Is sophistication a panacea? The learning capacity of random feature models for financial forecasting

This paper investigates the ability of random feature models to learn more complex patterns than the simple negative autocorrelation of financial returns. Theoretical insights highlight that the learning capacity of random features ridge regression is determined by the interplay between signal-to-noise ratio (SNR) level and regularization. We characterize the different learning regimes through the derivation of a particular threshold: the SNR frontier. Practically, our results permit to determine the minimum penalty level to be introduced into the estimation process in order to guard against a drop in the SNR during the implementation phase. We empirically apply this methodology to equity premium forecasting and use the output to build mean-variance efficient portfolios. The resulting portfolios yield substantial economic value over a portfolio construction scheme based on a short-term reversal strategy.