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A network-based data mining approach to portfolio selection via weighted clique relaxations |
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| Authors: | Vladimir Boginski Sergiy Butenko Oleg Shirokikh Svyatoslav Trukhanov Jaime Gil Lafuente |
| Institution: | 1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA 2. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA 3. Microsoft Corporation, Redmond, WA, USA 4. Department of Business Economics and Organization, University of Barcelona, Barcelona, Spain |
| Abstract: | We introduce a new network-based data mining approach to selecting diversified portfolios by modeling the stock market as a network and utilizing combinatorial optimization techniques to find maximum-weight s-plexes in the obtained networks. The considered approach is based on the weighted market graph model, which is used for identifying clusters of stocks according to a correlation-based criterion. The proposed techniques provide a new framework for selecting profitable diversified portfolios, which is verified by computational experiments on historical data over the past decade. In addition, the proposed approach can be used as a complementary tool for narrowing down a set of “candidate” stocks for a diversified portfolio, which can potentially be analyzed using other known portfolio selection techniques. |
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