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

Nicholas C. Barberis and Lawrence Jin

No 31081, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: D03 G02 G11 (search for similar items in EconPapers)
Date: 2023-03
New Economics Papers: this item is included in nep-fmk
Note: AP
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.nber.org/papers/w31081.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:31081

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w31081

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().