A Comparative Study of Ensemble-based Forecasting Models for Stock Index Prediction

Stock prices as time series are, often, non-linear and non-stationary. This paper presents an ensemble forecasting model that integrates Empirical Mode Decomposition (EMD) and its variation Ensemble Empirical Mode Decomposition (EEMD) with Artificial Neural Network (ANN) for short-term forecasts of stock index. In first stage, the data is decomposed into a smaller set of Intrinsic Mode Functions (IMFs) and residuals using EMD and EEMD. In the next stage, IMFs and residue are taken as the inputs for the ANN model to train and predict the future stock price. The methodology was tested with weekly Nifty data for a period of 8 years. The results suggest that the ensemble forecast model using aggregation of the decomposed series performs better than traditional ANN and Support Vector Regression Models. Further, trading strategies based on EEMDANN models yielded better return on investments than Buy-and-Hold strategy.