Topic: Efficient Coding and Risky Choice
Speaker: Lawrence J. Jin, Assistant Professor of Finance, California Institute of Technology
Date: June 26, 2019 (Wednesday)
Time: 10:00am-11:30am
Location: 4-102, Building 4
Language: English
Abstract:
We present a model of risky choice in which the decision maker (DM) perceives a lottery payoff with noise due to the brain's limited capacity to represent information. We model perception using the principle of efficient coding, which implies that perception is most precise for frequently occurring stimuli. Our model shows that it is efficient for risk taking to be more sensitive to those payoffs that the DM encounters more frequently. The model also predicts that the DM's value function fluctuates with the recently encountered distribution of payoffs. To test the model, we manipulate the distribution of payoffs in a laboratory experiment. We find that risk taking is indeed more sensitive to those payoffs that are presented more frequently. We then conduct an additional experiment to test efficient coding by incentivizing subjects to classify which of two symbolic numbers is larger. We find that subjects exhibit higher accuracy for those numbers that they have observed more frequently, providing further evidence that perception of a given numerical quantity varies with the recent environment. Overall, our experimental results suggest that risk taking depends systematically on the payoff distribution to which the DM's perceptual system has recently adapted.
About the speaker:
Lawrence J. Jin received his Ph.D. in Financial Economics from Yale University in May 2015. His research focuses on asset pricing, behavioral finance, financial intermediaries, 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 Review of Financial Studies and the Journal of Financial Economics. His JFE paper “X-CAPM: An Extrapolative Asset Pricing Model” received the Q-Group's 2014 Jack Treynor Prize—the prize recognizes superior academic papers with potential applications in the fields of investment management and financial markets. His job market paper “A Speculative Asset Pricing Model of Financial Instability” received the AQR Top Finance Graduate Award in 2015—the award recognizes finance Ph.D. graduates worldwide whose dissertations and broader research potential carry the greatest promise of making an impact on the practice of finance and in academia. His paper “Extrapolative Beliefs in the Cross-Section: What Can We Learn from the Crowds?” received the MFA Outstanding Paper Award.
