Forecasting Stock Prices With Linear And Nonlinear Settings: A Comparison

Abstract

This paper is devoted to the application and comparison of linear (VAR) and nonlinear Multiple Adaptive Regression Splines (MARS) forecasting models, in estimating, evaluating, and selecting among linear and non-linear forecasting models for economic and financial time series.  We argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic.  Nonlinear models reduce nonlinearity and Gaussianity in the residuals of the linear models.  Linear models, however, demonstrate better forecasts than nonlinear.  However, much remains to be done.  Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.