Full-scale optimization (FSO) is a utility maximization approach to portfolio choice problems that has theoretical appeal but that suffers from computational burden in large scale problems. We apply the heuristic technique differential evolution to solve FSO-type asset selection problems of 97 assets under complex utility functions rendering rough utility search surfaces. We show that this large scale problem is computationally feasible and that the solutions retrieved are stable optima. Furthermore, the study constitutes the first FSO application to stock selection. The results indicate that when investors are loss averse, FSO improves stock selection performance compared to mean-variance portfolios averse. This finding widens the scope of applicability of FSO, but it is also stressed that out-of-sample success will always be dependent on the forecasting ability of the input return distributions
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