Financial Distress Prediction for Digital Economy Firms: Based on PCA-Logistic

Abstract

Financial distress prediction is important for risk prevention and control of digital economy firms, as well as going concern guarantee. This paper takes 100 Chinese A-share listed digital economy firms from 2017 and 2021 as samples, obtains financial indicators by combining the characteristics of digital economy firms, the first three periods of financial distress are systematically modeled employs Logistic regression, while we use the Principal Component Analysis method to deal with the problem of multicollinearity. The results show that the profitability factor has the greatest contribution to the predictive role; the closer to the year in which the financial distress occurred, the higher the prediction accuracy rate. Finally, this model achieves 86% prediction accuracy. The successful modelling provides a basis for information users to determine the financial distress of firms accurately and prospectively in the digital economy.