| 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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Biography:
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Lawrence J. Jin is an (untenured) Associate Professor of Finance at Cornell’s SC Johnson College of Business and a Faculty Research Fellow at the National Bureau of Economic Research (NBER). His research focuses on asset pricing, behavioral finance, neuroeconomics, and household finance. Much of his research studies how biased beliefs and non-traditional preferences affect asset prices, investor behavior, and firm behavior. His recent work begins to incorporate a more fundamental set of ideas from psychology and neuroscience into models of economic and financial decision-making. Jin’s 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. His work has received the Q-Group’s Jack Treynor Prize, the AQR Top Finance Graduate Award, the Vernon L. Smith Excellence Award, and the MFA Outstanding Paper Award. Jin received his Ph.D. in Financial Economics from Yale University in May 2015. He holds a B.S. in Mathematics and Physics from Tsinghua University and a M.S. in Electrical Engineering from Caltech. He also spent two years as a research and trading analyst at Citigroup. Prior to coming to Cornell, Jin was on the faculty at the Division of the Humanities and Social Sciences at Caltech.
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