{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T06:16:50Z","timestamp":1778221010932,"version":"3.51.4"},"reference-count":81,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2013,3,1]],"date-time":"2013-03-01T00:00:00Z","timestamp":1362096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["RG33\/11"],"award-info":[{"award-number":["RG33\/11"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2013,3]]},"abstract":"<jats:p>\n            Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named\n            <jats:italic>Confidence Weighted Mean Reversion<\/jats:italic>\n            (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR models the portfolio vector as a Gaussian distribution, and sequentially updates the distribution by following the mean reversion trading principle. CWMR\u2019s closed-form updates clearly reflect the mean reversion trading idea. We also present several variants of CWMR algorithms, including a CWMR mixture algorithm that is theoretical universal. Empirically, CWMR strategy is able to effectively exploit the power of mean reversion for online portfolio selection. Extensive experiments on various real markets show that the proposed strategy is superior to the state-of-the-art techniques. The experimental testbed including source codes and data sets is available online.\n          <\/jats:p>","DOI":"10.1145\/2435209.2435213","type":"journal-article","created":{"date-parts":[[2013,3,19]],"date-time":"2013-03-19T13:34:23Z","timestamp":1363700063000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":104,"title":["Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection"],"prefix":"10.1145","volume":"7","author":[{"given":"Bin","family":"Li","sequence":"first","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven C. H.","family":"Hoi","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peilin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivekanand","family":"Gopalkrishnan","sequence":"additional","affiliation":[{"name":"Deloitte Analytics Institute (Asia)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2013,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Agarwal A. and Hazan E. 2005. New algorithms for repeated play and universal portfolio management. Tech. rep. Princeton University.  Agarwal A. and Hazan E. 2005. New algorithms for repeated play and universal portfolio management. Tech. rep. Princeton University."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143846"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539702405619"},{"key":"e_1_2_1_4_1","unstructured":"Aldridge I. 2010. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. Wiley Hoboken N.J.  Aldridge I. 2010. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems . Wiley Hoboken N.J."},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Belentepe C. Y. 2005. A statistical view of universal portfolios. Ph.D. thesis University of Pennsylvania.  Belentepe C. Y. 2005. A statistical view of universal portfolios. Ph.D. thesis University of Pennsylvania.","DOI":"10.1109\/ISIT.2005.1523400"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.2307\/1909829"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007530728748"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1985.tb05004.x"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1987.tb04569.x"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 4th Latin American Symposium on Theoretical Informatics. Springer-Verlag","author":"Borodin A."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622467.1622484"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Boyd S. and Vandenberghe L. 2004. Convex Optimization. Cambridge University Press Cambridge UK.   Boyd S. and Vandenberghe L. 2004. Convex Optimization . Cambridge University Press Cambridge UK.","DOI":"10.1017\/CBO9780511804441"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability 1, 65--78","author":"Breiman L.","year":"1961"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.820556"},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Cesa-Bianchi N. and Lugosi G. 2006. Prediction Learning and Games. Cambridge University Press Cambridge UK.   Cesa-Bianchi N. and Lugosi G. 2006. Prediction Learning and Games . Cambridge University Press Cambridge UK.","DOI":"10.1017\/CBO9780511546921"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2004.833339"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1108\/03074350310768490"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.3905\/jpm.1993.409440"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the Annual IEEE International Symposium on Information Theory.","author":"Cover T."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9965.1991.tb00002.x"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/0196-8858(86)90029-1"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.485708"},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Cover T. M. and Thomas J. A. 1991. Elements of Information Theory. Wiley New York.   Cover T. M. and Thomas J. A. 1991. Elements of Information Theory . Wiley New York.","DOI":"10.1002\/0471200611"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1162\/jmlr.2003.3.4-5.951"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248566"},{"key":"e_1_2_1_26_1","unstructured":"Crammer K. Dredze M. and Pereira F. 2008. Exact convex confidence-weighted learning. In Advances in Neural Information Processing Systems.  Crammer K. Dredze M. and Pereira F. 2008. Exact convex confidence-weighted learning. In Advances in Neural Information Processing Systems."},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language. Association for Computational Linguistics","author":"Crammer K."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9965.00016"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020588"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390190"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390191"},{"key":"e_1_2_1_32_1","unstructured":"Elton E. J. Gruber M. J. Brown S. J. and Goetzmann W. N. 1995. Modern Portfolio Theory and Investment Analysis. Wiley New York.  Elton E. J. Gruber M. J. Brown S. J. and Goetzmann W. N. 1995. Modern Portfolio Theory and Investment Analysis. Wiley New York."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007662407062"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1019271201970"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-1889(02)00053-2"},{"key":"e_1_2_1_36_1","first-page":"213","article-title":"A new approximate maximal margin classification algorithm","volume":"2","author":"Gentile C.","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_37_1","unstructured":"Golub G. H. and Van Loan C. F. 1996. Matrix Computations. Johns Hopkins University Press Baltimore MD.  Golub G. H. and Van Loan C. F. 1996. Matrix Computations . Johns Hopkins University Press Baltimore MD."},{"key":"e_1_2_1_38_1","unstructured":"Grinold R. and Kahn R. 1999. Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk. McGraw-Hill New York.  Grinold R. and Kahn R. 1999. Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk . McGraw-Hill New York."},{"key":"e_1_2_1_39_1","volume-title":"Learning Theory: Methods, Models and Applications","author":"Gy\u00f6rfi L.","year":"2003"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87987-9_13"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9965.2006.00274.x"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219024907004251"},{"key":"e_1_2_1_43_1","first-page":"145","article-title":"Nonparametric nearest neighbor based empirical portfolio selection strategies","volume":"26","author":"Gy\u00f6rfi L.","year":"2008","journal-title":"Statist"},{"key":"e_1_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Gy\u00f6rfi L. Ottucsak G. and Walk H. 2012. Machine Learning for Financial Engineering. World Scientific Singapore.  Gy\u00f6rfi L. Ottucsak G. and Walk H. 2012. Machine Learning for Financial Engineering . World Scientific Singapore.","DOI":"10.1142\/p818"},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of Advances in Neural Information Processing Systems.","author":"Hazan E."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553425"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007301011561"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9965.00058"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1990.tb05110.x"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1991.tb04624.x"},{"key":"e_1_2_1_51_1","first-page":"423","article-title":"Efficient algorithms for universal portfolios","volume":"3","author":"Kalai A.","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1956.tb03809.x"},{"key":"e_1_2_1_53_1","unstructured":"Kimoto T. Asakawa K. Yoda M. and Takeoka M. 1993. Stock market prediction system with modular neural networks. Neural Netw. Finance Invest. 343--357.  Kimoto T. Asakawa K. Yoda M. and Takeoka M. 1993. Stock market prediction system with modular neural networks. Neural Netw. Finance Invest. 343--357."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.830991"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1086\/258157"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488508005364"},{"key":"e_1_2_1_58_1","volume-title":"Proceedings of the 29th Annual International Conference on Machine Learning. ACM","author":"Li B."},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961193"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2435209.2435213"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-012-5281-z"},{"key":"e_1_2_1_62_1","volume-title":"Proceedings of Advances in Neural Information Processing Systems.","author":"Li Y."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/3.2.175"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2009.02.001"},{"key":"e_1_2_1_65_1","first-page":"99","article-title":"Maximum drawdown","volume":"10","author":"Magdon-Ismail M.","year":"2004","journal-title":"Risk Mag."},{"key":"e_1_2_1_66_1","first-page":"77","article-title":"Portfolio selection","volume":"7","author":"Markowitz H.","year":"1952","journal-title":"J. Finance"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/238061.238161"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1524\/stnd.2007.25.1.63"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(88)90021-9"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0042519"},{"key":"e_1_2_1_71_1","first-page":"277","article-title":"A simplified model for portfolio analysis. Manage","volume":"9","author":"Sharpe W. F.","year":"1963","journal-title":"Sci."},{"key":"e_1_2_1_72_1","first-page":"425","article-title":"Capital asset prices: A theory of market equilibrium under conditions of risk","volume":"19","author":"Sharpe W. F.","year":"1964","journal-title":"J. Finance"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.3905\/jpm.1994.409501"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065797000434"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0305-0483(01)00026-3"},{"key":"e_1_2_1_76_1","volume-title":"Proceedings of Business and Economics Section of the American Statistical Association.","author":"Thorp E. O.","year":"1971"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-923(03)00087-3"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279947"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.5555\/92571.92672"},{"key":"e_1_2_1_80_1","volume-title":"Proceedings of the 29th Annual International Conference on Machine Learning. ACM","author":"Wang J."},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2021051"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2435209.2435213","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2435209.2435213","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T08:35:40Z","timestamp":1750235740000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2435209.2435213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,3]]},"references-count":81,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2013,3]]}},"alternative-id":["10.1145\/2435209.2435213"],"URL":"https:\/\/doi.org\/10.1145\/2435209.2435213","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,3]]},"assertion":[{"value":"2011-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2012-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2013-03-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}