We examine whether firms with high organization capital (OC) are more likely to purchase Directors’ and Officers’ (D&O) liability insurance, using a panel of Chinese A-share listed companies from 2009 to 2021. We document a robust positive association between OC and the propensity to carry D&O insurance. The effect remains statistically and economically significant after controlling for firm characteristics and employing multiple identification strategies to address endogeneity. We propose two economic channels through which OC affects D&O insurance demand, namely, agency and information asymmetry. Consistent with these mechanisms, we find that the positive OC–D&O relationship is significantly stronger in firms with weaker internal governance and those facing opaquer information environments. Additional cross-sectional analyses show that this effect is concentrated in privately-owned firms and in regions with more developed market institutions, suggesting that external pressures accentuate the value of insuring key decision-makers. Our results are robust to alternative model specifications and remain stable after using propensity score matching, instrumental variable approaches, and the Heckman two-stage model. Overall, the findings highlight OC as a critical internal driver of corporate insurance decisions. Firms with substantial intangible assets strategically obtain D&O coverage to strengthen governance and reduce information frictions, especially in emerging markets like China where formal investor protections are still evolving.

This paper studies missing firm characteristics in the Chinese stock market and their implications for empirical asset pricing. Relative to the U.S. market, missing firm characteristics in China remain underexplored despite substantial differences in data availability and disclosure environments. Using a dataset of 106 firm characteristics from 1992 to 2021, we document a pronounced cliff-shaped pattern in missingness, with missing rates falling sharply after 2000. We then compare expectation-maximization (EM) and mean imputation (MN) in both univariate characteristic-sorted portfolios and machine-learning applications that combine many predictors. Results indicate that, in univariate analysis, the two methods produce very similar return spreads because they assign largely the same stocks to the extreme deciles. In machine-learning applications, however, EM-imputed data generally produce better-performing prediction-sorted portfolios than mean-imputed data. These findings provide new evidence on missing firm characteristics in a major emerging market and highlight the importance of imputation choices in machine-learning asset-pricing applications.

We propose the AI Narrative Gap as a novel firm characteristic—the systematic divergence between a firm’s AI strategic narrative intensity and its subsequent AI capital expenditure commitment—and document its capital market consequences. Using Chinese A-share listed firms from 2015 to 2022, we show that firms with a wider AI Narrative Gap attract significantly more optimistic and less accurate analyst earnings forecasts. These distorted expectations, in turn, predict lower subsequent stock returns, lower industry-adjusted abnormal returns, and weaker future accounting performance. A double-sort portfolio placing firms simultaneously in the highest tercile of the AI Narrative Gap and highest tercile of analyst optimism earns a mean return 22.8 percentage points below that of the lowest tercile on both dimensions (t = −5.10). The return reduction in the AI Narrative Gap coefficient is attenuated but not eliminated after controlling for optimism, consistent with a partial expectation-distortion channel. Collectively, these results establish the AI Narrative Gap as a cross-sectionally informative firm characteristic that captures the credibility of a firm’s AI strategic identity, with systematic implications for analyst expectations and asset prices.

This paper examines whether employees avoid firms that commit environmental, social and governance (ESG) misconduct in China where ESG norms are weak. We find that the number of employees grows slower when firms have more ESG incidents after accounting for performance, risk, corporate governance, and time-invariant firm characteristics. The result is mostly attributable to social incidents and incidents that affect China, better educated knowledge workers, and high tech and non-labor-intensive industries, and is unlikely to be caused by layoffs. Overall, workers with better job fluidity respond to incidents that affect them personally.

We examine ESG pricing in the Chinese stock market. The results show that holding stocks with high ESG scores does not provide investors with higher future excess returns. On the contrary, stocks with low ESG scores perform better. However, this negative ESG premium feature is robust only in small-cap stocks. As size increases, the negative ESG premium fades away and is characterized by a positive premium in larger stock subgroups. We further examine the source of the negative ESG premium in small-cap stocks. The results show that this negative premium can not be explained by firm characteristics, short-term reversal effects, and lottery characteristics of stocks, but is associated with ESG investors. Specifically, the higher the ESG score with more ESG investors in small-cap stocks, the lower the expected excess return of the stock. This result implies that firms may benefit from ESG performance and disclosure, while investors may suffer from ESG strategies. Based on the results, we remind investors that they should be cautious in using ESG indicators to guide their investment decisions.

Using data from 11 major international markets that celebrate six cultural New Year holidays that do not occur on January 1, we find that stock markets tend to outperform in days surrounding a cultural New Year. After controlling for firm characteristics, an average stock earns 2.5% higher abnormal returns across all markets in the month of a cultural New Year relative to other months of the year. Further evidence suggests that positive holiday moods, in conjunction with cash infusions prior to a cultural New Year, produce elevated stock prices, particularly among those stocks most preferred and traded by individual investors.

This paper proposes a conditional factor model embedded with heterogeneous group structure, called grouped Instrumented Principal Component Analysis (Grouped IPCA) model, to study the enhancement of industry classifcations on the pricing power of frm characteristics. We derive an inferential theory on the alternating least square (ALS) estimators of the grouped IPCA model under an unbalanced panel data. Based on this, we use two BIC-type information criteria to determine the number of latent factors. We further examine the group heterogeneity with a bootstrap test statistics. Simulations are conducted to evaluate both our asymptotic theory and test statistics. In the empirical study, we show that the in-sample performance of Grouped IPCA model excels the IPCA model, and fnd a strong evidence on the incremental pricing power of industries.

Based on the expected utility under uncertain probability distribution, we explore whether the ambiguity of individual stocks is priced in China’s A-share market and the mechanism behind the ambiguity premium phenomenon. Theoretically, when the asset price is in a specific price range, investors with ambiguity aversion do not participate in the transaction of the asset. As the ambiguity of assets increases, investors with high ambiguity aversion withdraw from the market, and investors with low ambiguity aversion remain in the market (the limited market participation phenomenon); investors who remain in the market due to lower ambiguity aversion are also willing to accept a low ambiguity premium. Empirically, we use "the volatility of the distributions of daily stock returns within a month" to measure monthly ambiguity; and find that (1) the equal-weighted average returns of the most ambiguous portfolios (top 20%) are significantly lower 1.38% than those of the least ambiguous portfolios (bottom 20%); (2) ambiguity still significantly negatively affects the cross-sectional stock return after controlling for common firm characteristics; (3) the higher the ambiguity, the lower the future trading activity, the empirical results are consistent to the theoretical predictions. Those findings reveal the mechanism of the negative ambiguity premium in the A-share market, provide new ideas for further building a factor pricing model suitable for the A-share market, and provide a fresh perspective for preventing systemic financial risk.

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.

Based on the expected utility under uncertain probability distribution, we explore whether the ambiguity of individual stocks is priced in China’s A-share market and the mechanism behind the ambiguity premium phenomenon. Theoretically, when the asset price is in a specific price range, investors with ambiguity aversion do not participate in the transaction of the asset. As the ambiguity of assets increases, investors with high ambiguity aversion withdraw from the market, and investors with low ambiguity aversion remain in the market (the limited market participation phenomenon); investors who remain in the market due to lower ambiguity aversion are also willing to accept a low ambiguity premium. Empirically, we use "the volatility of the distributions of daily stock returns within a month" to measure monthly ambiguity; and find that (1) the equal-weighted average returns of the most ambiguous portfolios (top 20%) are significantly lower 1.38% than those of the least ambiguous portfolios (bottom 20%); (2) ambiguity still significantly negatively affects the cross-sectional stock return after controlling for common firm characteristics; (3) the higher the ambiguity, the lower the future trading activity, the empirical results are consistent to the theoretical predictions. Those findings reveal the mechanism of the negative ambiguity premium in the A-share market, provide new ideas for further building a factor pricing model suitable for the A-share market, and provide a fresh perspective for preventing systemic financial risk.