A Stock Prices Prediction Approach Via Neural Network by Several Investor Indicators

Abstract

The prediction of stock indicators such as prices, trends and market indices is the focus of researchers. However, stock market has the characteristics of high noise and non-linearity. Generally, linear algorithms are not good for predicting stock market indicators. Therefore, BP neural network, a model suitable for nonlinear task, is widely used in stock market forecasting. However, many BP neural network prediction models are only based on historical stock quantitative data, and do not consider the impact of investor behavior on the stock market. Therefore, based on historical stock data and quantitative data of investor behavior of ten selected Chinese stocks, this paper trains a three-layer BP neural network to predict the stock prices such as the highest price the opening price ,the closing price, the lowest price in a short term. And then, the model that incorporates the investor behavior indicator is compared with the model that is not added. The results show that investor behavior indicators can improve the accuracy and generalization of the stock price forecasting model effectively, especially when the model based on stock quantitative data has a poor prediction accuracy on the test set.