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Conference Paper/Proceeding/Abstract 1371 views 516 downloads

Recurrent Neural Networks for Financial Time-Series Modelling

Gavin Tsang, Jingjing Deng, Xianghua Xie Orcid Logo

Pages: 892 - 897

Swansea University Authors: Jingjing Deng, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1109/ICPR.2018.8545666

Abstract

In this paper, we present a novel deep Long Short-Term Memory (LSTM) based time-series data modelling for use in stock market index prediction. A dataset comprised of six market indices from around the world were chosen to demonstrate the robustness in varying market conditions with an aim to foreca...

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ISSN: 1051-4651
Published: Beijing, China 25th International Conference on Pattern Recognition 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa39477
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spelling 2019-01-22T16:40:31.2152727 v2 39477 2018-04-18 Recurrent Neural Networks for Financial Time-Series Modelling 6f6d01d585363d6dc1622640bb4fcb3f Jingjing Deng Jingjing Deng true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2018-04-18 MACS In this paper, we present a novel deep Long Short-Term Memory (LSTM) based time-series data modelling for use in stock market index prediction. A dataset comprised of six market indices from around the world were chosen to demonstrate the robustness in varying market conditions with an aim to forecast the next day closing price. Conference Paper/Proceeding/Abstract 892 897 25th International Conference on Pattern Recognition Beijing, China 1051-4651 Deep Learning, Neural networks, time series data analysis, financial modelling. 31 12 2018 2018-12-31 10.1109/ICPR.2018.8545666 http://www.icpr2018.org/index.php?m=content&amp;c=index&amp;a=init COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2019-01-22T16:40:31.2152727 2018-04-18T18:38:03.1738031 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Gavin Tsang 1 Jingjing Deng 2 Xianghua Xie 0000-0002-2701-8660 3 0039477-18042018183853.pdf icpr2018.pdf 2018-04-18T18:38:53.4070000 Output 478640 application/pdf Accepted Manuscript true 2019-01-21T00:00:00.0000000 true eng
title Recurrent Neural Networks for Financial Time-Series Modelling
spellingShingle Recurrent Neural Networks for Financial Time-Series Modelling
Jingjing Deng
Xianghua Xie
title_short Recurrent Neural Networks for Financial Time-Series Modelling
title_full Recurrent Neural Networks for Financial Time-Series Modelling
title_fullStr Recurrent Neural Networks for Financial Time-Series Modelling
title_full_unstemmed Recurrent Neural Networks for Financial Time-Series Modelling
title_sort Recurrent Neural Networks for Financial Time-Series Modelling
author_id_str_mv 6f6d01d585363d6dc1622640bb4fcb3f
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 6f6d01d585363d6dc1622640bb4fcb3f_***_Jingjing Deng
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Jingjing Deng
Xianghua Xie
author2 Gavin Tsang
Jingjing Deng
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_start_page 892
publishDate 2018
institution Swansea University
issn 1051-4651
doi_str_mv 10.1109/ICPR.2018.8545666
publisher 25th International Conference on Pattern Recognition
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://www.icpr2018.org/index.php?m=content&amp;c=index&amp;a=init
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description In this paper, we present a novel deep Long Short-Term Memory (LSTM) based time-series data modelling for use in stock market index prediction. A dataset comprised of six market indices from around the world were chosen to demonstrate the robustness in varying market conditions with an aim to forecast the next day closing price.
published_date 2018-12-31T01:35:50Z
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score 11.04748

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