Textbook models assume that investors try to insure against bad states of the world associated with specific risk factors when investing. This is a testable assumption and we develop a survey framework for doing so. Our framework can be applied to any risk factor. We demonstrate the approach using consumption growth, which makes our results applicable to most modern asset‐pricing models. Participants respond to changes in the mean and volatility of stock returns consistent with textbook models, but we find no evidence that they view an asset's correlation with consumption growth as relevant to investment decisions.

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns as candidate predictors. The LASSO increases both out‐of‐sample fit and forecast‐implied Sharpe ratios. This out‐of‐sample success comes from identifying predictors that are unexpected, short‐lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.