Machine Traders, Human Behavior, and Model (Mis)Specification

Abstract

I examine how investors utilize data, exploiting a setting in which investors design machine-driven trading strategies under controlled yet realistic conditions. Investors disagree considerably in how they interpret identical information, leading to widely dispersed trading strategies and performance outcomes. Inexperienced investors underweight variables with predictive power for returns, and instead exhibit a bias towards variables with which they are more familiar. With experience, investors learn to overcome their bias, and benefit substantially from additional data availability. Investors' familiarity bias leads them to mis-specify their models of the world, and is encoded by the machine traders they design.

Keywords: Experience, Familiarity Bias, Predictive Models, Machine Learning, Big Data

JEL Classification: G11, G14, G41