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http://dx.doi.org/10.9717/kmms.2020.24.3.448

Machine Learning Based Stock Price Fluctuation Prediction Models of KOSDAQ-listed Companies Using Online News, Macroeconomic Indicators, Financial Market Indicators, Technical Indicators, and Social Interest Indicators  

Kim, Hwa Ryun (Dept. of Economics, Seoul Women's University)
Hong, Seung Hye (Dept. of Economics, Seoul Women's University)
Hong, Helen (Dept. of Software Convergence, Seoul Women's University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.3, 2021 , pp. 448-459 More about this Journal
Abstract
In this paper, we propose a method of predicting the next-day stock price fluctuations of 10 KOSDAQ-listed companies in 5G, autonomous driving, and electricity sectors by training SVM, XGBoost, and LightGBM models from macroeconomic·financial market indicators, technical indicators, social interest indicators, and daily positive indices extracted from online news. In the three experiments to find out the usefulness of social interest indicators and daily positive indices, the average accuracy improved when each indicator and index was added to the models. In addition, when feature selection was performed to analyze the superiority of the extracted features, the average importance ranking of the social interest indicator and daily positive index was 5.45 and 1.08, respectively, it showed higher importance than the macroeconomic financial market indicators and technical indicators. With the results of these experiments, we confirmed the effectiveness of the social interest indicators as alternative data and the daily positive index for predicting stock price fluctuation.
Keywords
Stock Prediction; Google Trends; Opinion Mining; SVM; XGBoost; LightGBM;
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Times Cited By KSCI : 3  (Citation Analysis)
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