This study investigates the mean-reversion behavior of trading volume, using China’s A-share market as a representative setting characterized by dispersed retail investors, frequent public disclosures, and active policy interventions. We compare two competing interpretations:the stealth-trading hypothesis, in which persistent volume reflects order-splitting by informed investors, and the informational efficiency hypothesis, which links faster volume reversion to more effective information processing. Using the Ornstein–Uhlenbeck (OU) model, we estimate reversion speeds for over 3,000 stocks and relate these to firm- and industry-level characteristics. We find that trading volume is broadly mean-reverting, with over 98% of stocks exhibiting stationarity. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger firm size, greater analyst coverage, lower volatility, and higher liquidity. Notably, reversion speed increased after accounting reforms but declined following capital access liberalization, suggesting that regulatory policy can both enhance and impair informational efficiency. These findings position reversion speed as an observable proxy for market responsiveness and highlight trading volume as a central variable in empirical market microstructure research.

货币结构研究长期存在一个核心困惑：1999-2019 年M1/QM存量比始终稳定在 1:2，2020 年后却出现持续性失锚。现有研究大多聚焦M1/M2增速剪刀差的短期周期特征，普遍忽视存量资金收支的底层约束，同时资本收益率与融资成本的核算口径混乱，导致不同研究结论缺乏可比性。本文构建居民-商业银行-实体企业三部门时变参数 DSGE 模型，采用白重恩（2006）国民核算法范式，采用MPK与全实体WACC对偶口径，提炼得到M1,tre,t=QMtrd,t这一收益等价核心恒等式。依托1999Q1-2026Q1跨国时序、全国年度、中国31省份季度面板三层数据，综合多套识别策略，分国别测算资本收益倍数Kt的破裂临界值：中国 1.801、日本 1.785、德国1.922，混合样本临界值1.803可作为类似制度特征工业化经济体的参考基准。当Kt跌破临界阈值时，存量货币持续从经营性活期向定期沉淀。纠正“单纯依靠放宽银行信贷供给就能改善货币结构、降息万能、紧盯历史比值调控”的政策误区。量化测算结果显示，累计新增 11.23 万亿元实体利润可修复收益闭环，经济体将自发形成适配现行制度的全新货币均衡比例。

货币结构研究长期存在一个核心困惑：1999-2019 年M1/QM存量比始终稳定在 1:2，2020 年后却出现持续性失锚。现有研究大多聚焦M1/M2增速剪刀差的短期周期特征，普遍忽视存量资金收支的底层约束，同时资本收益率与融资成本的核算口径混乱，导致不同研究结论缺乏可比性。本文构建居民-商业银行-实体企业三部门时变参数 DSGE 模型，采用白重恩（2006）国民核算法范式，采用MPK与全实体WACC对偶口径，提炼得到M1,tre,t=QMtrd,t这一收益等价核心恒等式。依托1999Q1-2026Q1跨国时序、全国年度、中国31省份季度面板三层数据，综合多套识别策略，分国别测算资本收益倍数Kt的破裂临界值：中国 1.801、日本 1.785、德国1.922，混合样本临界值1.803可作为类似制度特征工业化经济体的参考基准。当Kt跌破临界阈值时，存量货币持续从经营性活期向定期沉淀。纠正“单纯依靠放宽银行信贷供给就能改善货币结构、降息万能、紧盯历史比值调控”的政策误区。量化测算结果显示，累计新增 11.23 万亿元实体利润可修复收益闭环，经济体将自发形成适配现行制度的全新货币均衡比例。

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.

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.

We study the role of superstition in China’s peer-to-peer lending market by ex-amining whether lenders time their bids according to “lucky hours” from the Chinese farmer’s calendar. Loans funded during lucky hours perform better—but only because the platform lists higher-rated loans at those times. This pattern is consistent with a screening mechanism: highly risk-averse lenders place greater value on both true risk reductions and auspicious-day signals, so the platform maximizes surplus by bundling the two—listing low-risk loans on auspicious days. Moreover, listing safer loans at lucky hours can further boost proffts because biased beliefs decay more slowly under asymmetric (bad-news-heavy) learning.

This paper examines how artificial intelligence (AI) adoption influences the total factor productivity (TFP) of Chinese A-share manufacturing firms from 2010 to 2023. Results show that AI significantly raises TFP, robust across multiple specifications and instrumental variable tests. AI also boosts operational efficiency by accelerating accounts receivable and inventory turnover, revealing a “technology–operation–productivity” pathway. The positive effect is stronger in regions with better digital infrastructure and in firms with stronger governance. The findings provide fresh evidence on AI’s productivity effects and offer policy implications for intelligent transformation and high-quality manufacturing development.

The northwest region of China faces many practical challenges, and its digital economy lags behind other areas of China. Digital transformation is a new source of competitive advantage in the digital economy era, which can help northwest enterprises rebuild their competitive advantage in the digital age, accelerate the development of the digital economy in the northwest region, bridge the digital gap between the East and the West, and promote the high-quality development of the national digital economy. In this study, the PLS-SEM method is used to collect data from 172 enterprises across five provinces in northwest China, to deeply analyze the mechanism and path through which digital transformation reshapes enterprise competitive advantage, identify the key sticking point hindering digital transformation in northwest China, and then propose more targeted strategic suggestions. It is found that the resource base of enterprises in northwest China is generally weak, making it difficult to deliver direct competitive advantage; existing enterprise resources can provide basic conditions for digital transformation and resource-orchestration capability; although digital transformation cannot directly create competitive performance, it can indirectly deliver competitive advantage by positively affecting resource-orchestration capability; resource-orchestration capability directly and significantly affects enterprise competitive performance and is the core competency for enterprises to build digital resilience.