We show that a non-parametric estimate of the pricing kernel, extracted using an information-theoretic approach, delivers smaller out-of-sample pricing errors and a better cross-sectional fit than leading multi factor models. The information SDF (I-SDF) identifies sources of risk not captured by standard factors, generating very large annual alphas (20%-37%) and Sharpe ratio (1.1). The I-SDF extracted from a wide cross-section of equity portfolios is highly positively skewed and leptokurtic, and implies that about a third of the observed risk premia represent a compensation for 2.5% tail events. The I-SDF offers a powerful benchmark relative to which competing theories and investment strategies can be evaluated.
This is a revised version of January 2026. The previous version was dated April 2016.
