Seminar (Dept. of Finance)

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

Speaker：Lawrence Jin, Assistant Professor of Finance, California Institute of Technology

Time：2022-03-18 09:00

Address：Online Meeting

Language：英文

ABSTRACT:

In the past decade, researchers in psychology and neuroscience studying human decision-making have increasingly adopted a framework that combines two systems, namely “model-free” and “model-based” learning. We import this framework into a simple financial setting, study its properties, and link it to a wide range of applications. We show that it provides a foundation for extrapolative demand and experience effects; resolves a puzzling disconnect between investor allocations and beliefs in both the frequency domain and the cross-section; helps explain the dispersion in stock market allocations across investors as well as the inertia in these allocations over time; and sheds light on the persistence of household investment mistakes. More broadly, the framework offers a way of thinking about individual behavior that is grounded in recent evidence on the computations that the brain undertakes when estimating the value of a course of action.

SHORT BIOGRAPHY:

Lawrence J. Jin received his Ph.D. in Financial Economics from Yale University in May 2015. His research focuses on asset pricing, behavioral finance, neuroeconomics, and household finance. He holds a B.S. in Mathematics and Physics from Tsinghua University and a M.S. in Electrical Engineering from Caltech. Prior to attending Yale, he spent two years as a research and trading analyst at Citigroup. His research has been published in the Quarterly Journal of Economics, the Journal of Finance, the Review of Financial Studies, and the Journal of Financial Economics. He has received the Q-Group's Jack Treynor Prize, the AQR Top Finance Graduate Award, the MFA Outstanding Paper Award, and Caltech's ASCIT Teaching Award.