We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the quality of their return forecasts. In the low signal-to-noise environment of a one month horizon, we find that it is preferable to rely on a theory-based approach instead of engaging...

We consider a canonical asset pricing model, where agents with quadratic preferences are allowed to retrade a limited set of securities over multiple periods, after which these securities expire, and agents consume their liquidation values. A key assumption in this model is that agents have...

We provide empirical evidence that CAPM-betas positively predict asset returns when market returns are predicted to be high, which occurs about every other month. Consequently, the product of beta and the predicted market return (CAPM) predicts asset returns by combining the out-of-sample...

This paper studies the time-series predictability of currency carry trades, constructed by selecting currencies to be bought or sold against the U.S. dollar, based on forward discounts. Changes in a commodity index, currency volatility and, to a lesser extent, a measure of liquidity predict...

The purpose of this paper is to examine whether earnings quality contributes to the book-to- market's predictive power in the cross section of stock returns. Earnings quality is embedded in the value-growth effect given that retained earnings is a key part of the book value of equity. Earnings...

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES)...