Is Rational Asset Valuation in Unstable Financial Environments Possible? Experimental Evidence Based on the Bandit Problem (Job Market paper)
An important issue in financial decision-making is the way people process new information. Prior studies have questioned the ability of people to use Bayes’ law in decision-making. None of those studies however probe situations similar to those typically encountered in financial markets. Here we explore, both theoretically and empirically, whether agents can apply Bayes’ law in a finance environment, captured as a nonstationary bandit task – the “Boardgame”. In it, we isolated the instability encountered in modern financial markets in the form of sudden changes (“jumps”) in the return processes. From subjects’ choices, we determined whether their learning in the task reflected optimal Bayesian inference instead of simple “Reinforcement Learning.” In contrast to the latter win-keep lose-switch heuristic, the Bayesian models accommodate nonstationarity either by tracking the probability of a jump or by dynamically adjusting the memory of learning through jump detection. Both Bayesian models fitted our data better than the Reinforcement Learning model did. This result suggests that agents may be better fit to learn in financial markets than previously thought. More broadly, it suggests that humans are wired to face very complex settings, and should prompt a reexamination of the scope of the bounded rationality paradigm.
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Hierarchical Versus Forgetting Bayes: Probabilistic Learning under Changing Probabilities (with P. Bossaerts) (submitted)
This paper examines with simulated data the learning efficiency and the fitness of the behavioral models introduced in the job market paper.
Risk, Estimation Uncertainty, and Unexpected Uncertainty: Bayesian Learning in Unstable Settings (with P. Bossaerts) (revise and resubmit, PLoS Computational Biology)
This paper explores the neurological foundations of the behavior at work in the Boardgame experiment.
