A CNN-LSTM Stock Prediction Model Based on Genetic Algorithm Optimization

Abstract

Predicting the stock market remains a difficult field because of its inherent volatility. With the development of artificial intelligence, research using deep learning for stock price prediction is increasing, but the importance of applying a prediction system consisting of preparing verified data and selecting an optimal feature set is lacking. Accordingly, this study proposes a GA optimization-based deep learning technique (CNN-LSTM) that predicts the next day's closing price based on an artificial intelligence model to more accurately predict future stock values. In this study, CNN extracts features related to stock price prediction, and LSTM reflects the long-term history process of input time series data. Basic stock price data and technical indicator data for the last 20 days prepare a data set to predict the next day's closing price, and then a CNN-LSTM hybrid model is set. In order to apply the optimal parameters of this model, GA was used in combination. The Korea Stock Index (KOSPI) data was selected for model evaluation. Experimental results showed that GA-based CNN-LSTM has higher prediction accuracy than single CNN, LSTM models, and CNN-LSTM model. This study helps investors and policy makers who want to use stock price fluctuations as more accurate predictive data using deep learning models.