Intraday stock price forecasting based on variational mode decomposition

•A new multiresolution technique is applied to the problem of intraday stock price forecasting.•Variational mode decomposition is employed to analyze stock prices.•The predictive model is based on a neural network optimized by particle swarm intelligence and trained with extracted variational modes.•Proposed model outperforms baseline model.

This paper presents a hybrid predictive model for forecasting intraday stock prices. The proposed model hybridizes the variational mode decomposition (VMD) which is a new multiresolution technique with backpropagation neural network (BPNN). The VMD is used to decompose price series into a sum of variational modes (VM). The extracted VM are used to train BPNN. Besides, particle swarm optimization (PSO) is employed for BPNN initial weights optimization. Experimental results from a set of six stocks show the superiority of the hybrid VMD–PSO–BPNN predictive model over the baseline predictive model which is a PSO–BPNN model trained with past prices.