Tae Seung Kim, Soowon Lee

KIPS Transactions on Software and Data Engineering, Vol. 10, No. 9, pp. 367-374, Sep. 2021
https://doi.org/10.3745/KTSDE.2021.10.9.367,   PDF Download:
Keywords: Stock Price Fluctuation Prediction, Deep Learning, Overseas Indices, Trading Trends by Investor
Abstract

Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models

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