Review for Optimisation of Neural Networks With Genetic Algorithms and Design of Experiments in Stock Market Prediction

Abstract

Neural networks are commonly used methods in stock market predictions. From the earlier studies in the literature, the requirement of optimising neural networks has been emphasised to increase the profitability, accuracy and performance of neural networks in exchange rate prediction. The paper proposes a literature review of two techniques to optimise neural networks in stock market predictions: genetic algorithms and design of experiments. These two methods have been discussed in three approaches to optimise the following aspects of neural networks: variables, input layer and hyper-parameters.