Background Economic stability depends on the ability to foresee financial risk, particularly in markets that are extremely volatile. Unbalanced financial data is difficult for traditional Support Vector Machine (SVM) models to handle, which results in subpar crisis detection capabilities. In order to improve financial risk early warning models, this study combines Gaussian SVM with stochastic gradient descent (SGD) optimisation (SGD-GSVM). Methods The suggested model was developed and assessed using a dataset from China's financial market that included more than 2,000 trading days (January 2022–February 2024). Missing value management, Min-Max scaling for normalising numerical characteristics, and ADASYN oversampling for class imbalance were all part of the data pretreatment process. Key evaluation metrics, such as accuracy, recall, F1-score, G-Mean, AUC-PR, and training time, were used to train and evaluate the SGD-GSVM model to Standard GSVM, SMOTE-SVM, CS-SVM, and Random Forest. Results Standard GSVM (76% accuracy, 1,200s training time) and CS-SVM (81% accuracy, 1,300s training time) were greatly outperformed by the suggested SGD-GSVM model, which obtained the greatest accuracy of 92% with a training time of just 180 seconds. Additionally, it showed excellent recall (90%) and precision (82%), making it the most effective and efficient model for predicting financial risk. Conclusion This work offers a new method for early warning of financial risk by combining SGD optimisation with Gaussian SVM and employing adaptive oversampling for data balancing. The findings show that SGD-GSVM is the best model because it strikes a balance between high accuracy and computational economy. Financial organisations can create real-time risk management plans with the help of the suggested technique. For additional performance improvements, hybrid deep learning approaches might be investigated in future studies.

Shill bidding, the use of non-genuine bids to inflate prices, undermines auction market integrity. Exploiting China’s online judicial housing auctions as a laboratory, we identify 2% of participants as suspected shill bidders, affecting 8% of auctions. They raise price premium by 14.3%, causing an annual deadweight loss of ¥570 million for homebuyers. Mechanism analysis reveals they create bidding momentum and intensify competition. We establish causality using a difference-in-differences analysis leveraging a 2017 regulatory intervention and an instrumental variable approach using dishonest judgment debtors. These findings offer actionable insights for policymakers and auction platforms to combat fraud in online high-stake auctions.

This study examines how housing purchase restrictions (HPRs) affect firms' robotics adoption through labor cost increases. Exploiting policy-driven housing price shocks across Chinese cities, we find firms significantly accelerate robot adoption in response to higher labor costs. Effects are pronounced among financially unconstrained firms, state-owned enterprises, and firms with skilled or educated workforces. Automation investments subsequently improve firm productivity, profitability, and market positions. Our findings highlight unintended spillovers from housing regulations to firm-level technological decisions and suggest policymakers consider these indirect effects when designing local market interventions.

We examine the risk-return trade-off in market anomalies within the A-share market, showing that even decaying anomalies may proxy for latent risk factors. To balance forecast bias and variance, we integrate the 1/N and mean-variance frameworks, minimizing out-of-sample forecast error. Treating anomalies as tradable assets, we construct optimized long-short portfolios with strong performance: an average annualized Sharpe ratio of 1.56 and a certainty-equivalent return of 29.4% for a meanvariance investor. These premiums persist post-publication and are largely driven by liquidity risk exposures. Our results remain robust to market frictions, including shortsale constraints and transaction costs. We conclude that even decaying market anomalies may reflect priced risk premia rather than mere mispricing. This research provides practical guidance for academics and investors in return predictability and asset allocation, especially in the unique context of the Chinese A-share market.

This research examines the impact of short selling on the financing cost of corporate bonds using panel data from Chinese A-share listed companies spanning the period from 2007 to 2022. The study aims to investigate the potential cross-market information spillover effects within the short selling system. The findings indicate that short selling significantly reduces the financing cost of corporate bonds, with a more pronounced effect observed under greater short selling forces. The robustness of the results is confirmed by controlling for various potential influencing factors and addressing the endogeneity issue through Propensity Score Matched Difference in Differences (PSM-DID) methodology. Moreover, the research reveals that the alleviation of information asymmetry serves as the primary mechanism through which short selling exerts its impact, particularly in regions with well-developed financial markets and favorable legal environments. This study offersa novel perspective of short selling in China and it sheds light on its cross-market spillover effects. By effectively enhancing resource allocation efficiency in capital markets, short selling emerges as a potent tool for mitigating information disparities between bond investors and enterprises.

We collect the geographical dataset of bank physical branch in China from 2008 to 2023, obtaining the 261,382 branches. Through careful data processing, we calculate the bank branch closure at city-level and merge it with regional entrepreneurship in China. With the panel dataset at city-industry-year level, we find that bank branch closure (BBC) significantly reduces neighbor entrepreneurship, which is proxied by the number of new firm entry. In mechanism analysis, we document that bank branch closure affects entrepreneurship through the financing channel and mobility channel. We also find that commercial bank branch closure plays a crucial role in affecting entrepreneurship. The reduction effect of BBC is more pronounced for those observations located in geographical intersections, coastal lines. Further, we explore the impact of BBC on the direction of entrepreneurship, showing that there is less new firm formation in manufacture industry after the BBC. In addition, we show that BBC may contribute to the entrepreneurship failure as well. Our findings may shed light on the policy makers, bank owners and those who want to form a new firm.

In this study, we investigate whether and how risk disclosure in peer firms’ management discussion and analysis (MD&A) influences analyst earnings forecast accuracy. We find that peer MD&A risk disclosure significantly improves forecast accuracy, demonstrating a positive spillover effect. Moreover, the impact of peer MD&A risk disclosure on analysts’ forecast accuracy strengthens with the comparability and reliability of peer firms’ information, while weakens with the disclosure quality of the focal firm. Finally, peer MD&A risk disclosure also reduces stock price crash risk, providing further evidence that it improves information environment of the focal firm.

Previous studies on the impact of corporate information disclosure on stock price crash risk have largely focused on financial statements. In contrast, China’s unique monthly operating report disclosure system—featuring high frequency and realtime operational data—offers a distinct information channel. Using data from A-share listed firms from 2010 to 2021, we find that monthly operating report disclosures significantly reduce stock price crash risk by alleviating information asymmetry between firms and external stakeholders. The underlying mechanisms involve restraining managerial opportunism and correcting investor expectation biases. Further analysis shows that firms’ official responses to investor inquiries has no significant effect on crash risk once monthly operational disclosures are accounted for, underscoring that the quality of information disclosed is as important as its frequency. The risk-reducing effect is more pronounced among firms with greater business complexity, weaker internal controls, and lower institutional ownership.

This paper investigates how the geographical distribution of shareholders in Chinese mutual fund management companies influences investment decisions. We show that mutual funds are more inclined to hold and overweight stocks from regions where their shareholders are located, thus capitalizing on a local information advantage. By examining changes in fund holdings in response to shifts in the shareholder base, we rule out the possibility that these effects are driven by fund managers’ local biases. Our findings reveal that stocks from the same region as the fund’s shareholders tend to outperform and significantly contribute to the fund’s overall performance.

The U.S. Treasury yields reached a 20-year high under acute inflation pressure in the post-pandemic era amid aggravated geopolitical conflicts. To quantify the underlying effects of regional geopolitical risks (GPRs) of key U.S. strategic interests, we employ an extended affine term structure model with unspanned GPRs and conventional macroeconomic drivers. We find that GPR shocks, particularly those manifesting U.S.-China rivalry, contribute more to expectations and variations of inflation and yields than shocks to U.S. macroeconomic variables. The results warn on the adequacy of monetary policy in curbing inflation in a fragmented global order with escalating GPRs.