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Conference Paper/Proceeding/Abstract 1207 views 362 downloads

Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns

Alex Momotov, Xianghua Xie Orcid Logo

International Conference on Data Mining and Knowledge Discovery

Swansea University Author: Xianghua Xie Orcid Logo

Abstract

This research contrasts and compares the state-of-the-art techniques of the two approaches within the domain of news sentiment analysis, as well as, investigates a novel document encoding representation of the `TF-IDF momentum matrix'. The presented lexicon-based methodology is centred around L...

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Published in: International Conference on Data Mining and Knowledge Discovery
Published: 2019
URI: https://cronfa.swan.ac.uk/Record/cronfa48786
first_indexed 2019-02-11T11:58:09Z
last_indexed 2019-03-27T11:20:10Z
id cronfa48786
recordtype SURis
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spelling 2019-03-26T16:10:13.7636167 v2 48786 2019-02-09 Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2019-02-09 MACS This research contrasts and compares the state-of-the-art techniques of the two approaches within the domain of news sentiment analysis, as well as, investigates a novel document encoding representation of the `TF-IDF momentum matrix'. The presented lexicon-based methodology is centred around Loughran & McDonald financial sentiment word lists and reaches 86.4% explained stock momentum variance, whereas the classification approach follows a thematic analysis pipeline implementing Latent Dirichlet Allocation and achieves that of 94.8%. As an additional element of model evaluation, the research implements Thermal Optimal Path method which relies on a dynamic programming approach for performance optimisation. Conference Paper/Proceeding/Abstract International Conference on Data Mining and Knowledge Discovery 1 4 2019 2019-04-01 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2019-03-26T16:10:13.7636167 2019-02-09T11:50:18.2870043 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Alex Momotov 1 Xianghua Xie 0000-0002-2701-8660 2 0048786-09022019115040.pdf DMKDConferencePaper.pdf 2019-02-09T11:50:40.9070000 Output 818661 application/pdf Accepted Manuscript true 2020-04-09T00:00:00.0000000 true eng
title Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns
spellingShingle Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns
Xianghua Xie
title_short Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns
title_full Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns
title_fullStr Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns
title_full_unstemmed Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns
title_sort Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Xianghua Xie
author2 Alex Momotov
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title International Conference on Data Mining and Knowledge Discovery
publishDate 2019
institution Swansea University
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
document_store_str 1
active_str 0
description This research contrasts and compares the state-of-the-art techniques of the two approaches within the domain of news sentiment analysis, as well as, investigates a novel document encoding representation of the `TF-IDF momentum matrix'. The presented lexicon-based methodology is centred around Loughran & McDonald financial sentiment word lists and reaches 86.4% explained stock momentum variance, whereas the classification approach follows a thematic analysis pipeline implementing Latent Dirichlet Allocation and achieves that of 94.8%. As an additional element of model evaluation, the research implements Thermal Optimal Path method which relies on a dynamic programming approach for performance optimisation.
published_date 2019-04-01T04:35:11Z
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score 11.089407

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