This paper documents the fact that the factors extracted from a large set of macroeconomic variables contain information that can be useful for predicting monthly US excess stock returns over the period 1975-2014. Factor-augmented predictive regression models improve upon benchmark models that include only valuation ratios and interest rate related variables, and possibly individual macro variables, as well as the historical average excess return. The improvements in out-of-sample forecast accuracy are significant, both statistically and economically. The factor-augmented predictive regressions have superior market timing abilities, such that a mean-variance investor would be willing to pay an annual performance fee of several hundreds of basis points to switch from the predictions offered by the benchmark models to those of the factor-augmented models. One important reason for the superior performance of the factor-augmented predictive regressions is the stability of their forecast accuracy, whereas the benchmark models suffer from a forecast breakdown during the 1990s.

Dynamic factor models, Model uncertainty, Return predictability, Variable selection
dx.doi.org/10.1016/j.ijforecast.2015.10.001, hdl.handle.net/1765/80120
Econometric Institute Reprint Series , ERIM Top-Core Articles
International Journal of Forecasting
Erasmus School of Economics

Çakmakli, C, & van Dijk, D.J.C. (2016). Getting the most out of macroeconomic information for predicting excess stock returns. International Journal of Forecasting, 32(3), 650–668. doi:10.1016/j.ijforecast.2015.10.001