Asset Pricing in the Resource-Constrained Brain

Despite scarcity being central to economics, the scarcity of brain’s internal resources has largely been ignored. Neuroscience research increasingly points to the brain evolving as a prediction engine in response to this internal-resource scarcity. The brain meets every situation with subconscious expectations, which are contrasted with information to generate error-signals. Selective processing of such error-signals, in lieu of the entire information-stream, saves brain-resources. I show that applying such predictive-processing to asset pricing gives rise to an alpha, with several empirically observed phenomena (value, momentum, size, high-alpha-of-low-beta, profitability, investment, overnight bias, and time-specific changes in SML slopes) corresponding to either cross-sectional or time-specific variations in this alpha. Additional insights about these phenomena emerge that are consistent with empirical evidence. This indicates that at least a part of the explanation of these puzzles is optimal utilization of the brain’s limited internal resources via predictive processing of incoming information.