Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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A Comparative Analysis of the Prediction Models for the Direction of Stock Price Using the Online Company Reviews

기업 리뷰 정보를 활용한 주가 방향 예측 모델 비교 분석

  • Lim, Yongtaek (Department of Bigdata Convergence, Korea University) ;
  • Lim, Heuiseok (Department of Computer Science and Engineering, Korea University)
  • 임용택 (고려대학교 빅데이터융합학과) ;
  • 임희석 (고려대학교 컴퓨터학과)
  • Received : 2020.05.06
  • Accepted : 2020.08.20
  • Published : 2020.08.28
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Abstract

Most of the stock price prediction research using text mining uses news and SNS data. However, there is a weakness that it is difficult to get honest and vivid information about companies from them. This paper deals with the problem of the prediction for the direction of stock price by doing text mining the online company reviews of internal staff indicating employee satisfaction. The comparative analysis of the prediction models for the direction of stock price showed the prediction model, which adds internal employee reviews, has better performance than those that did not. This paper presents the convergence study using natural language processing in financial engineering. In the field of stock price prediction, This paper pursued a new methodology that used employee satisfaction. In practice, it is expected to provide useful information in the field of forecasting stock price direction.

텍스트 마이닝을 활용한 주가 방향 예측 연구에서는 대부분 뉴스, SNS 데이터를 사용하고 있다. 하지만 뉴스, SNS 데이터로부터 기업에 대한 솔직하고 생생한 정보는 얻기 어렵다는 약점이 존재한다. 본 논문에서는 실제 근무 경험이 있는 내부 직원의 기업 리뷰를 반영하여, 종업원 만족도를 활용한 주가의 방향성을 예측하는 문제를 다룬다. 머신러닝 모델별 성능평가를 통해 예측 정확도를 비교, 분석한 결과 종업원의 기업 리뷰 데이터를 추가로 이용한 주가 방향 예측 모델은 그렇지 않은 모델 대비 뛰어난 분류 성과를 보였다. 본 연구는 금융 공학에 자연어처리기술을 활용한 융합 연구로서 주가 예측 분야에서 종업원 만족도를 활용한 기존에 없던 새로운 방법론을 추구하였다. 실무적으로 주가 방향 예측 분야에 유용한 정보를 제공할 것으로 기대된다.

Keywords

References

  1. J. Y. Park, J. P. Ryu & H. J. Shin. (2016). Predicting KOSPI Stock Index using Machine Learning Algorithms with Technical Indicators. Journal of Information Technology and Architecture, 13(2), 331-340.
  2. D. W. Hah, Y. M. Kim & J. J. Ahn. (2019). A study on KOSPI 200 direction forecasting using XGBoost model. Journal of the Korean Data And Information Science Society, 30(3), 655-669 https://doi.org/10.7465/jkdi.2019.30.3.655
  3. J. Y. Heo & J. Y. Yang. (2015). SVM based Stock Price Forecasting Using Financial Statements. KIISE Transactions on Computing Practices, 21(3), 167-172. https://doi.org/10.5626/KTCP.2015.21.3.167
  4. J. S. Jeong, D. S. Kim & J. G. Kim. (2015). A Study on the Prediction of Individual Stock Prices Using Online News Sensitivity Analysis. Korea Intelligent Information Systems Society, 45-58.
  5. D. Y. Kim, J. W. Park & J. H. Choi. (2014). A Comparative Study between Stock Price Prediction Models Using Sentiment Analysis and Machine Learning Based on SNS and News Articles. Journal of Information Technology Services, 13(3), 221-233. https://doi.org/10.9716/KITS.2014.13.3.221
  6. J. B. Kim & H. J. Kim. (2017). A domain-specific sentiment lexicon construction method for stock index directionality. Journal of Digital Contents Society 18(3), 2017.6, 585-592 https://doi.org/10.9728/dcs.2017.18.3.585
  7. Y. J. Yi & C. L. Lee. (2006). The Effects of Customer Satisfaction on Firm's Profitability and Value. Korean Journal of Marketing, 21, 85-113.
  8. Zeithaml, V. A., Berry, L. L. & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of marketing, 60(2), 31-46. https://doi.org/10.2307/1251929
  9. O. R. Oh. (2017). The Effects of Working Conditions on Stock Value. Journal of Taxation and Accounting, 18(6), 107-128. https://doi.org/10.35850/KJTA.18.6.04
  10. Best, R. J. (2008). Employee satisfaction, firm value and firm productivity. Retrieved August, 23, 2013.
  11. Edmans, A. (2011). Does the stock market fully value intangibles? Employee satisfaction and equity prices. Journal of Financial economics, 101(3), 621-640. https://doi.org/10.1016/j.jfineco.2011.03.021
  12. D. W. Kim, J. Y. Kang & J. I. Lim. (2016). Comparative Analysis of Job Satisfaction Factors, Using LDA Topic Modeling by Industries. Journal of the Korean IT Service. 15, 157-171.
  13. Jobplanet. https://www.jobplanet.com
  14. NAVER finance. https://finance.naver.com/
  15. E. J. Park & S. J. Cho. (2014). KoNLPy: Korean natural language processing in Python. Proceedings of the 26th Annual Conference on Human & Cognitive Language Technology, 1-4.