Why do firms appoint expired independent directors? How do expired independent directors affect corporate governance and thus impact investment decisions? By taking advantage of the sharp increase in expired independent directors’ re-employment in China caused by exogenous regulatory shocks, Rule No. 18 and Regulation 11, this paper adopts a PSM-DID design to test the impact of expired independent directors on CSR performance. We find that firms experience a significant decrease in CSR performance after re-hiring expired independent directors and the effect is stronger for CSR components mostly related to internal governance. The results of robustness tests show that the main results are robust to alternative measures of CSR performance, an extended sample period, alternative control groups, year-by-year PSM method, and a staggered DID model regarding Rule No. 18 as a staggered quasi-natural experiment. We address the endogeneity concern that chance drives our DID results by using exogenous regulatory shock, an instrumental variable (the index of regional guanxi culture), and placebo tests. We also find that the negative relation between the re-employment of expired independent directors and CSR performance is more significant for independent directors who have more relations with CEOs and raise less objection to managers’ decisions, and for firms that rely more on expired independent directors’ monitoring roles (e.g., a lower proportion of independent directors, CEO duality, high growth opportunities, and above-median FCF). The mediating-effect test shows that the re-employment of expired independent directors increases CEOs’ myopia and thus reduces CSR performance. In addition, we exclude the alternative explanation that the negative relation is caused by the protective effect brought by expired independent directors’ political backgrounds. Our study shows that managers may build reciprocal relationships with expired independent directors in the Chinese guanxi culture and gain personal interest.

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.

This paper examines the effects of superstitious psychology on investors’ decision making in the context of Mercury retrograde, a special astronomical phenomenon meaning “everything going wrong”. Using natural experiments in the Chinese stock market, we find a significant decline in stock prices, approximately -3.14% in the vicinity of Mercury retrogrades, with a subsequent reversal following these periods. The Mercury effect is robust after considering seasonality, the calendar effect, and well-known firm-level characteristics. Our mechanism tests are consistent with model-implied conjectures that stocks covered by higher investor attention are more influenced by superstitious psychology in the extensive and intensive channels. A superstitious hedge strategy motivated by our findings can generate an average annualized market-adjusted return of 8.73%.

This study shows that investment decisions drive tail risks (i.e., systemic risk and stand-alone tail risk) of TBTF (Too-Big-to-Fail) banks, while financing decisions determine tail risks of non-TBTF banks. After the Dodd-Frank Act, undercapitalized non-TBTF banks continue to gamble for resurrection, and their stand-alone tail risk become more sensitive to funding availability and net-stable-funding-ratio than TBTF banks. We show that implementing a slimmed-down version of TBTF regulations on non-TBTF banks cannot efficiently contain the stand-alone risk of non-TBTF banks and cannot eliminate TBTF privilege. Moreover, non-TBTF banks together generate larger pressure of contagion on the real economy, and they herd more when making financing decisions after the Act. Our findings highlight the need for enhanced regulations on the liability-side of non-TBTF banks.

This paper examines the effects of superstitious psychology on investors’ decision making in the context of Mercury retrograde, a special astronomical phenomenon meaning “everything going wrong”. Using natural experiments in the Chinese stock market, we find a significant decline in stock prices, approximately -3.14% in the vicinity of Mercury retrogrades, with a subsequent reversal following these periods. The Mercury effect is robust after considering seasonality, the calendar effect, and well-known firm-level characteristics. Our mechanism tests are consistent with model-implied conjectures that stocks covered by higher investor attention are more influenced by superstitious psychology in the extensive and intensive channels. A superstitious hedge strategy motivated by our findings can generate an average annualized market-adjusted return of 8.73%.

This work models the spillover of liquidity and volatility and their joint dynamics in the Chinese stock market. Methodologically, we implement a copula-based vector multiplicative error model for sectors. Utilizing intraday data from 2014 to 2022, our empirical analysis reveals strong interdependence between liquidity and volatility at the sectoral level. Moreover, different sectors dominate the transmission of liquidity and volatility shocks at different times. In normal times, sector volatilities transmit shocks notably (though not always dominantly), while in turbulent times, illiquidity is the key channel through which shocks spread. We also pay special attention to how two catastrophic events impacted the Chinese stock market: the 2015/16 stock market crash and the COVID-19 pandemic. Our ffndings are useful for policymakers monitoring and making policy at the sectoral level, as well as for institutional and private investors making investment decisions.

We examine the impact of political connections on the investment decisions of Chinese mutual funds. We identify a direct link between mutual funds’ political connections and stocks held from the same political network using hand-collected information on the professional backgrounds of Chinese mutual fund managers and fund management company (FMC) shareholders. While mutual funds tend to allocate more investments to stocks based on their political connections, this effect alleviates somewhat after the 2012 anti-corruption campaign. Our findings suggest that anti-corruption campaigns can help to reduce the political effects of government-related agencies on fund holdings and contribute to better market fairness.

Evidence from the U.S. and Europe suggests that current risk assessment tools used by researchers and financial professionals to determine individuals’ risk tolerance and provide suitable portfolio recommendations may be flawed due to “mis”perceptions of risk. Limited research has examined the reliability of these tools as measures of relative risk tolerance for households in emerging economies like China. This study develops a multidimensional index of risk tolerance specifically tailored for Chinese households using a psychometric approach. The effectiveness of this multidimensional index in predicting individuals’ financial decisions is tested and compared to traditional unidimensional measures of risk tolerance commonly used in developed countries. The findings indicate that multidimensional measures are more consistent and significant predictors of Chinese households’ investment decisions. Additionally, the study uncovers evidence that cultural differences, related to market expectations and social networks, which are often overlooked in U.S. and European models, play a crucial role in shaping individuals' risk perceptions and investment choices in China. Robustness checks were conducted to account for potential endogeneity between risk tolerance and investment decisions. The findings provide valuable insights for researchers and financial professionals seeking to develop more accurate risk assessment tools that capture risk attitudes and perceptions in China and other developing countries. By adopting a multidimensional approach that accounts for cultural and psychosocial factors, these improved tools can enhance the precision of risk evaluation and facilitate more appropriate investment recommendations.

Against the backdrop of the United Kingdom's withdrawal from the European Union (BREXIT), this study examines predictability in the stock markets of sixteen European countries, the United States, and the BRICS (Brazil, China, India, Russia, and South Africa) by analyzing how their returns predict the returns of sixteen commodities at different quantile levels. The study builds upon existing literature on predictability and extends it by investigating the impact of the BREXIT crisis on these markets. The findings suggest that investors can hedge their portfolios with various commodities during times of the BREXIT crisis, but caution is advised, and the trend of both equities and commodities should be closely monitored before making investment decisions.

This paper proposes a new forecasting framework for the stock market that combines machine learning algorithms with several technical analyses. The paper considers three different algorithms: the Random Forests (RF), the Gradient-boosted Trees (GBT), and the Deep Neural Networks (DNN), and performs forecasting tasks and statistical arbitrage strategies. The portfolio weight optimization strategy is also proposed to capture the model's return and risk information from output probabilities. The paper then uses the stock data in the Chinese A-share market from January 1, 2011, to December 31, 2020, and observes that all three machine learning models achieve significant returns in the Chinese stock market. The DNN achieves an average daily return of 0.78% before transaction costs, outperforming the 0.58% of the RF and 0.48% of the GBT, far exceeding the general market level. The performance of the weighted portfolio based on the ESG score is also improved in all three machine learning strategies compared to the equally weighted portfolio. These results help bridge the gap between academic research and professional investments and offer practical implications for financial asset pricing modelling and corporate investment decisions.