Hybridization of ARIMA with Learning Models for Forecasting of Stock Market Time Series

This paper aims to highlight in a relevant way the interest of hybrid models (coupling of ARIMA processes and machine learning models) for economic or financial agents. These models are likely to allow better consideration of certain stylized facts (not necessarily taken into account by often-used models such as ARIMA-GARCH), observed in the analysis of financial time series. Most hybrid models assume that the random perturbation of the chosen ARIMA process follows a normal distribution. Thus, the literature on hybrid models remains on this Gaussian framework and it would be necessary to consider a non-Gaussian and much more realistic framework. We consider hybrid models with the assumption that the random disturbance process follows a Student's distribution (denoted by ARIMA T), allowing us to take into account the leptokurticity often observed when analyzing financial time series returns. Under certain assumptions, the empirical results show the power of the hybrid models considered.