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Does a Bayesian approach generate robust forecasts? Evidence from applications in portfolio investment decisions

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Abstract

We employ a statistical criterion (out-of-sample hit rate) and a financial market measure (portfolio performance) to compare the forecasting accuracy of three model selection approaches: Bayesian information criterion (BIC), model averaging, and model mixing. While the more recent approaches of model averaging and model mixing surpass the Bayesian information criterion in their out-of-sample hit rates, the predicted portfolios from these new approaches do not significantly outperform the portfolio obtained via the BIC subset selection method.

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Authors and Affiliations

  1. Graduate School of Management, University of California-Davis, Davis, CA, 95616-8609, USA

    Chih-Ling Tsai & Ning Zhu

  2. Department of Business Statistics and Econometrics, Guanghua School of Management, Peking University, 100871, Beijing, People’s Republic of China

    Hansheng Wang

Authors
  1. Chih-Ling Tsai
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  2. Hansheng Wang
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  3. Ning Zhu
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Correspondence to Chih-Ling Tsai.

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Tsai, CL., Wang, H. & Zhu, N. Does a Bayesian approach generate robust forecasts? Evidence from applications in portfolio investment decisions. Ann Inst Stat Math 62, 109–116 (2010). https://doi.org/10.1007/s10463-009-0250-4

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  • DOI: https://doi.org/10.1007/s10463-009-0250-4

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