(untitled) (Informatica, 2024)
With the global economy in a downward cycle under the influence of the epidemic, companies are facing a crisis in their business and financial conditions, and most companies are more likely to be in financial distress in a poor economic environment. The existence of concept drift problem makes the actual prediction of financial distress prediction poor or can only solve limited types of concept drift. Most existing research on financial distress prediction methods use machine learning methods, such as random forests, but there are limitations in dealing with concept drift problems, such as difficulty in model updating and data imbalance. Therefore, a study proposes a model that combines the minimum absolute shrinkage and selection operator with gradient boosting tree algorithm to solve the problem of dynamic concept drift and accurately predict the financial difficulties of enterprises. The study selected financial datasets from Chinese A-share listed companies from 2019 to 2022, with selection criteria including but not limited to the company’s market value, industry representativeness, and financial information. In order to reduce potential sample bias caused by market structure changes, policy adjustments, and other factors, the study adopts time series and industry stratified sampling methods to ensure the representativeness of the samples. Firstly, conduct a thorough analysis of the two algorithms and apply them to dynamic financial indicator selection in financial samples. Secondly, a comprehensive prediction model is established using the sample similarity index. The experimental results show that the model has high accuracy rates of 92.47% and 92.31% in dynamic environments, with high F values of 85.33% and 85.12%, and G values of 91.78%, 91.65%, and 91.92%, respectively. This prediction model has high accuracy and dynamic stability in solving the concept drift problem in financial distress. The study achieved effective processing of dynamic concept drift for the first time by combining two algorithms and using sample similarity index.