Simple Forecasts and Paradigm Shifts
研究在真实世界为多变量模型但投资者仅使用简单单变量模型进行学习时的资产定价含义，理论预测了价值-魅力收益差、波动率和偏度的可预测变化，并得到实证验证。
ABSTRACT We study the asset pricing implications of learning in an environment in which the true model of the world is a multivariate one, but agents update only over the class of simple univariate models. Thus, if a particular simple model does a poor job of forecasting over a period of time, it is discarded in favor of an alternative simple model. The theory yields a number of distinctive predictions for stock returns, generating forecastable variation in the magnitude of the value‐glamour return differential, in volatility, and in the skewness of returns. We validate several of these predictions empirically.