Dynamic Inconsistency in Risky Choice: Evidence from the Lab and Field
71 Pages Posted:
Date Written: May 13, 2020
Many economically important settings, from financial markets to consumer choice, involve dynamic decisions under risk. People are willing to accept risk as part of a sequence of choices---even when it is fair or has a negative expected value---while at the same time rejecting positive-expected value gambles offered in isolation. We use a unique brokerage dataset containing traders' ex-ante investment plans and their subsequent decisions (N=190,000) and two pre-registered experiments (N=940) to study what motivates decisions to take risk in dynamic environments. In both settings, people accept risk as part of a ``loss-exit'' strategy---planning to take more risk after gains and stop after losses. Notably, this strategy generates a positively-skewed outcome distribution that is not available when the same gambles are offered in isolation. People's actual behavior exhibits the reverse pattern, deviating from their intended strategy by cutting gains early and chasing losses. More individuals are willing to accept risk when offered a commitment to the initial strategy, which suggests at least partial sophistication about this dynamic inconsistency. We use our data to formally identify a model of decision-making that predicts both the observed deviations in planned versus actual behavior, as well as the discrepancy in risk-taking in static and dynamic environments. We then use this model to quantify the welfare costs of naivete in our setting. Together, our results have implications for evaluating the welfare consequences of behavioral biases in dynamic settings, such as the disposition effect, and highlight potentially unintended effects of regulation mandating non-binding commitment.
Keywords: dynamic risk taking, choice under uncertainty, probability weighting, dynamic inconsistency, behavioral economics, retail trading, limit orders
JEL Classification: D01 D09 D9 G4
Suggested Citation: Suggested Citation