{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T06:47:56Z","timestamp":1751870876520,"version":"3.38.0"},"reference-count":19,"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:\/\/linproxy.fan.workers.dev:443\/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> We examine to what extent the GICS sector categorization of equity securities may be systematically reconstructed from historical quarterly firm fundamental data using gradient boosted tree classification. Model complexity and performance tradeoffs are examined and relative feature importance is described. Potential extensions are outlined including ideas to improve feature engineering, validating internal consistency and integrating additional data sources to further improve classification accuracy. <\/jats:p>","DOI":"10.3233\/af-200308","type":"journal-article","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T15:00:12Z","timestamp":1592924412000},"page":"91-99","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Sector categorization using gradient boosted trees trained on fundamental firm data"],"prefix":"10.1177","volume":"8","author":[{"given":"Ming","family":"Fang","sequence":"first","affiliation":[{"name":"Martin Tuchman School of Management, Newark, New Jersey, USA"}]},{"given":"Lilian","family":"Kuo","sequence":"additional","affiliation":[{"name":"NJIT Department of Computer Science, University Heights, Newark, NJ, USA"}]},{"given":"Frank","family":"Shih","sequence":"additional","affiliation":[{"name":"NJIT Department of Computer Science, University Heights, Newark, NJ, USA"}]},{"given":"Stephen","family":"Taylor","sequence":"additional","affiliation":[{"name":"Martin Tuchman School of Management, Newark, New Jersey, USA"},{"name":"Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University, Sokolovska, Prague, Czech Republic"}]}],"member":"179","published-online":{"date-parts":[[2020,6,22]]},"reference":[{"key":"ref001","doi-asserted-by":"publisher","DOI":"10.1016\/0378-4266(77)90017-6"},{"key":"ref002","unstructured":"BouchaudJ.P., PottersM., 2000. From Statistical Physics to Risk Management. Cambridge University Press."},{"key":"ref003","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316764"},{"key":"ref004","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(89)90095-0"},{"key":"ref005","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(93)90023-5"},{"issue":"1","key":"ref006","first-page":"105","volume":"58","author":"Gombola M.","year":"1983","journal-title":"The Accounting Review"},{"key":"ref007","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/4.3.389"},{"key":"ref008","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2011.10.001"},{"key":"ref009","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2012.09.022"},{"key":"ref010","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2016.06.094"},{"key":"ref011","unstructured":"KeG., 2017. A highly efficient gradient boosting decision tree. 31st Conference on Neural Information Processing Systems (NIPS 2017)."},{"key":"ref012","doi-asserted-by":"publisher","DOI":"10.3115\/1620754.1620794"},{"key":"ref013","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2006.08.043"},{"key":"ref014","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.2005.00813.x"},{"key":"ref015","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1765(91)90171-G"},{"key":"ref016","unstructured":"Standard & Poor\u2019s. Compustat (Global) Data Guide. McGraw-Hill Companies, 2002."},{"key":"ref017","unstructured":"Standard and Poors. 2018. Global Industry Classification Standard (GICS) Methdology. Standard and Poors Down Jones Indicies."},{"key":"ref018","doi-asserted-by":"publisher","DOI":"10.1016\/j.jebo.2010.01.004"},{"key":"ref019","doi-asserted-by":"publisher","DOI":"10.1007\/s11142-008-9083-2"}],"container-title":["Algorithmic Finance"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/journals.sagepub.com\/doi\/pdf\/10.3233\/AF-200308","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/journals.sagepub.com\/doi\/full-xml\/10.3233\/AF-200308","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/journals.sagepub.com\/doi\/pdf\/10.3233\/AF-200308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T22:38:02Z","timestamp":1741646282000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/journals.sagepub.com\/doi\/10.3233\/AF-200308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,8]]},"references-count":19,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2020,6,8]]}},"alternative-id":["10.3233\/AF-200308"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3233\/af-200308","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]]}}}