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Abstract
The research shows the use of a hybrid stock-picking technique that integrates neural network, fuzzy logic and various quantum techniques to perform on the fluctuations and the movement of the correlations in stock price changes, to increase the efficiency of stock trading while using a suitable model that added advantage for a fund manager with many clients, each with a different portfolio and with variable probability for risk. Many classical soft computing approaches have successfully applied in the prediction of stock price and showed a good performance. This research investigates the power of Quantum Genetic Algorithm in a neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used in prediction of stock market, identified using an optimization technique based on a double chains quantum genetic algorithm