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Maximizing equity market sector predictability in a Bayesian time-varying parameter model

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  • Johnson, Lorne D.
  • Sakoulis, Georgios
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    Abstract

    The Kalman filter methodology is employed to develop a dynamic sector allocation model for US equities. Bayesian parameter estimation and model selection criteria result in significantly improved sector return predictability over static or rolling parameter specifications. A simple trading strategy illustrates how widely tested financial and economic variables can be used as inputs in for a potentially profitable investment strategy.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4PV2RNY-1/1/36375a99a813d1a5e163fc073d0cc1a8
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 52 (2008)
    Issue (Month): 6 (February)
    Pages: 3083-3106

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    Handle: RePEc:eee:csdana:v:52:y:2008:i:6:p:3083-3106

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    Web page: http://www.elsevier.com/locate/csda

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    References

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    Cited by:
    1. BAUWENS, Luc & ROMBOUTS, Jeroen, 2009. "On marginal likelihood computation in change-point models," CORE Discussion Papers 2009061, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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