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A Dynamic Modeling and Validation Framework for the Market Direction Prediction

Author

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  • Xugang Ye

    (Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA)

  • Jingjing Li

    (Leeds School of Business, University of Colorado, Boulder, CO, USA)

Abstract

There are many research papers talking about building various machine learning models to predict the market index. However, very few attention has been paid to effectively validating or calibrating the prediction results. The focus of this paper is to present a dynamic modeling and validation framework for the market direction prediction. The central idea is to calibrate the probabilistic prediction by estimating two conditional probabilities of correct forecast from the dynamic validation data set. The calibration method can be combined with any predictive model that generates probabilistic prediction of the market direction.

Suggested Citation

  • Xugang Ye & Jingjing Li, 2015. "A Dynamic Modeling and Validation Framework for the Market Direction Prediction," International Journal of Business Analytics (IJBAN), IGI Global, vol. 2(2), pages 1-13, April.
    • Handle: RePEc:igg:jban00:v:2:y:2015:i:2:p:1-13
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