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

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. 2013 Springer Science+Business Media New York.

authors

• Butenko, Sergiy

published proceedings

• ANNALS OF OPERATIONS RESEARCH

author list (cited authors)

• Boginski, V., Butenko, S., Shirokikh, O., Trukhanov, S., & Gil Lafuente, J.

citation count

• 27

complete list of authors

• Boginski, Vladimir||Butenko, Sergiy||Shirokikh, Oleg||Trukhanov, Svyatoslav||Gil Lafuente, Jaime

publication date

• May 2014

publisher

• Springer Nature  Publisher

published in

• Annals of Operations Research  Journal

keywords

• Clique
• Clique Relaxations
• Cohesive Network Clusters
• Diversified Portfolios
• Market Graph
• Maximum-weight S-plex
• Network-based Data Mining

Digital Object Identifier (DOI)

• 10.1007/s10479-013-1395-3

start page

• 23

end page

• 34

volume

• 216

issue

• 1

URL

• http://dx.doi.org/10.1007/s10479-013-1395-3