The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A hidden Markov model for predicting global stock market index

은닉 마르코프 모델을 이용한 국가별 주가지수 예측

  • Kang, Hajin (Department of Applied Statistics, Chung-Ang University) ;
  • Hwang, Beom Seuk (Department of Applied Statistics, Chung-Ang University)
  • 강하진 (중앙대학교 응용통계학과) ;
  • 황범석 (중앙대학교 응용통계학과)
  • Received : 2021.01.15
  • Accepted : 2021.02.15
  • Published : 2021.06.30
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Abstract

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

은닉 마르코프 모델(hidden Markov model, HMM)은 은닉된 상태와 관찰 가능한 결과의 두 가지 요소로 이루어진 통계적 모형으로 확률론적 접근이 가능하고, 다양한 수학적인 구조를 가지고 있어 여러 분야에서 활발하게 사용되고 있다. 특히 금융 분야의 시계열 데이터에 응용되어 다양한 연구가 진행되고 있다. 본 연구는 HMM 이론을 국내 KOSPI200 주가지수와 더불어 NIKKEI225, HSI, S&P500, FTSE100과 같은 해외 주가지수 예측에 적용해 보고자 한다. 또한, 최근 인공지능 분야의 발전으로 인해 주식 가격 예측에 빈번하게 사용되는 서포트 벡터 회귀(support vector regression, SVR) 결과와 어떤 차이가 있는지 비교하여 살펴보고자 한다.

Keywords

Acknowledgement

이 논문은 2020년도 중앙대학교 연구장학기금 지원에 의한 것임.

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