Turkish private pension fund size forecasting as an application of data analytics

Abstract

In this study univariate and multivariate models are used to forecast the net changes in total pension fund size of a private pension company in Turkey, using the daily data between November 2003 and November 2020. Univariate models include the naïve, autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models. Multivariate models include vector autoregression (VAR) and multiple linear regression models. Our findings suggest that multivariate model predictions outperform univariate model predictions. Univariate model predictions can be improved with walk forward approach. Increased lag size can help improve AR, MA, ARMA and VAR model predictions. Naïve model produces the weakest predictions.