Prediction of the Stock Adjusted Closing Price Based On Improved PSO-LSTM Neural Network

Abstract

Volatility in the stock market has a significant impact on all finance-related fields. As an important part of stock data, the adjusted closing price often reflects the attention of market funds to a stock, helping predict the market movement of the next trading day, especially for short-term investors. With the development of artificial intelligence technology, the machine learning algorithms are widely applied to predict stock trends. However, the noisy, nonlinear, and chaotic nature of stock price changes makes the prediction not accurate enough. Hence, we proposed a hybrid prediction model combining improved particle swarm optimization (IPSO) and long short-term memory (LSTM) neural network to predict the adjusted closing price of the stock. In this paper, nonlinear methods are presented to optimize the velocity inertia weight and learning factors of traditional particle swarm optimization (PSO). Meanwhile, IPSO is used to optimize the hyperparameters of LSTM neural network to improve its prediction accuracy. The experiments proved that the proposed IPSO-LSTM outperformed the Autoregressive Integrated Moving Average model (ARIMA), LSTM, and PSO-LSTM on the prediction of the S&P 500 Index. Furthermore, the Dow Jones Industrial Average Index (DJI) and Nasdaq Composite Index (IXIC) were chosen to verify the accuracy and robustness of the model we put forward.