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STTM: an efficient approach to estimating news impact on stock movement direction
Open text data, such as financial news, are thought to be able to affect or to describe stock market behavior, however, there are no widely accepted algorithms for extracting the relationship between stock quotes time series and fast-growing textual representation of economic information. The field...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280229/ https://www.ncbi.nlm.nih.gov/pubmed/37346316 http://dx.doi.org/10.7717/peerj-cs.1156 |
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author | Riabykh, Aleksei Surzhko, Denis Konovalikhin, Maxim Koltcov, Sergei |
author_facet | Riabykh, Aleksei Surzhko, Denis Konovalikhin, Maxim Koltcov, Sergei |
author_sort | Riabykh, Aleksei |
collection | PubMed |
description | Open text data, such as financial news, are thought to be able to affect or to describe stock market behavior, however, there are no widely accepted algorithms for extracting the relationship between stock quotes time series and fast-growing textual representation of economic information. The field remains challenging and understudied. In particular, topic modeling as a powerful tool for interpretable dimensionality reduction has been hardly ever used for such tasks. We present a topic modeling framework for assessing the relationship between financial news stream and stock prices in order to maximize trader’s gain. To do so, we use a dataset of economic news sections of three Russian national media sources (Kommersant, Vedomosti, and RIA Novosti) containing 197,678 economic articles. They are used to predict 39 time series of the most liquid Russian stocks collected over eight years, from 2013 to 2021. Our approach shows the ability to detect significant return-predictive signals and outperforms 26 existing models in terms of Sharpe ratio and annual return of simple long strategy. In particular, it shows a significant Granger causal relationship for more than 70% of portfolio stocks. Furthermore, the approach produces highly interpretable results, requires no domain-specific dictionaries, and, unlike most existing industrial solutions, can be calibrated for individual time series. This makes it directly usable for trading strategies and analytical tasks. Finally, since topic modeling shows its efficiency for most European languages, our approach is expected to be transferrable to European stock markets as well. |
format | Online Article Text |
id | pubmed-10280229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102802292023-06-21 STTM: an efficient approach to estimating news impact on stock movement direction Riabykh, Aleksei Surzhko, Denis Konovalikhin, Maxim Koltcov, Sergei PeerJ Comput Sci Algorithms and Analysis of Algorithms Open text data, such as financial news, are thought to be able to affect or to describe stock market behavior, however, there are no widely accepted algorithms for extracting the relationship between stock quotes time series and fast-growing textual representation of economic information. The field remains challenging and understudied. In particular, topic modeling as a powerful tool for interpretable dimensionality reduction has been hardly ever used for such tasks. We present a topic modeling framework for assessing the relationship between financial news stream and stock prices in order to maximize trader’s gain. To do so, we use a dataset of economic news sections of three Russian national media sources (Kommersant, Vedomosti, and RIA Novosti) containing 197,678 economic articles. They are used to predict 39 time series of the most liquid Russian stocks collected over eight years, from 2013 to 2021. Our approach shows the ability to detect significant return-predictive signals and outperforms 26 existing models in terms of Sharpe ratio and annual return of simple long strategy. In particular, it shows a significant Granger causal relationship for more than 70% of portfolio stocks. Furthermore, the approach produces highly interpretable results, requires no domain-specific dictionaries, and, unlike most existing industrial solutions, can be calibrated for individual time series. This makes it directly usable for trading strategies and analytical tasks. Finally, since topic modeling shows its efficiency for most European languages, our approach is expected to be transferrable to European stock markets as well. PeerJ Inc. 2022-12-16 /pmc/articles/PMC10280229/ /pubmed/37346316 http://dx.doi.org/10.7717/peerj-cs.1156 Text en ©2022 Riabykh 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Riabykh, Aleksei Surzhko, Denis Konovalikhin, Maxim Koltcov, Sergei STTM: an efficient approach to estimating news impact on stock movement direction |
title | STTM: an efficient approach to estimating news impact on stock movement direction |
title_full | STTM: an efficient approach to estimating news impact on stock movement direction |
title_fullStr | STTM: an efficient approach to estimating news impact on stock movement direction |
title_full_unstemmed | STTM: an efficient approach to estimating news impact on stock movement direction |
title_short | STTM: an efficient approach to estimating news impact on stock movement direction |
title_sort | sttm: an efficient approach to estimating news impact on stock movement direction |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280229/ https://www.ncbi.nlm.nih.gov/pubmed/37346316 http://dx.doi.org/10.7717/peerj-cs.1156 |
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