Using Deep Learning to Develop a Stock Price Prediction Model Based on Individual Investor Emotions

Abstract

The general purpose of stock price prediction is to help stock analysts design a strategy to increase stock returns. We present the conceptual framework of an emotion-based stock prediction system (ESPS) focused on considering the multidimensional emotions of individual investors. To implement and evaluate the proposed ESPS, emotion indicators (EIs) are generated using emotion term frequency-inverse emotion document frequency (etf - iedf), which modifies term frequency-inverse document frequency (tf - idf). Stock price is predicted using a deep neural network (DNN). To compare the performance of the ESPS, sentiment analysis and a naive method are employed. The prediction accuracy of the experiments using EIs was the highest at 95.24%, 96.67%, 94.44%, and 95.31% for each training period. The accuracy of prediction using EIs was better than the accuracy of prediction using other methods.