitystructure. To validate the results, this study performed several robustness tests, including the parallel test, theplacebo test, the Heckman two-stage regression and the propensity score matching. Findings – This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on thedebt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit.Channel tests show that capital market liberalization improves firms’ information environment and curbsself-interested management behavior. Originality/value – This research provides empirical evidence for the consequences of capital marketliberalization and enriches the literature on the determinants of corporate debt maturity structure. Further thisstudy makes a reference for regulators and financial institutions to improve corporate financing through thegovernance role of capital market liberalization.