{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:00:39Z","timestamp":1760709639610,"version":"3.41.0"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2018,6,22]],"date-time":"2018-06-22T00:00:00Z","timestamp":1529625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Academic Team Building Plan for Young Scholars from Wuhan University","award":["WHU2016012"],"award-info":[{"award-number":["WHU2016012"]}]},{"name":"Singapore Ministry of Education Academic Research Fund Tier 1","award":["14-C220-SMU-016"],"award-info":[{"award-number":["14-C220-SMU-016"]}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"crossref","award":["15ZR1408300"],"award-info":[{"award-number":["15ZR1408300"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11501204, 71401128 and U1711262"],"award-info":[{"award-number":["11501204, 71401128 and U1711262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Program of Science and Technology Innovation Action of Science and Technology Commission of Shanghai Municipality","award":["17511105204"],"award-info":[{"award-number":["17511105204"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>\n            Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. A popular and state-of-the-art family of strategies is to explore the reversion phenomenon through online learning algorithms and statistical prediction models. Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. However, such model selection is often unstable and may cause unnecessarily high variability in the final estimation, leading to poor prediction performance in real datasets and thus non-optimal portfolios. To overcome the drawbacks, in this article, we propose to exploit the reversion phenomenon by using combination forecasting estimators and design a novel online portfolio selection strategy, named\n            <jats:italic>Combination Forecasting Reversion<\/jats:italic>\n            \u00a0(CFR), which outputs optimal portfolios based on the improved reversion estimator. We further present two efficient CFR implementations based on online Newton step (ONS) and online gradient descent (OGD) algorithms, respectively, and theoretically analyze their regret bounds, which guarantee that the online CFR model performs as well as the best CFR model in hindsight. We evaluate the proposed algorithms on various real markets with extensive experiments. Empirical results show that CFR can effectively overcome the drawbacks of existing reversion strategies and achieve the state-of-the-art performance.\n          <\/jats:p>","DOI":"10.1145\/3200692","type":"journal-article","created":{"date-parts":[[2018,6,22]],"date-time":"2018-06-22T12:07:56Z","timestamp":1529669276000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Combination Forecasting Reversion Strategy for Online Portfolio Selection"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5267-9671","authenticated-orcid":false,"given":"Dingjiang","family":"Huang","sequence":"first","affiliation":[{"name":"East China Normal University 8 East China University of Science and Technology, Shanghai, China"}]},{"given":"Shunchang","family":"Yu","sequence":"additional","affiliation":[{"name":"East China University of Science and Technology, Shanghai, China"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University, Luojia Hill, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4584-3453","authenticated-orcid":false,"given":"Steven C. 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