KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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SVM based Stock Price Forecasting Using Financial Statements

SVM 기반의 재무 정보를 이용한 주가 예측

  • 허준영 (한성대학교 컴퓨터공학과) ;
  • 양진용 (한성대학교 컴퓨터공학과)
  • Received : 2014.10.02
  • Accepted : 2015.01.16
  • Published : 2015.03.15
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Abstract

Machine learning is a technique for training computers to be used in classification or forecasting. Among the various types, support vector machine (SVM) is a fast and reliable machine learning mechanism. In this paper, we evaluate the stock price predictability of SVM based on financial statements, through a fundamental analysis predicting the stock price from the corporate intrinsic values. Corporate financial statements were used as the input for SVM. Based on the results, the rise or drop of the stock was predicted. The SVM results were compared with the forecasts of experts, as well as other machine learning methods such as ANN, decision tree and AdaBoost. SVM showed good predictive power while requiring less execution time than the other machine learning schemes.

기계 학습은 컴퓨터를 학습시켜 분류나 예측에 사용되는 기술이다. 그 중 SVM은 빠르고 신뢰할 만한 기계 학습 방법으로 분류나 예측에 널리 사용되고 있다. 본 논문에서는 재무 정보를 기반으로 SVM을 이용하여 주식 가격의 예측력을 검증한다. 이를 통해 회사의 내재 가치를 나타내는 재무정보가 주식 가격 예측에 얼마나 효과적인지를 평가할 수 있다. 회사 재무 정보를 SVM의 입력으로 하여 주가의 상승이나 하락 여부를 예측한다. 다른 기법과의 비교를 위해 전문가 점수와 기계 학습방법인 인공신경망, 결정트리, 적응형부스팅을 통한 예측 결과와 비교하였다. 비교 결과 SVM의 성능이 실행 시간이나 예측력면에서 모두 우수하였다.

Keywords

Acknowledgement

Supported by : 한성대학교

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