A network-based data mining approach to portfolio selection via weighted clique relaxations
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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. © 2013 Springer Science+Business Media New York.
author list (cited authors)
Boginski, V., Butenko, S., Shirokikh, O., Trukhanov, S., & Gil Lafuente, J.