Research on financial risk prediction and prevention for small and medium-sized enterprises - based on a neural network

Abstract

For companies, timely and accurate risk prediction plays an an essential role in sustaining business growth. In this paper, firstly, the financial risk of small and medium-sized enterprises (SMEs) was simply analyzed. Some financial indicators were selected, and then some of the indicators were eliminated by Mann-Whitney U test and Pearson test. For risk prediction, an improved sparrow search algorithm-back-propagation neural network (ISSA-BPNN) method was designed by optimizing the BPNN with the piecewise linear chaotic map (PWLCM)-improved SSA. Experiments were performed on 82 special treatment (ST) enterprises and 164 non-ST enterprises. The results showed that the BPNN had higher accuracy in risk prediction than methods such as Fisher discriminant analysis; the optimization of the ISSA for the BPNN was reliable as the accuracy and F1 value of the ISSA-BPNN method were 0.9834 and 0.9425, respectively; the prediction was wrong for only one sample out of 20 randomly selected samples. The results demonstrate the reliability and practical applicability of the ISSA-BPNN method.