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

2019
Sergejs Paršutins, Yunus Emre Midilli

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.

Atslēgas vārdi
Design of experiment; genetic algorithms; neural networks; stock market
DOI
10.7250/itms-2019-0003
Hipersaite
https://doi.org/10.7250/itms-2019-0003

Paršutins, S., Midilli, Y. Review for Optimisation of Neural Networks with Genetic Algorithms and Design of Experiments in Stock Market Prediction. Information Technology and Management Science, 2019, Vol. 22, 15.-21. lpp. ISSN 2255-9086. e-ISSN 2255-9094. Pieejams: doi:10.7250/itms-2019-0003

Publikācijas valoda
English (en)