Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 31, 2023 erstellt
Description:
We propose an experimental method to elicit stopping times—each subject’s complete contingent plan for taking a risk for up to five times—to study repeated risk-taking under precommitment. In addition to time- and outcome-contingent risk-taking, we allow some subjects to use path-dependent or randomized stopping times. Our experimental design thus allows for hundreds of different risk-taking plans. Using an unsupervised machine-learning algorithm, we find that individuals’ risk-taking strategies map well to stop-loss, take-profit, or buy-and-hold strategies. Most strategies are of a continue-when-winning and stop-when-losing type, with a profit-trailing stopping barrier. Path-dependence and randomization are used extensively, even if they are costly. We further analyze dynamic consistency in a sequential risk-taking task and find that subjects largely follow the unconstrained plans that we elicited