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We empirically examine how institutional investors react to adjustments in environmental policies in China. We observe a seemingly counterintuitive phenomenon: when environmental policies intensify, fund managers do not increase their holdings in high ESG-rated firms as might typically be expected; instead, they significantly divest from these firms. This behavior stems from the fact that, under stringent environmental policies, maintaining a high level of ESG investing leads to financial losses and fund outflows, especially in the short term, which impair fund managers’ compensation and raise career concerns. Further, within the context of environmental policy adjustments, our heterogeneity analysis tries to disentangle the true motivations behind institutional investors' ESG adoptions. We demonstrate that both pro-social preferences and financial incentives play pivotal roles, and that fund managers do not tolerate unlimited financial losses when ESG investing underperform. Our findings reveal the economic impact of environmental policies on institutional investors and shed light on the contentious and complex nature of the ESG concepts.

While many studies document cross-stock predictability where returns of some stocks predict returns of other similar stocks, most evidence comes from US markets. Following Chen et al. (2019), we identify peer firms based on historical return similarity and construct a Peer Return Gap (PRG) measure, defined as the difference between a stock’s lagged return and its peers’ returns. Our empirical evidence from Chinese markets shows that past-return-linked peers strongly predict focal firm returns. A long-short portfolio sorted on PRG generates an equal-weighted monthly return of 1.26% (t = 3.81) and a Fama-French five-factor alpha of 1.10% (t = 2.86). These abnormal returns remain unexplained by several alternative factor models.

I conduct an exploratory study about the feasibility of factor timing in the Chinese stock market, covering 24 representative and well-identiffed risk factors in ten categories from the literature. The long-short portfolio of short-term reversal exhibits strong and statistically signiffcant out-of-sample predictability, which is robust across various models and all types of predictors. However, such results are not evident in the prediction of all other factors’ long-short portfolios, as well as all factors’ long-wing and short-wing portfolios. The high exposure to the market beta, together with the unpredictability of the market return, explains these failures to some degree. On the other hand, a simple investment strategy based on predicted returns of the reversal factor’s long-short portfolio obtains a signiffcant return three times higher than the simple buy-and-hold strategy in the sample period, with a signiffcant annualized 20.4% CH-3 alpha.

This paper proposes the concepts of ESG information flows and a predictable framework of ESG flows based on AR process, and studies how ESG information flows are incorporated into and affect a dynamic portfolio with transaction costs. Two methods, called the ESG factor model and the ESG preference model, are considered to embed ESG information flows into a dynamic mean-variance model. The dynamic optimal portfolio can be expressed as a traditional optimal portfolio without ESG information and a dynamic ESG preference portfolio, and the impact of ESG information on optimal trading is explicitly analyzed. The rich numerical results show that ESG information can improve the out-of-sample performance, and ESG preference portfolio has the best out-of-sample performance including the net returns, Sharpe ratio and cumulative return of portfolios, and contribute to reducing risk and transaction costs. Our dynamic trading strategy provides valuable insights for sustainable investment both in theory and practice.

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market. We document pronounced existence of factor MAX anomaly in the United States and other G7 countries.

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market.

This study investigates the impact of investor attention on stock market reactions to ChatGPT using dialogues on the Chinese interactive investor platforms (IIPs). We measure investor attention by the number of investors’ questions toward ChatGPT on the IIPs and categorize the firms’ answers as Investing, Speculative, and Absent. The research reveals positive and statistically significant market reactions surrounding the initial questions that occur before firm responses. Positive abnormal returns are also observed around the initial answer dates, with Investing firms evoking the highest market response, followed by Speculative firms, and Absent firms exhibiting the lowest reactions. Furthermore, positive market reactions persist even as firms modify their ChatGPT involvement statements or face stock exchanges inquiries, suggesting that the stock price upswing may primarily be fueled by ChatGPT-related mania. Our findings imply the potential of ChatGPT fervor: collusion caused by investor attention to ChatGPT and firm’s responses catering to investors.

This study investigates the relationship between ESG (environmental, social, and governance) ratings and labor share at the firm level. Using data from Chinese A-share listed firms from 2011 to 2021, we find a significantly positive relationship between the two. Furthermore, we document that state-owned enterprises do not demonstrate a strong sense of political and social responsibility in their employee recruitment projects, while companies with high ESG ratings in East China could increase their labor share due to less stringent financial constraints. Finally, the employment-creation effect of ESG ratings is one of the important channels for improving labor share. Considering the increasing awareness of ESG concepts and the boom in ESG investing, our findings hold significant relevance for employees, directors, investors, and public policymakers.

Drifting in the direction of earnings surprises for a prolonged period is a decades-puzzling financial anomaly, i.e., the “post-earnings-announcement drift” (PEAD). This paper provided a new simple measure of earnings surprise called ORJ. Based on ORJ, not only is the medium effect of investors’ attention on the relationship between earnings surprises and PEAD analyzed, but a tractable and profitable investing strategy is provided. Through comprehensive empirical analysis of the Chinese stock market, we found that i) both earnings surprises and investor attention can increase the degree of PEAD; ii) “good” (bad) earnings surprises strengthen (weaken) the degree of drift by attracting (decreasing) investor attention; it is asymmetric that the positive effects of “good” earnings surprises are stronger than that of “bad” earnings surprises on PEAD; and iii) the strategy obtains an average 6.78% return per quarter in excess of the market and only longs dozens of stocks . iv) Typical pricing factors such as the Fama-French three factors, illiquidity and company characteristics have little explanatory power for the returns of the strategy. This paper strongly shows the importance of monitoring overnight returns of earnings announcements to digging the unexpected information, reveals one mechanism of earnings surprises on PEAD and demonstrates the potential profitability of PEAD in the Chinese market.