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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...

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Detalles Bibliográficos
Autores principales: Liu, Anqi, Chen, Jing, Yang, Steve Y., Hawkes, Alan G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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