This paper analyzes the forecast performance of historical S&P500 and Dow Jones
Industrial Average (DJIA) excess returns while using nonparametric functional data analysis (NP-FDA).
The empirical results show that the NP-FDA forecasting strategy outperforms not only the the
prevailing-mean model, but also the traditional univariate predictive regressions with standard predictors
used in the literature and, most cases, also combination approaches that use all predictors jointly.
In addition, our results clearly have important implications for investors, from an asset allocation
perspective, a mean-variance investor realizes substantial economic gains. Indeed, our results show that
NP-FDA is the only one individual model that can overcome the historical average forecasts for excess
returns in statistically and economically significant manners for both S&P500 and DJIA during the entire
period, NBER recession, and expansions periods.