Neural networks, financial trading and the efficient markets hypothesis
ACSC '02 Proceedings of the twenty-fifth Australasian conference on Computer science - Volume 4
Evolutionary Artificial Neural Network Optimisation in Financial Engineering
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
A neural evolutionary approach to financial modeling
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Designing safe, profitable automated stock trading agents using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
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Automated Trading is the activity of buying and selling financial instruments for the purpose of gaining a profit, through the use of automated trading rules. This work presents an evolutionary approach for the design and optimization of artificial neural networks to the discovery of profitable automated trading rules. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns.