Abstract
Stock market prediction is one of the complex analysis of all time. Different expert analysts, as well as computer scientists, are working for the development of a stable and robust platform for the prediction of future stock value. The primary challenge is the nature of the movement of the daily price, which depends on various factors. To build a predictive model for the analysis of stock data and prediction is an active area of research. However, we found only a few numbers of studies performed on Korean stock market analysis, including both KOSDAQ and KOSPI companies. This study proposed a three-stage approach based on Natural Language Processing and Deep Learning techniques to analyze, comprehends the past and present market scenario, and also predict the future value of a stock. This study involves the application of natural processing techniques and deep learning techniques on around 2500 Korean companies covering KOSDAQ and KOSPI. Firstly, this paper successfully presents the current condition of the stock and overall Korean stock exchange; secondly, it recommends the potential months and weeks for investment, and finally, it predicts the future value of a stock with high accuracy. This paper may pose as a structural framework for developing a complete stock market prediction application.
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Acknowledgment
This research was supported by the MISP (Ministry of Science, ICT & Future Planning), Korea, under the National Program for Excellence in SW) supervised by the IITP (Institute for Information & communications Technology Promotion) having number 1711102971.
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Chatterjee, I., Gwan, J., Kim, Y.J., Lee, M.S., Cho, M. (2021). An NLP and LSTM Based Stock Prediction and Recommender System for KOSDAQ and KOSPI. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_40
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