An Information-Theoretic Asset Pricing Model
用信息论方法非参数估计定价核，比主流多因子模型样本外定价误差更小、截面拟合更好，发现标准因子未捕捉的风险源，年化alpha达20-37%，夏普比率1.1。
Abstract 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 stochastic discount factor (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 compensation for 2.5% tail events. The I-SDF offers a powerful benchmark relative to which competing theories and investment strategies can be evaluated.