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Stock index trend prediction based on TabNet feature selection and long short-term memory
In this study, we propose a predictive model TabLSTM that combines machine learning methods such as TabNet and Long Short-Term Memory Neural Network (LSTM) with a complete factor library for stock index trend prediction. Our motivation is based on the notion that there are numerous interrelated fact...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746941/ https://www.ncbi.nlm.nih.gov/pubmed/36512541 http://dx.doi.org/10.1371/journal.pone.0269195 |
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author | Wei, Xiaolu Ouyang, Hongbing Liu, Muyan |
author_facet | Wei, Xiaolu Ouyang, Hongbing Liu, Muyan |
author_sort | Wei, Xiaolu |
collection | PubMed |
description | In this study, we propose a predictive model TabLSTM that combines machine learning methods such as TabNet and Long Short-Term Memory Neural Network (LSTM) with a complete factor library for stock index trend prediction. Our motivation is based on the notion that there are numerous interrelated factors in the stock market, and the factors that affect each stock are different. Therefore, a complete factor library and an efficient feature selection technique are necessary to predict stock index. In this paper, we first build a factor database that includes macro, micro and technical indicators. Successively, we calculate the factor importance through TabNet and rank them. Based on a prespecified threshold, the optimal factors set will include only the highest-ranked factors. Finally, using the optimal factors set as input information, LSTM is employed to predict the future trend of 4 stock indices. Empirical validation of the model shows that the combination of TabNet for factors selection and LSTM outperforms existing methods. Moreover, constructing a factor database is necessary for stock index prediction. The application of our method does not only show the feasibility to predict stock indices across different financial markets, yet it also provides an complete factor database and a comprehensive architecture for stock index trend prediction, which may provide some references for stock forecasting and quantitative investments. |
format | Online Article Text |
id | pubmed-9746941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97469412022-12-14 Stock index trend prediction based on TabNet feature selection and long short-term memory Wei, Xiaolu Ouyang, Hongbing Liu, Muyan PLoS One Research Article In this study, we propose a predictive model TabLSTM that combines machine learning methods such as TabNet and Long Short-Term Memory Neural Network (LSTM) with a complete factor library for stock index trend prediction. Our motivation is based on the notion that there are numerous interrelated factors in the stock market, and the factors that affect each stock are different. Therefore, a complete factor library and an efficient feature selection technique are necessary to predict stock index. In this paper, we first build a factor database that includes macro, micro and technical indicators. Successively, we calculate the factor importance through TabNet and rank them. Based on a prespecified threshold, the optimal factors set will include only the highest-ranked factors. Finally, using the optimal factors set as input information, LSTM is employed to predict the future trend of 4 stock indices. Empirical validation of the model shows that the combination of TabNet for factors selection and LSTM outperforms existing methods. Moreover, constructing a factor database is necessary for stock index prediction. The application of our method does not only show the feasibility to predict stock indices across different financial markets, yet it also provides an complete factor database and a comprehensive architecture for stock index trend prediction, which may provide some references for stock forecasting and quantitative investments. Public Library of Science 2022-12-13 /pmc/articles/PMC9746941/ /pubmed/36512541 http://dx.doi.org/10.1371/journal.pone.0269195 Text en © 2022 Wei et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Wei, Xiaolu Ouyang, Hongbing Liu, Muyan Stock index trend prediction based on TabNet feature selection and long short-term memory |
title | Stock index trend prediction based on TabNet feature selection and long short-term memory |
title_full | Stock index trend prediction based on TabNet feature selection and long short-term memory |
title_fullStr | Stock index trend prediction based on TabNet feature selection and long short-term memory |
title_full_unstemmed | Stock index trend prediction based on TabNet feature selection and long short-term memory |
title_short | Stock index trend prediction based on TabNet feature selection and long short-term memory |
title_sort | stock index trend prediction based on tabnet feature selection and long short-term memory |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746941/ https://www.ncbi.nlm.nih.gov/pubmed/36512541 http://dx.doi.org/10.1371/journal.pone.0269195 |
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