Using Deep Learning to Develop a Stock Price Prediction Model Based on Individual Investor Emotions. Issue 4 (18th October 2021)

• Record Type: Journal Article
• Authors: Chun, Jaeheon; Ahn, Jaejoon; Kim, Youngmin; Lee, Sukjun
• Is Part Of: Journal of behavioral finance. Volume 22:Issue 4(2021)
• Page Start: 480
• Page End: 489
• Publication Date: 2021-10-18
• Subjects: Stock price prediction -- Multidimensional emotions -- Emotion indicator -- Deep neural network; Capital market -- Psychological aspects -- Periodicals; Finance -- Psychological aspects -- Periodicals; 332.019
• DOI: 10.1080/15427560.2020.1821686
• Languages: English
• ISSNs: 1542-7560

Abstract

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 ( t f − idf ). 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.