Hybrid of neuro-fuzzy inference system and quantum genetic algorithm for prediction in stock market

Abstract

Many classical soft computing approaches have successfully applied in the prediction of stock price and showed a good performance. This paper 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. In this paper the ANFIS controller and the stock market process model inputs are chosen based on a comparative study of fifty different combinations of past stock prices to determine the stock market process model inputs that return the best stock trend prediction for the next day in terms of the minimum Mean Square Error (MSE). The proposed model are tested with  actual financial  data  and  show  the weak form of compared approach by demonstrating much improved and better predictions by finding optimal value for optimization variable in ANFIS using a double chains quantum genetic algorithm.

Keywords: Fuzzy logic, neural network, stock market prediction, neuro-fuzzy, quantum genetic algorithm