Using Deep Learning to Develop a Stock Price Prediction Model Based on Individual Investor Emotions
- Authors
- Chun, Jaeheon; Ahn, Jaejoon; Kim, Youngmin; Lee, Sukjun
- Issue Date
- 2021
- Publisher
- Taylor and Francis Inc.
- Keywords
- Stock price prediction; Multidimensional emotions; Emotion indicator; Deep neural network
- Citation
- Journal of Behavioral Finance
- Journal Title
- Journal of Behavioral Finance
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2219
- DOI
- 10.1080/15427560.2020.1821686
- ISSN
- 1542-7560
1542-7579
- 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.
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Collections - SCH Media Labs > Department of Big Data Engineering > 1. Journal Articles
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