Model-free and Model-based Learning as Joint Drivers of Investor Behavior / Nicholas C. Barberis, Lawrence J. Jin.

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Barberis, Nicholas C

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w31081.Publication details: Cambridge, Mass. National Bureau of Economic Research 2023.Description: 1 online resource: illustrations (black and white)Subject(s):

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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.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w31081 (Browse shelf(Opens below))	Not For Loan

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March 2023.

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.