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Model-free and Model-based Learning as Joint Drivers of Investor Behavior

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  • Nicholas C. Barberis
  • Lawrence J. Jin

Abstract

Motivated by neural evidence on the brain's computations, cognitive scientists are increasingly adopting a framework that combines two systems, namely “model-free” and “model-based” learning. We import this framework into a financial setting, study its properties, and use it to account for a range of facts about investor behavior. These include extrapolative demand, experience effects, the disconnect between investor allocations and beliefs in the frequency domain and the cross-section, the inertia in investors’ allocations, and stock market non-participation. Our results suggest that model-free learning plays a significant role in the behavior of some investors.

Suggested Citation

  • Nicholas C. Barberis & Lawrence J. Jin, 2023. "Model-free and Model-based Learning as Joint Drivers of Investor Behavior," NBER Working Papers 31081, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31081
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    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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