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The Flow of Information in Trading: An Entropy Approach to Market Regimes
In this study, we use entropy-based measures to identify different types of trading behaviors. We detect the return-driven trading using the conditional block entropy that dynamically reflects the “self-causality” of market return flows. Then we use the transfer entropy to identify the news-driven t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597144/ https://www.ncbi.nlm.nih.gov/pubmed/33286833 http://dx.doi.org/10.3390/e22091064 |
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author | Liu, Anqi Chen, Jing Yang, Steve Y. Hawkes, Alan G. |
author_facet | Liu, Anqi Chen, Jing Yang, Steve Y. Hawkes, Alan G. |
author_sort | Liu, Anqi |
collection | PubMed |
description | In this study, we use entropy-based measures to identify different types of trading behaviors. We detect the return-driven trading using the conditional block entropy that dynamically reflects the “self-causality” of market return flows. Then we use the transfer entropy to identify the news-driven trading activity that is revealed by the information flows from news sentiment to market returns. We argue that when certain trading behavior becomes dominant or jointly dominant, the market will form a specific regime, namely return-, news- or mixed regime. Based on 11 years of news and market data, we find that the evolution of financial market regimes in terms of adaptive trading activities over the 2008 liquidity and euro-zone debt crises can be explicitly explained by the information flows. The proposed method can be expanded to make “causal” inferences on other types of economic phenomena. |
format | Online Article Text |
id | pubmed-7597144 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75971442020-11-09 The Flow of Information in Trading: An Entropy Approach to Market Regimes Liu, Anqi Chen, Jing Yang, Steve Y. Hawkes, Alan G. Entropy (Basel) Article In this study, we use entropy-based measures to identify different types of trading behaviors. We detect the return-driven trading using the conditional block entropy that dynamically reflects the “self-causality” of market return flows. Then we use the transfer entropy to identify the news-driven trading activity that is revealed by the information flows from news sentiment to market returns. We argue that when certain trading behavior becomes dominant or jointly dominant, the market will form a specific regime, namely return-, news- or mixed regime. Based on 11 years of news and market data, we find that the evolution of financial market regimes in terms of adaptive trading activities over the 2008 liquidity and euro-zone debt crises can be explicitly explained by the information flows. The proposed method can be expanded to make “causal” inferences on other types of economic phenomena. MDPI 2020-09-22 /pmc/articles/PMC7597144/ /pubmed/33286833 http://dx.doi.org/10.3390/e22091064 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Anqi Chen, Jing Yang, Steve Y. Hawkes, Alan G. The Flow of Information in Trading: An Entropy Approach to Market Regimes |
title | The Flow of Information in Trading: An Entropy Approach to Market Regimes |
title_full | The Flow of Information in Trading: An Entropy Approach to Market Regimes |
title_fullStr | The Flow of Information in Trading: An Entropy Approach to Market Regimes |
title_full_unstemmed | The Flow of Information in Trading: An Entropy Approach to Market Regimes |
title_short | The Flow of Information in Trading: An Entropy Approach to Market Regimes |
title_sort | flow of information in trading: an entropy approach to market regimes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597144/ https://www.ncbi.nlm.nih.gov/pubmed/33286833 http://dx.doi.org/10.3390/e22091064 |
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