This research examines the effects of commercial insurance on household financial vulnerability using data from the China Household Finance Survey (CHFS). Data were collected from 39875 households in 29 provinces of China. The probit model was used to test the relationship between the study variables. The results show that commercial insurance participation reduces the likelihood of a household's financial vulnerability. Heterogeneity analysis found that commercial insurance participation had a more significant dampening effect on the financial vulnerability of households with low personal expenses, low-income, low human capital, rural areas, and the central and western regions, indicating that commercial insurance has a universal effect. This study offers several policy implications for combating household financial vulnerability. First, improving the commercial insurance protection system in both urban and rural areas could improve households' risk management capacity. Second, establishing tax-rewarding policies to encourage households to participate in commercial insurance. Third, increasing the popularity of commercial insurance, particularly in rural areas, and exploring the rural commercial insurance market.

This paper explores the relationship between CEO age in target firms, earnings management, mergers and acquisitions decision-making, and performance by using a sample of Chinese firms from 2008 to 2017. We found that CEO age is negatively correlated with M&A decision-making and target firms engage in a higher degree accrual-based earnings management (AEM) than non-target firms. In addition, target firms with young CEOs exhibit a greater extent of AEM in the pre-M&A period. We also found that the relationship between CEO age and M&A performance is inverted U-shaped. AEM of pre-M&A is negatively correlated with M&A performance, indicating that M\&A performance is affected by AEM of pre-M&A.

This paper proposes a hybrid deep-learning prediction methodology that integrates rolling variational mode decomposition (VMD) and long short-term memory (LSTM) neural network optimized by tunicate swarm algorithm (TSA) for the short-term prediction of dissolved oxygen (DO) in a river system. We use a rolling VMD method at first to extract the variation characteristics of different frequencies in the previous period for each time’s prediction. The decomposition results, the history data of DO, other water quality parameters, and some climatic parameters are served as features to construct a prediction model based on the LSTM optimized by TSA. The water quality data of the Dongyang River is used to examine the effectiveness of this prediction methodology. The experimental results demonstrate that the model has better effectiveness and robustness compared to other benchmark models in terms of accuracy and correlation, which illustrates the proposed prediction method can be recommended as a promising method for water quality forecasting, especially in some small tributaries.