Contest between the international or national enterprises stimulates the formation of innovative or improved products or of well-organized processes. Nevertheless, reliance on carbon-based materials and energy emission sources has been highlighted as a primary problem of the 21st century. The current study examines the influence of carbon disclosure information (CDI), media reporting and financial influence on state-owned enterprises (SOEs) and non-state-owned enterprises (NSOEs) by using Shenzhen and Shanghai’s heavy polluting listed industries’ dataset from 2014 to 2019. By applying different data approaches, the estimated results demonstrate that the CDI level is significantly negative related to SOE compared to NSOE. The estimated results explain that media’s positive reporting offsets the additional benefits to stakeholders. While media’s negative reporting negatively influences a firm’s competitive position, it mitigates the stock price and its social value. Our results suggest that external factors are encouraging for the financial values of stakeholders, along with those of enterprises.

Based on the DSSW model, we analyze the nonlinear impact mechanism of investor sentiment on stock return and volatility by adjusting its hypothesis in Chinese stock market. We examine the relationship between investor sentiment, stock return, and volatility by applying OLS regression and quantile regression. Our empirical results show that the effects of investor sentiment on stock market return are asymmetric. There is “Freedman effect” in Chinese stock market, but only optimistic sentiment has a significant nonlinear impact on stock market returns when the stock market is a balanced market or a bear market. Meanwhile, “create the space effect” does exist in Chinese stock market too. It only exists when the market is in equilibrium, and only pessimistic sentiment has the nonlinear effect on stock market volatility.

The large-scale proliferation of China’s new type of agricultural entities has given rise to a higher demand for funds. Farmers have insufficient effective collateral, which makes it difficult for them to obtain sufficient loans. Chinese financial institutions have developed a biological asset mortgage loan business to cope with this situation. China has not considered biological mortgages but has been using real estate and asset mortgage models with strong realizability. This innovative financial business has achieved positive results since it was attempted, but it also faces many risks. It is very important to comprehensively and accurately consider the risk factors of biological asset mortgage loans. Based on 1249 production and operation data samples of new agricultural entities in Zhejiang, Henan, and Shandong provinces, this study constructs an XGBoost model for empirical analysis and compares it with logical regression, support vector machine, and random forest algorithms to obtain the optimal model and feature importance value. According to the characteristic importance value, a biological asset mortgage loan risk assessment system with 4 primary indicators and 20 secondary indicators is established, which can effectively identify the biological asset mortgage loan risk of new agricultural entities.

This paper captures the RMB exchange rate volatility using the Markov-switching GARCH (MSGARCH) models and traditional single-regime GARCH models. Through the Markov Chain Monte Carlo (MCMC) method, the model parameters are estimated to study the volatility dynamics of the RMB exchange rate. Furthermore, we compare the MSGARCH models to the single-regime GARCH specifications in terms of Value-at-Risk (VaR) prediction accuracy. According to the Deviance information criterion method, the research shows that MSGARCH models outperform the single-regime specifications in capturing the complexity of RMB exchange rate volatility. After the RMB exchange rate reform in 2015, the volatility is more asymmetric and persistent, and the probability of being in the turbulent volatility regime is significantly increased. The continuous escalation of Sino-US trade friction has increased the VaR of RMB exchange rate log-returns. From the evaluation results of the actual over expected exceedance ratio (AE), the conditional coverage (CC) test, and the dynamic quantile (DQ) test, we find strong evidence that two-regime MSGARCH models could forecast VaR more accurately, which provides practical value for China’s foreign exchange management authorities to manage the financial risk.

This paper studies the dependence structure and information spillover effect between the RMB exchange rate and the Chinese stock market based on the R-vine copula model and spillover index model. The results show that due to the occurrence of the trade war, the correlation between the three RMB exchange rate indicators and the two stock market indicators increases in varying degrees. In the intensity of spillover, the information spillover of the stock market to the RMB exchange rate is significantly enhanced, and the information spillover intensity of the RMB Index to the stock market increases, but the information spillover of the US dollar and Hong Kong dollar exchange rates to the stock market is significantly weakened. In the direction of spillover, the spillover of the RMB Index and stock market shows the characteristics of alternating transformation, while the exchange rate of a single currency and the stock market shows a one-way transmission from the stock market to the exchange rate. Additionally, the information spillover between the RMB exchange rate and the stock market is closely related to the degree of market openness. The RMB Index contains more information than the exchange rate of a single currency.

Considering the multitargets of corporate carbon emission reduction and the fairness preference psychology of the company, a government incentive model for corporate carbon emission reduction was constructed. The impact of corporate fairness preferences on government carbon emission reduction incentive strategies is studied. In addition, numerical simulation is used to analyze the impact of changes in correlation coefficients, fairness preference coefficients, and discount rates on the optimal enterprise effort coefficient and the government optimal incentive coefficient. Research shows that the degree of fairness preference of a company has a direct impact on the degree of corporate effort, while the discount rate will only have an impact on the company’s long-term effort. In order to improve corporate carbon emission reduction efforts, the government must not only consider the impact of fairness preference on corporate efforts but also flexibly adjust the incentive coefficient of long-term and short-term tasks based on the discount rate.