Research on Financial Risk Early Warning Based on Deep Neural Network and Logistic Model

This study mainly discusses how to combine deep neural network with traditional Logistic regression model to improve the accuracy of financial risk prediction. In this paper, the basic principles and characteristics of deep neural network and Logistic regression model are described in detail. In this paper, we design a fusion model that utilizes long short-term memory networks (LSTM) and multi-layer perceptrons (MLP) in deep neural networks to learn potential nonlinear relationships and patterns through large amounts of historical financial data. These networks work through multiple hidden layers to extract complex features from the data, which are then used as inputs to a Logistic regression model for final risk prediction and classification. The results of the fusion model were compared with those of LSTM, MLP or Logistic regression models alone. The experimental data comes from listed companies in multiple industries, and the results show that the fusion model is superior to the single model in multiple key performance indicators, which verifies the effectiveness of the combination of deep learning and traditional statistical methods.