Trading support system for portfolio construction using wisdom of artificial crowds and evolutionary computation

Abstract

Effective portfolio management requires vast quantities of information and accurate forecasts to make decisions that generate a profitable strategy. In this study, we propose a framework that extracts useful information from Virtual Experts, which are generated using Strongly-Typed Genetic Programming. Specifically, we created a Virtual Expert pool that provides different recommendations on selling or purchasing of a particular stock, and then applied a Wisdom of Artificial Crowds post-processing algorithm to decide which action to take. We call this framework Community of Virtual Expert Investors, and it is evaluated on different metrics of risk. Results show that this approach manages to outperform a Buy and Hold strategy in a long-term scenario, both in return and in Conditional Sharpe Ratio measures. To test the robustness of these results, a bootstrapping test was performed, in which the general findings of the results were maintained.