  We propose a discrete-time econometric model that combines autoregressive
filters with factor regressions to predict stock returns for portfolio
optimisation purposes. In particular, we test both robust linear regressions
and general additive models on two different investment universes composed of
the Dow Jones Industrial Average and the Standard & Poor's 500 indexes, and we
compare the out-of-sample performances of mean-CVaR optimal portfolios over a
horizon of six years. The results show a substantial improvement in portfolio
performances when the factor model is estimated with general additive models.
