Journal of Information Technology Applications and Management

  • 제22권3호
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  • Pages.1-18
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  • 2015
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  • 1598-6284(pISSN)
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  • 2508-1209(eISSN)
한국데이터전략학회 (Korea Data Strategy Society)
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A Selective Induction Framework for Improving Prediction in Financial Markets

  • Kim, Sung Kun (Information Systems, School of Business, Chung-Ang University)
  • 투고 : 2015.06.24
  • 심사 : 2015.09.04
  • 발행 : 2015.09.30
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초록

Financial markets are characterized by large numbers of complex and interacting factors which are ill-understood and frequently difficult to measure. Mathematical models developed in finance are precise formulations of theories of how these factors interact to produce the market value of financial asset. While these models are quite good at predicting these market values, because these forces and their interactions are not precisely understood, the model value nevertheless deviates to some extent from the observable market value. In this paper we propose a framework for augmenting the predictive capabilities of mathematical model with a learning component which is primed with an initial set of historical data and then adjusts its behavior after the event of prediction.

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참고문헌

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