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 identifies an incentive-compatible mechanism to foster individual environmental engagement. Utilizing a dataset comprising 100,000 randomly selected users of Ant Forest—a prominent personal carbon accounting platform embedded within Alipay, China's leading BigTech super-app—we provide causal evidence that individuals strategically engage in eco-friendly behaviors to enhance their credit limits, particularly when approaching borrowing constraints. These behaviors not only illustrate the green nudging effect of BigTech but also generate value for the platform by leveraging individual green actions as soft information, thereby improving the efficiency of credit allocation. Using a structural model, we estimate an annual green value of 427.52 million US dollars generated by linking personal carbon accounting with BigTech credit. We also show that the incentive-based mechanism surpasses green mandates and subsidies in improving consumer welfare and overall societal welfare. Our findings highlight the role of an incentive-aligned approach, such as integrating personal carbon accounts into credit reporting frameworks, in addressing environmental challenges.

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.

This study reconsiders the timing of the equity risk factors by using the flexible neural networks specified for image recognition to determine the timing weights. The performance of each factor is visualized to be standardized price and volatility charts and `learned' by flexible image recognition methods with timing weights as outputs. The performance of all groups of factors can be significantly improved by using these ``deep learning--based'' timing weights. In addition, visualizing the volatility of factors and using deep learning methods to predict volatility can significantly improve the performance of the volatility-managed portfolio for most categories of factors. Our further investigation reveals that the timing success of our method hinges on its ability in identifying ex ante regime switches such as jumps and crashes of the factors and its predictability on future macroeconomic risk.

We introduce a novel perspective on private information, specifically microstructure-based private information, to unravel how institutional investors predict stock returns. Using tick-by-tick transaction data from the Chinese stock market, we find that in retail-dominated markets, institutional investors positively predict stock returns, consistent with findings from institution-dominated markets. However, in contrast to the traditional view that institutional investors primarily rely on value-based private information, our results indicate that microstructure-based private information contributes almost as much to their predictive power as value-based private information does, with both components jointly accounting for approximately two-thirds of the total predictive power of institutional order flow. This finding reveals that retail investors’ trading activities significantly impact institutional investors, naturally forcing them to balance firm value information with microstructure information, thus profoundly influencing the price discovery process in the stock market.

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.

With the increasing executive-employee pay disparity in recent years, research on executive compensation has grown exponentially. This paper reviews all articles on executive compensation published between 2000 and 2022 in the six accounting journals with the highest impact index in South Korea and China (five journals in China), and evaluates and analyzes the research in both countries. The analysis results are organized as follows: First, the research on executive compensation started earlier in South Korea than in China; second, the focus of the research on executive compensation differs between the two countries; then, the study on the determinants of executive compensation varies between the two countries; forth, the proxies for firm performance are mostly the same in the two countries; and finally, most of the studies in the two countries assert that executive compensation has a positive impact on firm performance. Based on the above research, this paper confirms that the agency theory, which has been widely validated in Western countries, is also valid in Asian countries. In addition, it provides an essential reference for future research on executive compensation in Asian countries.

Financial technology, also known as Fintech, is transforming our daily life and revolutionizing the financial industry. Yet at present, consensus regarding the effect of Fintech on green bonds market is lacking. With novel data from China, this study documents robust evidence showing that Fintech development can significantly boost green bonds issuance. Further analysis suggests that this promotion effect occurs by empowering intermediary institutions and increasing social environmental awareness. Additionally, we investigate the heterogeneous effect and find that the positive relation is more pronounced for bonds without high ratings and in cities connected with High-Speed Railways network. The results call for the attention from policymakers and security managers to take further notice of Fintech utilization in green finance products.

Local financial development is crucial for advancing regional financial supply side structural reform, enabling local governments to leverage financial instruments to effectively mobilize land resources and foster competitive growth. The introduction of numerous financial products linked to land-related rights and interests has resulted in a pronounced transmission and interconnection of fiscal and financial risks across regions. This study examines the impact of local financial development on land financialization in China using panel data from prefecture-level cities and detailed information on land mortgages. The findings indicate that the establishment of city commercial banks (CCBs) contributes to the progress of land financialization by incentivizing local government financing vehicles to participate in land mortgage financing, increasing the transfer of debt risks to the financial sector. Notably, the impact of CCBs on land financialization is more pronounced in regions with urban agglomeration, high GDP manipulation, inadequate local financial regulation, and robust implicit government guarantees. Further analysis reveals that CCB establishment has negative spillover effects on land financialization in neighboring areas, while expansion strategies such as establishing intercity branches, engaging in cross-regional mergers, and relaxing regulations have mitigated the rise of land financialization at the regional level. This study provides policy recommendations that focus on reducing local governments’ reliance on land financing and enhancing the prevention and management of financial risks.

We examine the relation between the CEO’s childhood socioeconomic class and corporate labor policies. We find that CEOs raised in low socioeconomic class families are less likely to invest in employee friendly firm policies measured by several types of labor and employment litigation, including litigation by unions, and occupational safety measures. These results are confirmed by crowdsourced employee firm reviews across several workplace dimensions. Our findings are supported by the studies of within-family transmission of occupational knowledge and formation of occupational norms as well as development of empathy and altruistic behaviors in children.