Optimal Stopping in a Dynamic Salience Model

While many puzzles in static choices under risk can be explained by a preference for positive and an aversion toward negative skewness, little is known about the implications of such skewness preferences for decision making in dynamic problems. Indeed, skewness preferences might play an even bigger role in dynamic environments because, even if the underlying stochastic process is symmetric, the agent can endogenously create a skewed distribution of returns through the choice of her stopping strategy. Guided by salience theory, we theoretically and experimentally analyze the implications of skewness preferences for optimal stopping problems. We find strong support for all salience-based predictions in a laboratory experiment, and we verify that salience theory coherently explains skewness preferences revealed in static and dynamic decisions. Based on these findings we conclude that the static salience model—unlike (static) cumulative prospect theory—can be reasonably applied to dynamic decision problems. Our results have important implications for common optimal stopping problems such as when to sell an asset, when to stop gambling