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

作者：Boginski Vladimir^*; Butenko Sergiy; Shirokikh Oleg; Trukhanov Svyatoslav; Gil Lafuente Jaime

来源：Annals of Operations Research, 2014, 216(1): 23-34.

DOI：10.1007/s10479-013-1395-3

• Network-based data mining
• Market graph
• Diversified portfolios
• Cohesive network clusters
• Clique
• Clique relaxations
• Maximum-weight s-plex

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.

• 出版日期2014-5