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Predictive Decision Synthesis for Portfolios: Betting on Better Models

In: Recent Developments in Bayesian Econometrics and Their Applications

Author

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  • Emily Tallman

    (Duke University, Department of Statistical Science)

  • Mike West

    (Duke University, The Arts & Sciences Distinguished Professor Emeritus of Statistics & Decision Sciences, Department of Statistical Science)

Abstract

We discuss and develop Bayesian dynamic modeling and predictive decision synthesis for portfolio analysis. The context involves model uncertainty with a set of candidate models for financial time series with main foci in sequential learning, forecasting, and recursive decisions for portfolio reinvestments. The foundational perspective of Bayesian predictive decision synthesis (BPDS) defines novel, operational analysis and resulting predictive and decision outcomes. A detailed case study of BPDS in financial forecasting of international exchange rate time series and portfolio rebalancing, with resulting BPDS-based decision outcomes compared to traditional Bayesian analysis, exemplifies and highlights the practical advances achievable under the expanded, subjective Bayesian approach that BPDS defines.

Suggested Citation

  • Emily Tallman & Mike West, 2025. "Predictive Decision Synthesis for Portfolios: Betting on Better Models," Springer Books, in: Stepan Mazur & Pär Österholm (ed.), Recent Developments in Bayesian Econometrics and Their Applications, pages 223-249, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-00110-8_10
    DOI: 10.1007/978-3-032-00110-8_10
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