{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T12:09:59Z","timestamp":1771502999329,"version":"3.50.1"},"reference-count":22,"publisher":"SAGE Publications","issue":"1-2","license":[{"start":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T00:00:00Z","timestamp":1686009600000},"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":[[2023,9,16]]},"abstract":"<jats:p> We present an algorithmic trading strategy based upon a graph version of the dynamic mode decomposition (DMD) model. Unlike the traditional DMD model which tries to characterize a stock\u2019s dynamics based on all other stocks in a universe, the proposed model characterizes a stock\u2019s dynamics based only on stocks that are deemed relevant to the stock in question. The relevance between each pair of stocks in a universe is represented as a directed graph and is updated dynamically. The incorporation of a graph model into DMD effects a model reduction that avoids overfitting of data and improves the quality of the trend predictions. We show that, in a practical setting, the precision and recall rate of the proposed model are significantly better than the traditional DMD and the benchmarks. The proposed model yields portfolios that have more stable returns in most of the universes we backtested. <\/jats:p>","DOI":"10.3233\/af-220432","type":"journal-article","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T17:11:18Z","timestamp":1686071478000},"page":"39-51","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph embedded dynamic mode decomposition for stock price prediction"],"prefix":"10.1177","volume":"10","author":[{"given":"Andy","family":"Yip","sequence":"first","affiliation":[{"name":"Radiant First Research Limited, Hong Kong"}]},{"given":"William","family":"Ng","sequence":"additional","affiliation":[{"name":"Koi Investment Partners International, Hong Kong"}]},{"given":"Ka-Wai","family":"Siu","sequence":"additional","affiliation":[{"name":"Koi Investment Partners International, Hong Kong"}]},{"given":"Albert C.","family":"Cheung","sequence":"additional","affiliation":[{"name":"Radiant First Research Limited, Hong Kong"},{"name":"Koi Investment Partners International, Hong Kong"}]},{"given":"Michael K.","family":"Ng","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Department of Mathematics, The University of Hong Kong, Hong Kong"}]}],"member":"179","published-online":{"date-parts":[[2023,6,6]]},"reference":[{"key":"ref001","doi-asserted-by":"publisher","DOI":"10.1038\/nrn2575"},{"key":"ref002","doi-asserted-by":"publisher","DOI":"10.1086\/521848"},{"key":"ref003","first-page":"187","volume":"13","author":"Ching W","year":"2002","journal-title":"IMA J Manage Math"},{"key":"ref004","first-page":"23","author":"Ching W","year":"2003","journal-title":"International Workshop on Bioinformatics: PAKDD"},{"key":"ref005","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2015.01.003"},{"key":"ref006","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2016.06.046"},{"key":"ref007","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2016.7552877"},{"key":"ref008","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2015.12.059"},{"key":"ref009","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"ref010","doi-asserted-by":"publisher","DOI":"10.1063\/1.4863670"},{"key":"ref011","author":"Kuttichira D","year":"2017","journal-title":"International Conference on Advances in Computing, Communications and Informatics (ICACCI)"},{"key":"ref012","unstructured":"Kutz, J, Brunton, S, Brunton, B, Proctor, J. 2000 Dynamic mode decomposition: Data-driven modeling of complex systems. 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