Considering the effect of economic agents’ preferences on their actions, the relationships between conventional summary statistics and forecast profits are investigated. An analytical examination of loss function families demonstrates that investors’ utility maximisation is determined by their risk attitudes. 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 that none of them is related to the profitability of the forecast. The profitability of networks trained with L₆ loss function appeared to be statistically significant and stable, although links between the loss functions and the accuracy of forecasts were less conclusive.