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Action-State Dependent Dynamic Model Selection

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  • Francesco Cordoni
  • Alessio Sancetta

Abstract

A model among many may only be best under certain states of the world. Switching from a model to another can also be costly. Finding a procedure to dynamically choose a model in these circumstances requires to solve a complex estimation procedure and a dynamic programming problem. A Reinforcement learning algorithm is used to approximate and estimate from the data the optimal solution to this dynamic programming problem. The algorithm is shown to consistently estimate the optimal policy that may choose different models based on a set of covariates. A typical example is the one of switching between different portfolio models under rebalancing costs, using macroeconomic information. Using a set of macroeconomic variables and price data, an empirical application to the aforementioned portfolio problem shows superior performance to choosing the best portfolio model with hindsight.

Suggested Citation

  • Francesco Cordoni & Alessio Sancetta, 2023. "Action-State Dependent Dynamic Model Selection," Papers 2307.04754, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:2307.04754
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    References listed on IDEAS

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    1. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
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