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.

This paper examines how profits from mutual funds’ participation in initial public offerings (IPOs) shape fund performance, investor flows, and market stability in China. Using comprehensive fund–IPO matched data from 2016 to 2023, we decompose fund returns into an IPO-lottery component and residual performance. At the aggregate level, IPO allocations add 2.05% to annualized excess returns; net of IPOs, excess return is −0.35% per year. At the individual level, the contribution of IPO profits varies substantially across funds and is most pronounced among mid-sized funds, inflating perceived managerial skill. Funds with higher IPO-driven gains attract greater inflows despite the absence of performance persistence, leading to capital misallocation. At the market level, IPO-profit-induced trading (PIT) predicts short horizon price run-ups that dissipate and reverse over subsequent months, while raising both total and idiosyncratic volatility. Overall, IPO profits temporarily enhance reported performance but erode market stability by propagating non-fundamental shocks through secondary markets.

Facing external shocks, maintaining and stabilizing imports is a major practical issue for many developing countries. We first document that sourcing market switching (SMS) is widespread for Chinese firms (For 2000-2016, SMS firms account for 76.29% of all import firms and 96.30% of total import value). Then we use Chinese firm-level data to show that SMS can significantly mitigate the negative impacts of international uncertainty on imports, which further stabilizes firm employment and innovation, leading to increases in national and even world welfare. Possible motivations for SMS include stabilizing import supply, lowering import tariffs, raising the real exchange rate, and increasing product switching. We also find that the effects of SMS vary by the type of uncertainty, firm ownership, productivity, credit constraints, trade mode, and product features.

Using firm-customer matched data from 2005 to 2020 in China, we examined the spillover effects and mechanisms of green innovation (GI) among supply chain partners. Results show a positive association between customers' GI and their supply firms' GI, indicating spillover effects in the supply chain. Customers' GI increase from the 25th to the 75th percentile leads to a significant 19% increase in supply firms' GI. Certain conditions amplify the spillover effect, including customers with higher bargaining power, operating in less competitive industries, and supply firms making relationship-specific investments or experiencing greater customer stability. Geographic proximity and shared ownership further enhance the spillover effect. Information-based and competition-based channels drive the spillover effect, while customers with higher GI encourage genuine GI activities by supply firms. External environmental regulations, such as the Chinese Green Credit Policy and Environmental Protection Law, strengthen the spillover effect, supporting the Porter hypothesis. This research expands understanding of spillover effects in the supply chain and contributes to the literature on GI determinants.

Using bank-county-year level mortgage application data, we document that minority borrowers are systematically evaluated with less soft information compared to White borrowers within the same bank-county branch. Using variation in local sunshine as an instrument and conducting a series of robustness checks, we show that the soft information imbalance significantly increases the denial gap between minority and White applicants. However, this imbalance does not appear to affect pricing disparities. Further analysis shows that internal capital reallocation to under-resourced bank branches can serve as an effective strategy to reduce soft information imbalances and, thus, promote more equitable credit allocation. Our results highlight that soft information imbalance is an overlooked but significant factor driving disparities against minority borrowers.

Developing an early warning model for company financial crises holds critical significance in robust risk management and ensuring the enduring stability of the capital market. Although the existing research has achieved rich results, the disadvantages of insufficient text information mining and poor model performance still exist. To alleviate the problem of insufficient text information mining, we collect related financial and annual report data from 820 listed companies in mainland China from 2018 to 2023 by using sophisticated web crawlers and advanced text sentiment analysis technologies and using missing value interpolation, standardization, and data balancing to build multi-source datasets of companies. Ranking the feature importance of multi-source data promotes understanding the formation of financial crises for companies. In the meantime, a novel Attention-based Fuzzy Neural Network (AFNN) was proposed to parse multi-source data to forecast financial crises among listed companies. Experimental results indicate that AFNN exhibits significantly improved performance compared to other advanced methods.

Corporate misconduct poses significant risks to financial markets, undermining investor confidence and economic stability. This study investigates the influence of World Heritage culture, with its social, historical, and symbolic values, on reducing corporate misconduct. Using firm-level data from China, with its rich cultural heritage and ancient civilization, we find a significant negative association between the number of World Heritage sites near a company and corporate misconduct. This suggests that a richer World Heritage culture fosters an informal institutional environment that mitigates corporate misconduct. This effect is robust across 100 km, 200 km, and 300 km thresholds and remains significant when using a binary misconduct indicator. The results also show that World Heritage culture enhances corporate social responsibility (CSR) and social capital, which in turn reduces corporate misconduct. Additionally, the impact of World Heritage culture is more pronounced in firms located in high social trust areas, those with high institutional investor supervision, and those farther from regulatory authorities. These findings advance academic knowledge and offer practical implications for policymakers and investors.

Corporate site visits provide investors with opportunities to obtain non-standard, tailored "soft" information about the firm. In this study, we investigate the impact of information disclosed from corporate site visits on stock market stability from the perspective of stock return volatility. Our findings suggest that it is the information disclosed rather than the visits themselves that significantly reduce stock return volatility, primarily by mitigating information asymmetry. Moreover, we observe that the volatility-mitigating effect of site visits is more pronounced when the visit information better aligns with investors' concerns and when it is more effectively disseminated. Our study contributes to the literature by demonstrating that the timely disclosure of site visit details serves as a stabilizing mechanism for stock prices through effective information mining and dissemination.

Compared to previous literature on external FinTech, this paper is more interested in the role played by bank FinTech. Based on panel data from Chinese commercial banks spanning 2010 to 2021, this paper investigates the impact of digital transformation on bank soundness and its potential mechanisms. The empirical findings demonstrate a positive association between digital transformation and bank soundness, driven primarily by strategic and management digitization. Mechanistic analysis indicates that digital transformation improves bank soundness by mitigating risk-taking behavior and promoting diversification. The positive effect of digital transformation is more pronounced in state-owned and joint-stock banks, banks with higher liquidity mismatch as well as in sub-samples with greater levels in external FinTech development and economic policies uncertainty. Additional analysis suggests that digital transformation can still enhance bank soundness even in the presence of relatively easy monetary and macroprudential policies, highlighting the harmonization and complementarity between internal innovation from digital transformation and external regulatory policies in maintaining banking stability. Overall, this paper contributes to the literature on bank FinTech, factors influencing bank stability. And it also provides a novel explanation for the relationship between financial innovation and financial stability.

With China being a large developing economy, the trading in China’s stock market is dominated by retail investors, and its government actively participates in this market. These features are quite different from those of typical developed markets, and This review focuses on two important questions: how do retail and institutional investors trade in China and why? We have three main findings after reviewing 100+ previous studies. First, small retail investors have low financial literacy, exhibit behavioral biases, and not surprisingly, negatively predict future returns; whereas large retail investors and institutions are capable of process information, and they positively predict future returns. Second, the macro- and firm-level information environment in China is slowly but gradually improving. Finally, the Chinese government actively adjusts their regulations of the stock market to serve the dual goals of growth and stability, with many of them being effective, while some may not generate intended consequences.