{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T18:18:45Z","timestamp":1774289925676,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T00:00:00Z","timestamp":1655251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51875457"],"award-info":[{"award-number":["51875457"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022GY-028"],"award-info":[{"award-number":["2022GY-028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022GY-050"],"award-info":[{"award-number":["2022GY-050"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021JQ714"],"award-info":[{"award-number":["2021JQ714"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research Project of Shaanxi Province","award":["51875457"],"award-info":[{"award-number":["51875457"]}]},{"name":"Key Research Project of Shaanxi Province","award":["2022GY-028"],"award-info":[{"award-number":["2022GY-028"]}]},{"name":"Key Research Project of Shaanxi Province","award":["2022GY-050"],"award-info":[{"award-number":["2022GY-050"]}]},{"name":"Key Research Project of Shaanxi Province","award":["2021JQ714"],"award-info":[{"award-number":["2021JQ714"]}]},{"name":"Natural Science Foundation of Shaanxi Province","award":["51875457"],"award-info":[{"award-number":["51875457"]}]},{"name":"Natural Science Foundation of Shaanxi Province","award":["2022GY-028"],"award-info":[{"award-number":["2022GY-028"]}]},{"name":"Natural Science Foundation of Shaanxi Province","award":["2022GY-050"],"award-info":[{"award-number":["2022GY-050"]}]},{"name":"Natural Science Foundation of Shaanxi Province","award":["2021JQ714"],"award-info":[{"award-number":["2021JQ714"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation among subtasks and achieve a satisfactory learning effect by training all tasks simultaneously. With this motivation, we assume that the related tasks are close enough to share a common model whereas having their own independent models. Based on this hypothesis, we propose a multitask learning least squares support vector regression (MTL-LS-SVR) algorithm, and an extension, EMTL-LS-SVR. Theoretical analysis shows that these models can be converted to linear systems. A Krylov-Cholesky algorithm is introduced to determine the optimal solutions of the models. We tested the proposed models by applying them to forecasts of the Chinese stock market index trend and the stock prices of five stated-owned banks. The experimental results demonstrate their validity.<\/jats:p>","DOI":"10.3390\/axioms11060292","type":"journal-article","created":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T22:17:01Z","timestamp":1655331421000},"page":"292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7031-6946","authenticated-orcid":false,"given":"Heng-Chang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Qing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Control and Intelligent Process, Xi\u2019an 710121, China"}]},{"given":"Fei-Yan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Hong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108106","DOI":"10.1016\/j.asoc.2021.108106","article-title":"Stock market forecasting using a multi-task approach integrating long short-term memory and the random forest framework","volume":"114","author":"Park","year":"2022","journal-title":"Appl. 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