Whether responsible investing reduces portfolio risk remains open to discussion. We study the relationship between ESG performance and downside risk at fund level in the Chinese equity mutual fund market. We find that fund ESG performance is positively associated with fund downside risk during the period between July 2018 and March 2021, and that the positive relationship weakens during the COVID-19 pandemic. We propose three channels through which fund ESG performance could affect fund downside risk: (i) the firm channel in which the risk-mitigation effect of portfolio firms’ good ESG practices could be manifested at fund level, (ii) the diversification channel in which the portfolio concentration of high ESG-rated funds could amplify fund downside risk, and (iii) the flow channel in which fund ESG performance may attract greater investor flows that could reduce fund downside risk. We show evidence that the observed time-varying relationship between fund ESG performance and downside risk is driven by the relative force of the three channels.

We study how professional fund managers’ growth expectations affect the actions they take with respect to equity investment and in turn the effects on prices. Using novel data on China’s mutual fund managers’growth expectations, we show that pessimistic managers decrease equity allocations and shift away from more-cyclical stocks. We identify a strong short-run causal effect of growth expectations on stock returns, despite statistically significant delays in price discovery from short-sale constraints. Finally, we find that an earnings-based measure of price informativeness is increasing in fund investment.

Over the last decade, China’s P2P lending industry has been seen as an important credit source but it has recently suffered from a wave of bankruptcies. Using 126,090 P2P loan deals from RenRen Dai, one of the biggest online P2P websites in China, this paper attempts to predict credit default probabilities for P2P lending by implementing machine-learning techniques. More specifically, thisstudy proposes a stacking ensemble machine-learning model to assess credit default risk for P2P lending platforms. A Max-Relevance and Min-Redundancy (MRMR) method is used for feature selection and then irrelevant features are eliminated by using k-means clustering method. Finally, the stacking ensemble model is performed to produce accurate and stable predictions in the feature subset. Experimental results show that stacking ensemble model yields high performance, not only in prediction accuracy but also in precision and recall. In comparison to single classifiers, the stacking ensemble machine-learning model has a minimum error rate and provides more accurate credit default risk prediction. The results also confirm the efficiency of the proposed stacking ensemble model through the area under the ROC curve.

Can bank competition help attenuate the prevalence of zombie firms? Motivated by a stylized model, this paper studies the effect of bank competition on the formation of zombie firms in two stages: the formation of distressed firms and distressed firms obtaining zombie lending. Using China’s 2009 bank entry deregulation as a quasi-natural experiment, the paper finds that bank competition lowers the probability of the formation of distressed firms, while it increases the probability of distressed firms obtaining zombie lending. Overall, bank competition decreases the formation of zombie firms. In addition, the findings show that a higher ex ante proportion of bad loans and higher probability of bad loan recovery will lead to a higher probability of distressed firms receiving zombie lending. Both factors encourage banks to sustain lending to distressed firms to keep them alive and to gamble that those firms may recover in the future.

In this paper, we leverage the bank governance reform in China as a laboratory to explore the impact of the banking governance system on lending activities. Specifically, a well-functioning governance system does not improve the bank’s selection abilities due to the regulation constraints. However, a good governance system enhances the bank’s monitoring abilities. Finally, a well-governance bank needs more independent directors on the board, lower shareholdings of the top 1 shareholder, the government as the top 1 shareholder, and fewer risk management committee meetings. Therefore, this paper sheds light on banking governance and has important policy implications for bank sectors in the transition economy.

Research into asset pricing anomalies in the China A-share market is hampered given the short time series of available returns. Even when average excess returns on candidate factor portfolios are economically sizeable, conventional portfolio sorting methods lack statistical power. We apply an efficient sorting procedure that combines firm characteristics with the covariance matrix. For the China A-share market, we find that the efficient sorting procedure doubles the t-statistics compared to conventional portfolio sorts, leading to nine instead of three significant anomalies over the postreform period from 2008 to 2020. We find significant size, value, low-risk, and returns-based anomalies. While portfolio characteristics differ between sorting methods, we find that efficient sorting portfolios highly correlate with equally weighted portfolios and capture the same underlying anomaly.

This paper examines whether sell-side analysts struggle to cope with macroeconomic uncertainty. We find that analysts issue more accurate earnings forecasts when facing higher economic policy uncertainty, which conflicts with the conclusions in the US. We provide a novel explanation for this finding and exclude the view that forecast accuracy improvement comes from analysts’ efforts to actively collect private information through site visits. Further evidence supports that heuristic cognitive bias and emotional framing effect hold back analysts’ tendency to optimism in China, resulting in higher forecast accuracy. As to why Chinese analysts do not work harder but issue more accurate forecasts, we suggest that it is mainly due to the different market regimes faced by analysts in the two countries. Our study sheds light on how macroeconomic uncertainty affects analysts’ unethical behavior and explains the cognitive processes involved.

This study is the first to examine China’s crude oil options market. Using high-frequency data and three different price discovery measures, we conduct a rigorous analysis and find that after its first 8 months of operation, China’s crude oil options market has already played an important role in price discovery. Factors such as volume, volatility, and speculation can impact its price discovery ability. We also find a unique phenomenon in China’s crude oil derivatives market, namely that speculative activity mainly occurs in the futures market and adds to the price discovery of the futures market rather than to the options

How to shape a firm’s stock price resilience in the increasingly uncertain environment has become an important topic. This paper investigates the effect of important market participantsfinancial analysts-on stock price resilience. Based on data from 3,444 listed firms from China, we find that firms with higher analyst coverage are more resilient during the Covid-19 induced crisis, which is manifested by a lower pandemic-induced decline in stock price, shorter duration of decline period, higher recovery probability, and shorter duration of the recovery period after the shock. This positive relationship is more prominent for small firms but does not depend on ownership type, and the ratio of star analyst coverage. Further channel tests show that analysts could help in attracting attention from media and institutional investors, improving corporate governance, and reducing financial constraints, which in turn enhance the ability of stock prices to absorb pandemic shocks.

Access to state-controlled resources can be a major source of firm-level competitive advantage. However, we know little regarding which firms are most likely positioned to capture the state and access resources beyond what their rule-complying merits command. This is partially due to the challenge in identifying irregular state funding that violates official resource-allocation rules. We study a leading innovation grant program in China, and we leverage unique access to the focal grant agency’s administrative data to trace its grant allocation process. We observe occurrences of rule-violating funding and show that firms vary in capability to influence the agency’s funding decision, depending on geographic proximity, as well as other institutional variables. The observed irregular awards are most likely associated with crony capitalism rather than bureaucratic heroism.