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Forecasting stock prices using a hierarchical Bayesian approach

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  • Lynn Kuo

    (University of Connecticut, USA)

  • Jun Ying

    (Indiana University School of Medicine, USA)

  • Gim S. Seow

    (University of Connecticut, USA)

Abstract

The Ohlson model is evaluated using quarterly data from stocks in the Dow Jones Index. A hierarchical Bayesian approach is developed to simultaneously estimate the unknown coefficients in the time series regression model for each company by pooling information across firms. Both estimation and prediction are carried out by the Markov chain Monte Carlo (MCMC) method. Our empirical results show that our forecast based on the hierarchical Bayes method is generally adequate for future prediction, and improves upon the classical method. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Lynn Kuo & Jun Ying & Gim S. Seow, 2005. "Forecasting stock prices using a hierarchical Bayesian approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 39-59.
    • Handle: RePEc:jof:jforec:v:24:y:2005:i:1:p:39-59
    DOI: 10.1002/for.933
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    References listed on IDEAS

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