Forecasting and decision making of firm’s financial indicators based on the SSA-MLP-BPNN model

Abstract

It is a complicated and important task to forecast and make decisions about financial indicators of listed enterprises, because accurate prediction can help enterprises better plan their financial strategy and business development. In recent years, with the development of artificial intelligence and machine learning technologies, more and more researchers begin to apply these technologies to the prediction and decision-making of enterprise financial indicators.In this paper, we develop a model combined with the Sparrow Search Algorithm(SSA), Multilayer Perceptron(MLP) and Back Propagation Neural Network(BPNN) (SSA-MLP-BPNN model) to study the prediction and decision-making of financial indicators of listed companies in China. By comparing the prediction results of SSA-MLP-BP model with other optimization algorithms, it is found that the SSA optimization algorithm performs superiorly in improving the performance of the MLP-BP model, and it is easier to find the global optimal solution, which improves the prediction accuracy of the model. The proposed algorithm can accelerate the convergence speed, leading to faster and more efficient training. Different optimization algorithms may perform differently on different datasets, so it is necessary to choose the appropriate optimization algorithm according to the specific situation. This study can provide reference for the prediction and decision-making of firm’s financial indicators.