Evolutionary Artificial Neural Network Optimisation in Financial Engineering

Authors:
Serge Hayward
Affiliations:
Ecole Supérieure de Commerce de Dijon, France
Venue:
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Year:
2004

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Abstract

Analytical examination of loss functions' families demonstrates that investors' utilitymaximisation is determined by their risk attitude. In computational settings, stock traders' fitness is assessed in response to a slow-step increase in the value of the risk aversion coefficient. The experiment rejects the claims that the accuracy of the forecast does not depend upon which error-criteria are used and none of them is related to the profitability of the forecast. Profitability of networks trained with L6 loss function appeared to be statistically significant and stable, although links between loss functions and accuracy of forecasts were less conclusive.