As China's bond market expands rapidly, the complexity of financial guarantee networks and their implications for credit risk have become critical issues in both academic research and financial practice. Utilizing micro-level data from China's credit bond market spanning 2014 to 2024, this study constructs a multi-layer network incorporating bonds, guarantors, and issuing firms to empirically examine the impact of guarantor network centrality on bond credit spreads. The results reveal a significant U-shaped relationship: moderate centrality reduces spreads by bolstering market confidence, whereas excessive centrality increases them due to heightened systemic risk. Mechanism analyses identify systemic risk and information asymmetry as key mediating channels through which centrality affects credit risk premiums. Heterogeneity tests indicate that this U-shaped pattern is more pronounced among state-owned guarantors, real estate firms, and high-risk clusters within the network. Furthermore, both cross-layer connectivity within the multi-layer structure and regional financial development levels significantly moderate the centrality-spread relationship. These findings offer a structural perspective on credit risk pricing in emerging markets and provide valuable policy insights for credit rating system design, guarantee regulation, and systemic risk prevention. International investors could also leverage these findings to better assess systemic risk in interconnected financial markets across emerging economies.

Starting from the change in decision-makers’ risk attitude, which suggests “an increase in risk leads to a heightened tendency for risk aversion”, this study explores the nonlinear relationship between idiosyncratic risk and corporate cash holdings. Empirical analysis results indicate that, with the enhancement of decision-makers’ risk-averse degree, the marginal increase in corporate cash holdings presents an upward trend as idiosyncratic risk rises. Associated with the changes in managers’ risk attitude, the nonlinear relationship between idiosyncratic risk and corporate cash holdings becomes insignificant when the firm purchases directors’ liability insurance or is located in regions with better business environments. However, if the executives are older or hold academic titles, the increase in corporate cash holdings with the rise of idiosyncratic risk is more rapid.

We propose a deep reinforcement learning method to improve pairs trading by identifying nonlinear relationships in stock news. Using the CSI 300 index constituents from 2015 to 2022, we integrated cointegration and news co-occurrence analysis in asset pairing and used a threshold-based approach in trading design. Results showed our NEWS-CO-DRL method, fusing deep learning and news co-occurrence, outperformed in return generation and risk control, indicating its potential for the Chinese A-share market.

Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the nonlinear relations between the two variables. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated. This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment. The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation (up to 16-fold) of the nonlinear relationship such predictors have with stock index for China. The representative delay model is used to project for up to 17 weeks for future trends in the stock index. From the findings, the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases.