In this chapter, I consider a design framework of a computational experiment in finance. The examination of statistics used for economic forecasts evaluation and profitability of investment decisions, based on those forecasts, reveals only weak relationships between them. The “degree of improvement over efficient prediction” combined with directional accuracy are proposed in an estimation technique, as an alternative to the conventional least squares. Rejecting a claim that the accuracy of the forecast does not depend upon which error-criteria are used, profitability of networks trained with L6 loss function appeared to be statistically significant and stable. The best economic performances are realized for a 1-year investment horizon with longer training not leading to enhanced accuracy. An improvement in profitability is achieved for models optimized with genetic algorithm. Computational intelligence is advocated for searching optimal relationships among economic agents’ risk attitude, loss function minimization in the learning process, and the profitability of trading decisions.