How do people cope with tail risk? In a lab experiment that removed informational and incentive confounds, subjects overwhelmingly behaved like Bayesian learners. The results of simulations further revealed that if one is to survive under tail risk, one needs to follow the Bayesian approach, as all boundedly rational alternatives fail. These findings support the Bayesian assumption commonly made in prior studies on tail risk and model uncertainty, and they also demonstrate the importance of optimal learning under tail risk. Received February 15, 2017; editorial decision December 24, 2017 by Editor Andrew Karolyi. Authors have furnished supplementary data and code, which are available on the Oxford University Press Web site next to the link to the final published paper online.

Neoclassical finance assumes that investors are Bayesian. In many realistic situations, Bayesian learning is challenging. Here, we consider investment opportunities that change randomly, while payoffs are observable only when invested. In a stylized version of the task, we wondered whether performance would be affected if one were to follow reinforcement learning principles instead. The answer is a definite yes. When asked to perform our task, participants overwhelmingly learned in a Bayesian way. They stopped being Bayesians, though, when not nudged into paying attention to contingency shifts. This raises an issue for financial markets: who has the incentive to nudge investors?

How do people record information about the outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are hampered in their perception of outcomes that they expect to seldom encounter. We provide experimental evidence for such “outlier blindness” and discuss how it provides a microfoundation for neglected tail risk by investors in financial markets.

Neoclassical finance assumes that investors are Bayesian. In many realistic situations, Bayesian learning is challenging. Here, we consider investment opportunities that change randomly, while payoffs are observable only when invested. In a stylized version of the task, we wondered whether performance would be affected if one were to follow reinforcement learning principles instead. The answer is a definite yes. When asked to perform our task, participants overwhelmingly learned in a Bayesian way. They stopped being Bayesians, though, when not nudged into paying attention to contingency shifts. This raises an issue for financial markets: who has the incentive to nudge investors?

Applying a well-established neuroscience framework to the issue of investor perception of volatility, we propose that after prolonged exposure to high volatility, investors tend to underestimate volatility due to adaptation to the high volatility, and vice versa. Using a combination of field and laboratory tests, we find strong support for this hypothesis. The evidence suggests that this neurobiologically-grounded perceptual bias can cause distortions of asset prices in sophisticated and liquid financial markets.