XGBoost, LightGBM을 이용한 종목추천 뉴스기사 기반 주식 수익률 방향성 예측
Prediction of Stock Returns from News Article‘s Recommended Stocks Using XGBoost and LightGBM Models
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투자자는 수익의 극대화를 위해 언론사의 기사를 포함한 다양한 정보를 활용하여 투자 전략을수립한다. 이에 국내 언론사에서도 신뢰도 있는 투자정보를 제공하기 위해, 애널리스트의 종목분석 보고서에 기초한 종목 추천기사를 게재하고 있다. 본 연구에서는 종목 추천기사 게재를 하나의 사건(event)으로 간주하고, XGBoost와 LightGBM 모델을 활용하여 기사 게재 10일 이후 가격의상승 또는 하락을 예측하는 분류 모델을 제시한다. 또한, 전체 추천종목을 유가증권시장과 코스닥시장 및 기업규모(대형/소형)에 따라 4가지로 분류하고, 하위 그룹에 따라 모델의 예측 정확도에차이가 있는지 파악하고자 한다. 학습 결과 전체 모델의 분류 정확도는 XGBoost 75%, LightGBM 71%로 나타났고, 예측 정확도는 유가증권 시장 예측력이 코스닥시장 주식 대비 높게 나타났으며, 대형주의 예측력이 소형주 보다 높게 나타났다. 마지막으로, SHAP(Shapley Additive exPlanations) 분석을 통해 개별 모델의 예측에 중요한 변수를 살펴보고 모델의 해석력을 제고하였다.
Abstract
This study examines the relationship between the release of the news and the individual stock returns. Investors utilize a variety of information sources to maximize stock returns when establishing investment strategies. News companies publish their articles based on stock recommendation reports of analysts, enhancing the reliability of the information. Defining release of a stock-recommendation news article as an event, we examine its economic impacts and propose a binary classification model that predicts the stock return 10 days after the event. XGBoost and LightGBM models are applied for the study with accuracy of 75%, 71% respectively. In addition, after categorizing the recommended stocks based on the listed market(KOSPI/KOSDAQ) and market capitalization(Big/Small), this study verifies difference in the accuracy of models across four sub-datasets. Finally, by conducting SHAP(Shapley Additive exPlanations) analysis, we identify the key variables in each model, reinforcing the interpretability of models.
참고문헌 (17)
- Chi Young Song, "News and Financial Prices", International Economic Journal, 8(3), pp.1~34 (2002)
- Cutler, David M., "What moves stock prices?", DOI 10.3386, 1988
- Tetlock, Paul C., "Giving content to investor sentiment : The role of media in the stock market", The Journal of finance, 62(3), pp.1139~1168 (2007)[DOI]
- G Serafeim, "Which corporate ESG news does the market react to?", Financial Analysts Journal, 78(1), pp.59~78 (2022)[DOI]
- Yoosin Kim, "Stock-Index Invest Model Using News Big Data Opinion Mining", Journal of Intelligence and Information Systems, 18(2), pp.143~156 (2012)[DOI]
- Hyeon Jiwon, "Sentiment Analysis of News on Corporation Using KoBERT", Korean Accounting Review, 47(4), pp.33~54 (2022)[DOI]
- Doo-Won Kang, "A study on Deep Learning-based Stock Price Prediction using News Sentiment Analysis", Journal of The Korea Society of Computer and Information, 27(8), pp.31~39 (2022)[DOI]
- Cheol Park, "Individual Trading Behavior Comparison Around Ex-Dividend Days Before and After the Dividend Tax Changes", Korean Journal of Financial Studies (2010)
- Hyunseok Kim, "Stock Return and Liquidity Effects of Bonus Issues, Stock Splits and Stock Dividends: Evidence from Korea", Korean Journal of Financial Studies (2018)[DOI]
- Lee Seokgyu, "Analysis of Market Response to Merger Announcements", Joumal of Accountifng and Finance, 18, pp.1~22 (2002)
- Suyeon Yang, "The prediction of the stock price movement after IPO using machine learning and text analysis based on TF-IDF", Journal of Intelligence and Information Systems, 28(2), pp.237~262 (2022)[DOI]
- Cui Jinhua, "Predicting Stock Prices Based on Neural Networks Around Earnings Announcements", Journal of The Korean Data Analysis Society, 22(6), pp.2667~2678 (2020)[DOI]
- Myung-woo Nam, "Prediction Of Traffic Accident Casualties Using Machine Learning : For Seoul Public Data", Korea Society of Computer Information Spring Conference Proceedings, 29(1), pp.27~30 (2021)
- Pei-Fen Tsai, "Stock Selection Using Machine Learning Based on Financial Ratios", Mathematics, 11(23), pp.4758~ (2023)[DOI]
- Reza Gharoie Ahangar, "The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange", International Journal of Computer Science and Information Security, 7(2) (2010)[DOI]
- Lundberg, S. M., "A unified approach to interpreting model predictions", Advances in neural information processing systems, 30 (2017)
- Hyung-Rok Oh, "Occupational accident prediction modeling and analysis using SHAP", Journal of Digital Contents Society, 22(7), pp.1115~1123 (2021)[DOI]