Composite Absolute Value and Sign Forecasts

Abstract

This paper introduces composite absolute value and sign (CAVS) forecasts, a nonlinear framework that combines forecasts of the sign and absolute value of a time series into conditional mean forecasts. In contrast to linear models, the proposed framework allows different predictors to separately impact the sign and absolute value of the target series. Among other results, I show that the conditional mean can be accurately approximated by the product of mean squared error optimal sign and absolute value forecasts. An empirical application using the FRED-MD dataset shows that CAVS forecasts substantially outperform linear forecasts for series that exhibit persistent volatility dynamics, such as output and interest rates.

Keywords: Forecasting, Directional Predictability, Machine Learning

JEL Classification: C53, C22, C52, C58