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기계학습알고리즘을 이용한 위험회복지수의 개발과 활용

원문정보

Development and Application of Risk Recovery Index using Machine Learning Algorithms

김선웅

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초록

영어

Asset prices decline sharply and stock markets collapse when financial crisis happens. Recently we have encountered more frequent financial crises than ever. 1998 currency crisis and 2008 global financial crisis triggered academic researches on early warning systems that aim to detect the symptom of financial crisis in advance. This study proposes a risk recovery index for detection of good opportunities from financial market instability. We use SVM classifier algorithms to separate recovery period from unstable financial market data. Input variables are KOSPI index and V-KOSPI200 index. Our SVM algorithms show highly accurate forecasting results on testing data as well as training data. Risk recovery index is derived from our SVM-trained outputs. We develop a trading system that utilizes the suggested risk recovery index. The trading result records very high profit, that is, its annual return runs to 121%.

목차

Abstract
 1. 서론
 2. 선행 연구
 3. 제안 모형
  3.1 SVM 모형의 학습
  3.2 위험회복지수의 도출
 4. 실험 설계 및 실증 분석
  4.1 변동성지수 V-KOSPI200
  4.2 실험 데이터
  4.3 시스템 설계
  4.4 실증 분석 결과
  4.5 위험회복지수를 활용한 트레이딩시스템의 성과 분석
 5. 결론 및 연구의 한계점
 References

저자정보

  • 김선웅 Sun Woong Kim. Associate Professor, Graduate School of Business Information Technology, Kookmin University

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