Evolutionary artificial neural network optimisation in financial engineering

Analytical examination of loss functions´ families demonstrates that investors´ utility maximisation 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.