Listed firms all over the globe are moving their focus from short-term profit maximization to long-term environmental, social, and governance (ESG) objectives. Most business leaders are now cognizant of the growing importance of ESG concerns, which may have an effect on an organization’s financial health as well as its reputation in the marketplace. A recent study found that countries with strong ESG performance may be able to improve their financial performance. Yet, in China, the subject of “how does ESG impact financial performance” has received little attention. In this paper, we examine the link between ESG operations and financial indices in China’s publicly traded firms using dynamic and static panel data analysis. We begin by gathering financial data and preprocessing it using z -score normalization. The consequence of ESG variables on the financial performance of the company under the pandemic was investigated using statistical analytic techniques such as the Pearson correlation test, logistic regression model, and Fisher’s exact test. Due to the study’s dynamic and static data, comprehensive ESG has a considerable influence on corporate value and profitability per share. The impact of ESG variables on the financial performance of the company in the event of a pandemic was analyzed using analytical methods such as Pearson correlation, logistic regression, and Fisher’s exact test. Performance in ESG may boost financial performance, which could impact investors, business administrators, decision-makers, and industry regulations.

P2P lending is an important part of Internet finance, which is popular among users because of its efficiency, low cost, wide range, and ease of operation. The problem of predicting loan defaults is affected by many factors, such as the linear and nonlinear nature of the data itself and time dependence and multiple external factors, which have not been well captured in the previous work. In this paper, we propose a multiattention mechanism to capture the different effects of various time slices and various external factors on the results, introduce ARIMA and LSTM to capture the linear and nonlinear characteristics of the lending data respectively, and establish a Time Series Multiattention Prediction Model (MAT-ALSTM) based on LSTM and ARIMA. This paper uses the Lending Club dataset from the United States to prove that our model is superior to ANN, SVM, LSTM, GRU, and ARIMA models in the prediction effect of MAE, RMSE, and DA.