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.

zlynne@ceibs.edu