Does large-scale privatization increase income inequality? To answer this question, we analyze the impact of China’s reform of state-owned enterprises on labor market outcomes in urban areas from 1992 to 2004, exploiting cross-prefecture variation in reform exposure stemming from initial differences in the employment shares of urban collective enterprises and state-owned enterprises. Our analysis reveals that workers in prefectures with higher exposure to the reform experienced a more rapid decline in employment and a slower increase in income, compared to those in less exposed areas. Further analysis shows that individuals with lower income and those with lower educational attainment experienced greater losses. A back-of-the-envelope analysis indicates that the reform contributed to more than 40% of the study period’s increase in income inequality.

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.

Portfolio underdiversification is one of the most costly losses accumulated over a household’s life cycle. We provide new evidence on the impact of financial inclusion services on households’ portfolio choice and investment efficiency using 2015, 2017, and 2019 survey data for Chinese households. We hypothesize that higher financial inclusion penetration encourages households to participate in the financial market, leading to better portfolio diversification and investment efficiency. The results of the baseline model are consistent with our proposed hypothesis that higher accessibility to financial inclusion encourages households to invest in risky assets and increases investment efficiency. We further estimate a dynamic double machine learning model to quantitatively investigate the non-linear causal effects and track the dynamic change of those effects over time. We observe that the marginal effect increases over time, and those effects are more pronounced among low-asset, less-educated households and those located in non-rural areas, except for investment efficiency for high-asset households.

This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

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.

This study examines how classified boards affect workplace safety, an important dimension of employee welfare. Using comprehensive establishment-level injury data from the U.S. Occupational Safety and Health Administration and a novel classified board database, we document that firms with classified boards experience 12-13% lower workplace injury rates. To establish causality, we employ instrumental variable and difference-in-differences approaches exploiting staggered board declassifications. The safety benefits of classified boards operate through increased safety expenditures, reduced employee workloads, and enhanced external monitoring through analyst coverage. These effects are strongest in financially constrained firms and those with weaker monitoring mechanisms. Our findings support the bonding hypothesis that anti-takeover provisions facilitate long-term value creation by protecting stakeholder relationships and provide novel evidence that classified boards benefit rank-and-file employees, not just executives and major customers. The results reveal an important mechanism through which governance structures impact employee welfare and challenge the conventional view that classified boards primarily serve managerial entrenchment.

We develop an information choice model where information costs are sticky and co-move with firm-level intrinsic uncertainty as opposed to temporal variations in uncertainty. Incorporating analysts' forecasts, we predict a negative relationship between information costs and information acquisition, as proxied by the predictability of analysts' forecast biases. Finally, the model shows a contrasting pattern between information acquisition and intrinsic and temporal uncertainty, where intrinsic uncertainty strengthens return predictability of analysts' biases through the information cost channel, while temporal uncertainty weakens it through the information benefit channel. We empirically confirm these opposing relationships that existing theories struggle to explain.

This study examines the relationship between industry litigation risk and corporate misconduct using China's A-share listed companies’ data from 2007 to 2022. The findings indicate a significant and negative association, where companies in industries with higher median litigation amounts relative to their assets exhibit reduced incidents of misconduct. This suggests that businesses in high-risk litigation sectors may adopt more cautious practices to mitigate legal challenges and protect their reputations. The robustness of these findings is confirmed through a variety of tests, including a quasi-experimental setting of the chief judges rotation implemented in 2008. Furthermore, the study finds that external monitors including financial analysts’ site visits and local law firms moderate the negative relationship between litigation risk and misconduct. We further show that legal enforcement and moral capital are the two channels through which industry litigation risk impacts corporate misconduct. Our findings underscore the role of litigation risk in shaping peer firms' behavior.

This paper investigates how transparency in lawsuit information affects capital allocation and aggregate industrial production. Greater transparency enhances the availability of information about firms' fundamentals, which can influence resource distribution. We exploit regional variations in courts' compliance with mandated judicial document disclosures in China, implemented since 2014, as a natural experiment. For firms with initially high marginal revenue products of capital (MRPK), a 10-percentage-point increase in legal transparency results in a 4.4% increase in physical capital and a 7.9% reduction in MRPK, relative to firms with lower MRPK. Additionally, regions with higher transparency experience a rise in aggregate output. Further analysis differentiating firms by ownership type, public listing status, and industry-level contract intensity enhances the robustness of our findings.

In the face of severe climate change, researchers have looked for assistance from financial instruments. They have examined how to hedge the risks of these instruments created by market fluctuations through various green financial derivatives, including green bonds (i.e., fixed-income financial instruments designed to support an environmental goal). In this study, we designed a green bond index option contract. First, we combined an autoregressive moving-average model (AMRA) with a generalized autoregressive conditional heteroskedasticity model (GARCH) to predict the green bond index. Next, we established a fractional Brownian motion option pricing model with temporally variable volatility. We used this approach to predict the closing price of the China Bond–Green Bond Index from 3 January 2017 to 30 December 2021 as an empirical analysis. The trend of the index predicted by the ARMA–GARCH model was consistent with the actual trend and predictions of actual prices were highly accurate. The modified fractional Brownian motion option pricing model improved the pricing accuracy. Our results provide a policy reference for the development of a green financial derivatives market, and can accelerate the transformation of markets towards a more sustainable economic development model.