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Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies

Stock trend prediction is a challenging task due to the market’s noise, and machine learning techniques have recently been successful in coping with this challenge. In this research, we create a novel framework for stock prediction, Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-spe...

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Autor principal: Dong, Zhengyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386270/
https://www.ncbi.nlm.nih.gov/pubmed/30794608
http://dx.doi.org/10.1371/journal.pone.0212487
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author Dong, Zhengyang
author_facet Dong, Zhengyang
author_sort Dong, Zhengyang
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description Stock trend prediction is a challenging task due to the market’s noise, and machine learning techniques have recently been successful in coping with this challenge. In this research, we create a novel framework for stock prediction, Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-specific areas based on the companies of interest, diversifies the feature set by creating different “advisors” that each handles a different area, follows an effective model ensemble procedure for each advisor, and combines the advisors together in a second-level ensemble through an online update strategy we developed. dynABE is able to adapt to price pattern changes of the market during the active trading period robustly, without needing to retrain the entire model. We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12% and an annualized absolute return of 359.55% with zero maximum drawdown. dynABE also consistently outperforms the baseline models of support vector machine, neural network, and random forest in all case studies.
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spelling pubmed-63862702019-03-09 Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies Dong, Zhengyang PLoS One Research Article Stock trend prediction is a challenging task due to the market’s noise, and machine learning techniques have recently been successful in coping with this challenge. In this research, we create a novel framework for stock prediction, Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-specific areas based on the companies of interest, diversifies the feature set by creating different “advisors” that each handles a different area, follows an effective model ensemble procedure for each advisor, and combines the advisors together in a second-level ensemble through an online update strategy we developed. dynABE is able to adapt to price pattern changes of the market during the active trading period robustly, without needing to retrain the entire model. We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12% and an annualized absolute return of 359.55% with zero maximum drawdown. dynABE also consistently outperforms the baseline models of support vector machine, neural network, and random forest in all case studies. Public Library of Science 2019-02-22 /pmc/articles/PMC6386270/ /pubmed/30794608 http://dx.doi.org/10.1371/journal.pone.0212487 Text en © 2019 Zhengyang Dong http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Dong, Zhengyang
Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies
title Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies
title_full Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies
title_fullStr Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies
title_full_unstemmed Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies
title_short Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies
title_sort dynamic advisor-based ensemble (dynabe): case study in stock trend prediction of critical metal companies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386270/
https://www.ncbi.nlm.nih.gov/pubmed/30794608
http://dx.doi.org/10.1371/journal.pone.0212487
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