Research on financial assets transaction prediction model based on LSTM neural network

Abstract

In recent years, with the breakthrough of big data and deep learning technology in various fields, many scholars have begun to study the stock market time series by using deep learning technology. In the process of model training, the selection of training samples, model structure and optimization methods are often subjective. Therefore, studying these influencing factors is beneficial to provide scientific suggestions for the training of recurrent neural networks and is beneficial to improve the prediction accuracy of the model. In this paper, the LSTM deep neural network is used to model and predict the financial transaction data of Shanghai, and the three types of factors affecting the prediction accuracy of the model are systematically studied. Finally, a high-precision short-term prediction model of financial market time series based on LSTM deep neural network is constructed. In addition, this paper compares BP neural network, traditional RNN and RNN improved LSTM deep neural network. It proves that the LSTM deep neural network has higher prediction accuracy and can effectively predict the stock market time series.