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NumHTML : numeric-oriented hierarchical transformer model for multi-task financial forecasting
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Yang, Linyi, Li, Jiazheng, Dong, Ruihai, Zhang, Yue and Smyth, Barry (2022) NumHTML : numeric-oriented hierarchical transformer model for multi-task financial forecasting. In: The 36th AAAI Conference on Artificial Intelligence, Virtual, 22 Feb – 1 Mar 2022. Published in: Proceedings of the AAAI Conference on Artificial Intelligence, 36 (10). pp. 11604-11612. doi:10.1609/aaai.v36i10.21414 ISSN 2374-3468.
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Official URL: http://dx.doi.org/10.1609/aaai.v36i10.21414
Abstract
Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model (NumHTML) to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context.
Item Type: | Conference Item (Paper) | ||||||||||||
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Subjects: | H Social Sciences > HB Economic Theory H Social Sciences > HG Finance |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Corporations -- Finance -- Forecasting, Econometrics -- Data processing, Machine learning, Finance -- Data processing | ||||||||||||
Journal or Publication Title: | Proceedings of the AAAI Conference on Artificial Intelligence | ||||||||||||
Publisher: | AAAI Press | ||||||||||||
ISSN: | 2374-3468 | ||||||||||||
Official Date: | 28 June 2022 | ||||||||||||
Dates: |
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Volume: | 36 | ||||||||||||
Number: | 10 | ||||||||||||
Page Range: | pp. 11604-11612 | ||||||||||||
DOI: | 10.1609/aaai.v36i10.21414 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Reuse Statement (publisher, data, author rights): | Published by AAAI Press, Palo Alto, California USA Copyright © 2022, Association for the Advancement of Artificial Intelligence 1900 Embarcadero Road, Suite 101, Palo Alto, California 94303 All Rights Reserved. | ||||||||||||
Access rights to Published version: | Free Access (unspecified licence, 'bronze OA') | ||||||||||||
Date of first compliant deposit: | 27 February 2023 | ||||||||||||
Date of first compliant Open Access: | 27 February 2023 | ||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||||||
Title of Event: | The 36th AAAI Conference on Artificial Intelligence | ||||||||||||
Type of Event: | Conference | ||||||||||||
Location of Event: | Virtual | ||||||||||||
Date(s) of Event: | 22 Feb – 1 Mar 2022 | ||||||||||||
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Open Access Version: |
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