In emerging markets characterized by signiffcant information asymmetry, mitigat-ing firm-level risk is paramount for market stability. While the governance role ofinstitutional investors is known, the impact of their direct, on-the-ground engagementremains underexplored. This study’s objective is to investigate how institutionalinvestor site visits, a crucial hands-on governance mechanism, affect stock returnvolatility. Using a sample of Chinese-listed A-share firms from 2012 to 2022, wefind that frequent site visits significantly reduce firm-level stock return volatility.This risk-reduction effect is more pronounced for firms with greater agency problems,poorer ESG performance, and higher expropriation risk. Our analysis, robust toendogeneity concerns, indicates this effect is driven by improved external oversight.We conclude that direct institutional engagement is a vital channel for reducinginformation asymmetry, enhancing corporate governance, and ultimately promotingmarket stability by lowering investment risk.

We examine price discovery after irregularly scheduled macroeconomic announce-ments. Exploiting time variation in Chinese macro announcements released outside regular trading hours, this paper isolates the role of elapsed non-trading time in facilitating investor learning and price discovery upon market reopening. We show that longer non-trading intervals generate more efficient post-announcement price discovery, reduce information asymmetry, and diminish subsequent intraday return reversals. The mechanism operates through enhanced retail investor learning: during non-trading hours, retail investors actively acquire information, subsequently trade more aggressively, earn higher profits, and face reduced informational disadvantages at market opening. Our findings highlight that retail investor learning during non-trading hours levels the informational playing field among heterogeneous investors and improves price quality around irregularly timed macroeconomic announcements. These results have broader implications for emerging markets, which similarly feature irregular announcement timing and large populations of uninformed retail investors.

This study constructs a three-dimensional space model based on fund investment styles, using a sample of open-end equity and mixed funds from 2005 to 2021 to measure the degree of style convergence. The research explores how style convergence impacts fund managers’ window dressing behavior. The results indicate that, after accounting for the effects of fund performance, style convergence exacerbates window dressing behavior among fund managers. Specifically, this is reflected in fund managers increasing their holdings in winning stocks and selling off losing stocks, which indirectly highlights the intense competition within China’s open-end fund industry. The findings remain robust after a series of endogeneity and robustness tests. Further analysis reveals that style convergence contributes to the risk of client attrition, thereby intensifying the agency problem within the fund industry. The window dressing effect due to style convergence is particularly pronounced in funds managed by individuals with lower educational backgrounds, lower investment skills, smaller family sizes, and lower institutional investor ownership. The paper offers valuable insights into the agency problems arising from investment style convergence and provides guidance for mitigating fund managers' self-interested behavior.

This study investigates how mutual funds impact the stock market by ana-lyzing the relationship between mutual fund investment behaviours (holding and trading) and stock returns and realized volatility in the Chinese market. It is found that stocks widely held or bought by mutual funds can earn higher excess returns, and more importantly, the trading measures out-perform the holding measures, which is evident by the portfolio analysis and Fama-MacBeth regressions. Moreover, the proportional holding, pro-portional trading and shares trading measures positively and significantly predict future realized volatility. Meanwhile, a weak asymmetric effect in the share-trade measure is found.

Contrary to the U.S., Chinese stock markets exhibit negative overnight returns, which further decrease with information asymmetry. We demonstrate that China’s "T+1" trading rule, which prohibits same-day selling, exacerbates adverse selection for uninformed buyers by limiting them to react to post-trade information. Prices are hence initially discounted at opening and recovered by the market close, generating negative overnight returns that are inversely related to information asymmetry risks. Consistent with adverse selection, empirical evidence reveals lower overnight returns during market declines and high-volatility periods, with robust negative associations between overnight returns and information asymmetry proxied by ffrm size, analyst coverage, and earnings announcement proximity. A model is introduced to rationalize our findings. The framework also sheds light on China’s "opening return puzzle", the phenomenon that intraday price rises concentrate predominantly in the initial 30 minutes of trading, by showing how reduced adverse selection enables rapid price recovery during opening session.

Digital mergers and acquisitions (M&As) are increasingly becoming a critical strategic approach for enterprises to advance digital transformation. This study conceptualizes digital M&As as positive shock events for corporate digital transformation. Using a dataset of digital M&As by Chinese listed companies from 2005 to 2024, this study applies the propensity score matching combined with difference-in-differences (PSM-DID) method to empirically examine the impact of digital M&As on the market value of acquiring firms. The results show that digital M&As significantly enhance acquirers’ market value. Mechanism tests reveal that this effect is driven by digital resource empowerment, operating through increased digital factor inputs and strengthened digital innovation capabilities. Heterogeneity analysis further indicates that the market value enhancement effect of digital M&As is predominantly significant in non-digital firms, non-state-owned enterprises, and firms located in eastern China. This study expands the research scope of the micro-level effects of the digital economy and offers useful references for the Chinese government in refining its digital economy strategies, as well as practical guidance for firms in formulating their own digital investment decisions.

Does digital financial inclusion alleviate poverty? This study investigates this question by integrating the Digital Financial Inclusion Index of Peking University with microdata from the China Family Panel Studies (CFPS) to examine how the expansion of digital financial inclusion affects household multidimensional poverty in China. Anchored in Amartya Sen ’ s capability approach and operationalized through the Alkire–Foster (A–F) framework, the study identifies multidimensional poverty across five key dimensions: income, health, education, insurance, and living standards. Probit models are employed to estimate how digital financial inclusion influences both the likelihood and structure of multidimensional poverty, while instrumental variable techniques are used to address potential endogeneity. Beyond the average effects, the study further explores the mechanisms through which digital financial inclusion contributes to poverty alleviation, focusing on three channels—promoting household consumption, increasing financial investment, and enhancing access to credit. The results reveal that digital financial inclusion significantly mitigates multidimensional poverty, particularly by improving income, living standards, and health outcomes, though its effects on education and insurance are limited. These findings underscore the transformative role of digital finance in fostering inclusive growth, suggesting that policies expanding digital financial infrastructure and literacy can amplify its poverty-reducing effects and advance equitable development.

Robo-advisors have been hailed as financial innovations that combine Artificial Intelligence (AI) and low-cost advisory services, with the potential to democratize stock market participation and improve financial inclusion, especially in less developed countries. However, to date their adoption has been slower than expected and existing research that has attempted to understand this puzzle focuses exclusively on existing users of robo-advisors. In this paper, we study the intention to adopt robo-advisors as an antecedent of actual adoption. Using data from a survey of 1,277 Chinese adults, a country with one of the highest saving rates in the world but also very low stock market participation rate, we find that financial literacy and technology acceptance strongly influence the intention to adopt robo-advisors. A one-unit increase in financial literacy (technology acceptance) is associated with a 5.69% (4.74%) increase in the probability of adopting robo-advisors. Importantly, financial confidence partially mediates the literacy-adoption link, highlighting a key psychological mechanism in improving stock market participation rates. Our results shed light on the underlying drivers that facilitate financial inclusion.

We document “foreign bias” in AI financial predictions, reversing the classic home bias. U.S.-based ChatGPT is systematically more optimistic than China-based DeepSeek about Chinese firms—in price predictions and directional forecasts—yet significantly less accurate. Evidence supports an information-availability mechanism: bias is strongest when U.S. media coverage of Chinese firms is limited and attenuates for cross-listed firms. Crucially, injecting Chinese news eliminates the prediction gap. Both models produce similar forecasts for U.S. firms, consistent with broader worldwide coverage. LLMs trained in different information environments can create divergent signals, with implications for investors and policymakers as AI increasingly intermediates global markets.

This study investigates how the development of digital finance influences the geographical distribution of corporate supply chains using data from Chinese A-share listed companies from 2010 to 2023. We examine whether digital finance enables firms to overcome traditional geographical constraints and adopt different supply chain distribution strategies. The analysis identifies two primary mechanisms through which digital finance influences supply chain geography: governance effects, which operate through enhanced risk management and information transparency, and financing effects, which function through alleviated capital constraints and trade credit provision. We further explore heterogeneous impacts across four dimensions: regional economic development, regional digital infrastructure, industry market competition, and enterprise lifecycle stages. By examining the geographical distribution of supply chains as an outcome of digital finance development, this study provides novel evidence on the micro-governance implications of digital finance. Our findings contribute to understanding how digital finance fundamentally changes the geographical constraints that have historically shaped supplier selection decisions and enables firms to develop more flexible supply chain configurations.