Trading support system for portfolio construction using wisdom of artificial crowds and evolutionary computation
•Virtual Experts are used to develop crowd wisdom to outperform market indexes.•Wisdom of Crowd is developed in an artificial framework for portfolio selection.•Artificial Crowds are developed for Trading Support Systems.•Community of Virtual Experts improve stock investment decisions. Effective por...
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| Published in: | Expert systems with applications Vol. 177; p. 114943 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01-09-2021
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| Abstract | •Virtual Experts are used to develop crowd wisdom to outperform market indexes.•Wisdom of Crowd is developed in an artificial framework for portfolio selection.•Artificial Crowds are developed for Trading Support Systems.•Community of Virtual Experts improve stock investment decisions.
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. |
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| AbstractList | 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. •Virtual Experts are used to develop crowd wisdom to outperform market indexes.•Wisdom of Crowd is developed in an artificial framework for portfolio selection.•Artificial Crowds are developed for Trading Support Systems.•Community of Virtual Experts improve stock investment decisions. 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. |
| ArticleNumber | 114943 |
| Author | Dheeriya, Prakash Kristjanpoller, Werner Michell, Kevin Minutolo, Marcel C. |
| Author_xml | – sequence: 1 givenname: Werner orcidid: 0000-0002-5878-072X surname: Kristjanpoller fullname: Kristjanpoller, Werner email: werner.kristjanpoller@usm.cl organization: Departamento de Industrias, Universidad Técnica Federico Santa María., Av. España 1680, Valparaíso, Chile – sequence: 2 givenname: Kevin orcidid: 0000-0002-9047-8213 surname: Michell fullname: Michell, Kevin organization: Departamento de Industrias, Universidad Técnica Federico Santa María., Av. España 1680, Valparaíso, Chile – sequence: 3 givenname: Marcel C. surname: Minutolo fullname: Minutolo, Marcel C. email: minutolo@rmu.edu organization: Robert Morris University, 6001 University Blvd, Moon Township, PA 15108, United States – sequence: 4 givenname: Prakash surname: Dheeriya fullname: Dheeriya, Prakash email: pdheeriya@csudh.edu organization: California State University Dominguez Hills, 1000 E Victoria St, Carson, CA 90747, United States |
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| Keywords | Trading support system Evolutionary Computation Wisdom of artificial crowds Portfolio selection Virtual community |
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| SubjectTerms | Evolutionary algorithms Evolutionary Computation Genetic algorithms Portfolio management Portfolio selection Post-processing Strategy Support systems Trading support system Virtual community Wisdom of artificial crowds |
| Title | Trading support system for portfolio construction using wisdom of artificial crowds and evolutionary computation |
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