Impact of an improved random forest-based financial management model on the effectiveness of corporate sustainability decisions

Abstract

With the development of the economy, more and more electronic manufacturing enterprises are emerging like mushrooms after rain. These enterprises, while developing, also face financial risks caused by various reasons. In order to provide early warning for financial risks of enterprises, improve the accuracy of identifying financial risks, avoid financial crises, and provide assistance for sustainable development decisions, this paper proposes a financial management model based on modified random forest. In order to improve the generalization ability of financial management models, pruning methods were adopted in the study to avoid overfitting. Synthetic minority oversampling technique is used to optimize the financial management model and reduce the calculation deviation of the model through its sampling ability. At the same time, the prediction index system is proposed to improve the analysis ability of the financial management model. The results show that the accuracy and recall rate of the improved algorithm based on random forest proposed in this study in identifying corporate financial distress are 98.03 % and 100 % respectively. The importance value of operating income and cash flow in enterprise indicators is 0.391, which is the most relevant indicator for enterprise financial forecasting. The results show that after the improvement of synthetic minority oversampling technique, the stochastic forest model can effectively improve the recognition and early warning ability of enterprises’ financial distress, and is conducive to maintaining good operating efficiency and sustainable operation of enterprises. Electronic manufacturing enterprises need to strengthen their attention to cash flow, improve their cash flow, and enhance their profitability. The financial management model designed by the research institute can provide technical and information support for financial early warning and sustainable development of electronic manufacturing enterprises.