A network-based data mining approach to portfolio selection via weighted clique relaxations

Vladimir Boginski (), Sergiy Butenko (), Oleg Shirokikh (), Svyatoslav Trukhanov () and Jaime Gil Lafuente ()

Annals of Operations Research, 2014, vol. 216, issue 1, 23-34

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. Copyright Springer Science+Business Media New York 2014

Keywords: Network-based data mining; Market graph; Diversified portfolios; Cohesive network clusters; Clique; Clique relaxations; Maximum-weight s-plex (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (15)

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DOI: 10.1007/s10479-013-1395-3

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