Evolutionary regressor selection in ARIMA model for stock price time series forecasting

Abstract

Stock price prediction over time is a problem of practical concern in economics and of scientific interest in financial time series forecasting. The matter also expands toward detecting the variables that play an important role in its behaviour. The current study thus appoints an ARIMA model with regressors to predict the daily return of ten companies enlisted in the Romanian stock market on the base of nine exogenous predictors. In order to additionally outline the most informative attributes for the prediction, feature selection is also considered and performed by means of genetic algorithms. The experimental results justify the benefits of the model with the evolutionary selector.