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

• Published in: The journal of behavioral finance Vol. 22; no. 4; pp. 480 - 489
• Main Authors: Chun, Jaeheon, Ahn, Jaejoon, Kim, Youngmin, Lee, Sukjun
• Format: Journal Article
• Language: English
• Published: Routledge 18.10.2021
• Subjects: Deep neural network; Emotion indicator; Multidimensional emotions; Stock price prediction
• ISSN: 1542-7560; 1542-7579
• DOI: 10.1080/15427560.2020.1821686

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 ( ), which modifies term frequency-inverse document frequency ( ). Stock price is predicted using a deep neural network (DNN). To compare the performance of the ESPS, sentiment analysis and a naïve 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.