{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T04:23:49Z","timestamp":1741753429092,"version":"3.38.0"},"reference-count":21,"publisher":"SAGE Publications","issue":"3-4","license":[{"start":{"date-parts":[[2020,6,8]],"date-time":"2020-06-08T00:00:00Z","timestamp":1591574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Algorithmic Finance"],"published-print":{"date-parts":[[2020,6,8]]},"abstract":"<jats:p> With the recent rise of Machine Learning (ML) as a candidate to partially replace classic Financial Mathematics (FM) methodologies, we investigate the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two assets that are intertwined. <\/jats:p><jats:p> In the Financial Mathematics approach we model the asset prices not via the common approaches used in pairs trading such as a high correlation or cointegration, but with the cointelation model in Mahdavi-Damghani (2013) that aims to reconcile both short-term risk and long-term equilibrium. We maximize the overall P&amp;L with Financial Mathematics approach that dynamically switches between a mean-variance optimal strategy and a power utility maximizing strategy. We use a stochastic control formulation of the problem of power utility maximization and solve numerically the resulting HJB equation with the Deep Galerkin method introduced in Sirignano and Spiliopoulos (2018) . <\/jats:p><jats:p> We turn to Machine Learning for the same P&amp;L maximization problem and use clustering analysis to devise bands, combined with in-band optimization. Although this approach is model agnostic, results obtained with data simulated from the same cointelation model gives a slight competitive advantage to the ML over the FM methodology <jats:sup>1<\/jats:sup> . <\/jats:p>","DOI":"10.3233\/af-200311","type":"journal-article","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T14:48:01Z","timestamp":1603205281000},"page":"101-125","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Portfolio optimization for cointelated pairs: SDEs vs Machine learning"],"prefix":"10.1177","volume":"8","author":[{"given":"Babak","family":"Mahdavi-Damghani","sequence":"first","affiliation":[{"name":"Oxford-Man Institute of Quantitative Finance, Oxford, UK"}]},{"given":"Konul","family":"Mustafayeva","sequence":"additional","affiliation":[{"name":"Department of Mathematics, King\u2019s College London, London, UK"}]},{"given":"Cristin","family":"Buescu","sequence":"additional","affiliation":[{"name":"Department of Mathematics, King\u2019s College London, London, UK"}]},{"given":"Stephen","family":"Roberts","sequence":"additional","affiliation":[{"name":"Oxford-Man Institute of Quantitative Finance, Oxford, UK"}]}],"member":"179","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"ref001","unstructured":"Al-Aradi,A., Correia,A., Naiff,D., Jardim,G., Sapito,Y., 2018. 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Investments. 4th edition, Irwin\/McGraw-Hill, Chicago."},{"key":"ref007","doi-asserted-by":"publisher","DOI":"10.2307\/1911242"},{"key":"ref008","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198283393.001.0001"},{"key":"ref009","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2017.04.026"},{"key":"ref010","doi-asserted-by":"publisher","DOI":"10.1016\/0022-0531(79)90043-7"},{"key":"ref011","doi-asserted-by":"publisher","DOI":"10.1137\/S0363012900377791"},{"key":"ref012","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9965.00100"},{"key":"ref013","first-page":"0","volume":"67","author":"Mahdavi-Damghani, B.","year":"2013","journal-title":"Wilmott Magazine"},{"key":"ref014","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3039179"},{"key":"ref015","doi-asserted-by":"publisher","DOI":"10.1002\/wilm.10167"},{"key":"ref016","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2008.4586628"},{"key":"ref017","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.08.029"},{"key":"ref018","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/166\/1\/012003"},{"key":"ref019","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRev.36.823"},{"key":"ref020","unstructured":"VidyamurthyG., 2004. Pairs Trading: Quantitative Methods and Analysis, John Wiley Sons, Inc., Hoboken, New Jersey."},{"key":"ref021","unstructured":"Wilmott,P., 2007. Paul Wilmott Introduces Quantitative Finance, John Wiley Sons Ltd., Chichester, West Sussex."}],"container-title":["Algorithmic Finance"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/AF-200311","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/AF-200311","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/AF-200311","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T07:14:19Z","timestamp":1741677259000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/AF-200311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,8]]},"references-count":21,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2020,6,8]]}},"alternative-id":["10.3233\/AF-200311"],"URL":"https:\/\/doi.org\/10.3233\/af-200311","relation":{},"ISSN":["2158-5571","2157-6203"],"issn-type":[{"type":"print","value":"2158-5571"},{"type":"electronic","value":"2157-6203"}],"subject":[],"published":{"date-parts":[[2020,6,8]]}}}