HYBRID DECOMPOSITION AND ENSEMBLE FRAMEWORK FOR STOCK PRICE FORECASTING: A COMPARATIVE STUDY

Abstract

In this study, a hybrid decomposition and ensemble framework incorporating Ensemble empirical mode decomposition (EEMD) and selected modeling methodologies are proposed for stock price forecasting. Under the framework, the original stock price series was first decomposed into several subseries including a number of intrinsic mode functions (IMFs) and a residue using EEMD technique. Then, extracted subseries was modeled to generate forecasts respectively. Finally, the forecasts of all extracted subseries were aggregated to produce an ensemble forecasts for the original stock price series. An extensive experiment was conducted to compare the feasibility and validity of the proposed hybrid framework employing different modeling methodologies, such as support vector machines (SVMs) (in the formulation of support vector regression (SVR), feed forward neural networks (FNN), and autoregressive integrated moving average (ARIMA). The real daily closing price series of Thirty Dow Jones industrial stocks from New York Stock Exchange (NYSE) was used for experimental evaluation. The results demonstrate that significant improvement can be achieved with the proposed hybrid decomposition and ensemble framework across all the three modeling methodologies, particularly, hybrid EEMD-based FNN modeling framework achieved the most significant improvement but hybrid EEMD-based SVMs modeling framework performed best in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE), and directional symmetry (DS).