A network-based data mining approach to portfolio selection via weighted clique relaxations
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 g...
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| Veröffentlicht in: | Annals of operations research 2014-05, Vol.216 (1), p.23-34 |
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| container_title | Annals of operations research |
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| creator | Boginski, Vladimir Butenko, Sergiy Shirokikh, Oleg Trukhanov, Svyatoslav Gil Lafuente, Jaime |
| description | 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. |
| doi_str_mv | 10.1007/s10479-013-1395-3 |
| format | Article |
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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.</description><identifier>ISSN: 0254-5330</identifier><identifier>EISSN: 1572-9338</identifier><identifier>DOI: 10.1007/s10479-013-1395-3</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Algorithms ; Business and Management ; Combinatorial analysis ; Combinatorics ; Data mining ; Markets ; Methods ; Networks ; Operations research ; Operations Research/Decision Theory ; Optimization ; Optimization techniques ; Portfolio management ; Raw materials ; Securities markets ; Stock exchanges ; Studies ; Systems engineering ; Theory of Computation ; Volatility</subject><ispartof>Annals of operations research, 2014-05, Vol.216 (1), p.23-34</ispartof><rights>Springer Science+Business Media New York 2013</rights><rights>COPYRIGHT 2014 Springer</rights><rights>Springer Science+Business Media New York 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-eba16bba1f6117faa2568210feca01074a1a8dcc3300c0f0024dd5dcdf55953d3</citedby><cites>FETCH-LOGICAL-c453t-eba16bba1f6117faa2568210feca01074a1a8dcc3300c0f0024dd5dcdf55953d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10479-013-1395-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10479-013-1395-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Boginski, Vladimir</creatorcontrib><creatorcontrib>Butenko, Sergiy</creatorcontrib><creatorcontrib>Shirokikh, Oleg</creatorcontrib><creatorcontrib>Trukhanov, Svyatoslav</creatorcontrib><creatorcontrib>Gil Lafuente, Jaime</creatorcontrib><title>A network-based data mining approach to portfolio selection via weighted clique relaxations</title><title>Annals of operations research</title><addtitle>Ann Oper Res</addtitle><description>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.</description><subject>Algorithms</subject><subject>Business and Management</subject><subject>Combinatorial analysis</subject><subject>Combinatorics</subject><subject>Data mining</subject><subject>Markets</subject><subject>Methods</subject><subject>Networks</subject><subject>Operations research</subject><subject>Operations Research/Decision Theory</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>Portfolio management</subject><subject>Raw materials</subject><subject>Securities markets</subject><subject>Stock exchanges</subject><subject>Studies</subject><subject>Systems engineering</subject><subject>Theory of 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s
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weighted market graph
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| source | Business Source Complete; SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Business and Management Combinatorial analysis Combinatorics Data mining Markets Methods Networks Operations research Operations Research/Decision Theory Optimization Optimization techniques Portfolio management Raw materials Securities markets Stock exchanges Studies Systems engineering Theory of Computation Volatility |
| title | A network-based data mining approach to portfolio selection via weighted clique relaxations |
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