A Quantum-Inspired Intelligent Hybrid Method for Stock Market Forecasting

This work introduces a Quantum-Inspired Intelligent Hybrid (QIIH) method for stock market forecasting. It performs a quantum-inspired evolutionary search for the minimum necessary dimension (time lags) embedded in the problem for determining the characteristic phase space that generates the financial time series phenomenon. The proposed QIIH method consists of a quantum-inspired intelligent hybrid model composed of an Artificial Neural Network (ANN) with a Modified Quantum-Inspired Evolutionary Algorithm (MQIEA), which is able to evolve the complete network architecture and parameters (pruning process), its training algorithm (used to further improve the ANN parameters supplied by the MQIEA) and the particular time lags capable of a fine tuned time series characterization. Initially, the proposed QIIH method chooses the most fitted forecasting model, thus it performs a behavioral statistical test in the attempt to adjust forecasting time phase distortions that appear in financial time series. Furthermore, an experimental analysis is conducted with the proposed QIIH method using three real world stock market time series, and the achieved results are discussed and compared, according to a group of relevant performance metrics, to results found with MultiLayer Perceptron (MLP) networks and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) method.