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In this study, we present an algorithm to calculate the bidding fraction, while taking into account the level of risk (i.e., the maximum drawdown). The proposed algorithm is based on ensemble learning with a combination of bagging and subset resampling. Our assessment results obtained using the FF48 (i.e., Fama-French-48) dataset revealed that when the maximum drawdown was 5% and 10%, ensemble learning outperformed the conventional approach by 2% and 4%, respectively.<\/jats:p>","DOI":"10.3233\/jifs-179654","type":"journal-article","created":{"date-parts":[[2020,2,14]],"date-time":"2020-02-14T09:23:53Z","timestamp":1581672233000},"page":"5651-5659","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Embedded draw-down constraint using ensemble learning for stock trading"],"prefix":"10.1177","volume":"38","author":[{"given":"Mu-En","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Information and Finance Management, National Taipei University of Technology, Taipei, Taiwan"}]},{"given":"Sheng-Hao","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Information Engineering, National Central University, Taoyuan, Taiwan"}]},{"given":"Jia-Ching","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Information Engineering, National Central University, Taoyuan, Taiwan"}]}],"member":"179","published-online":{"date-parts":[[2020,2,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1956.1056803"},{"key":"e_1_3_2_3_2","unstructured":"MacLeanL.C. 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