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Opinion dynamics in financial markets via random networks
We investigate financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize individuals in a financial market according to their trading strategy, namely, whether they are noise traders or fundamentalists. The opinion of a local majority com...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894230/ https://www.ncbi.nlm.nih.gov/pubmed/36445969 http://dx.doi.org/10.1073/pnas.2201573119 |
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author | Granha, Mateus F. B. Vilela, André L. M. Wang, Chao Nelson, Kenric P. Stanley, H. Eugene |
author_facet | Granha, Mateus F. B. Vilela, André L. M. Wang, Chao Nelson, Kenric P. Stanley, H. Eugene |
author_sort | Granha, Mateus F. B. |
collection | PubMed |
description | We investigate financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize individuals in a financial market according to their trading strategy, namely, whether they are noise traders or fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the decisions of fundamentalist agents. We introduce a noise parameter, q, to represent the level of anxiety and perceived uncertainty regarding market behavior, enabling the possibility of adrift financial action. We place individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interactions. At any given time, individuals assume one of two possible opinion states ±1 regarding buying or selling an asset. The model exhibits fundamental qualitative and quantitative real-world market features such as the distribution of logarithmic returns with fat tails, clustered volatility, and the long-term correlation of returns. We use Student’s t distributions to fit the histograms of logarithmic returns, showing a gradual shift from a leptokurtic to a mesokurtic regime depending on the fraction of fundamentalist agents. Furthermore, we compare our results with those concerning the distribution of the logarithmic returns of several real-world financial indices. |
format | Online Article Text |
id | pubmed-9894230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-98942302023-02-03 Opinion dynamics in financial markets via random networks Granha, Mateus F. B. Vilela, André L. M. Wang, Chao Nelson, Kenric P. Stanley, H. Eugene Proc Natl Acad Sci U S A Physical Sciences We investigate financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize individuals in a financial market according to their trading strategy, namely, whether they are noise traders or fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the decisions of fundamentalist agents. We introduce a noise parameter, q, to represent the level of anxiety and perceived uncertainty regarding market behavior, enabling the possibility of adrift financial action. We place individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interactions. At any given time, individuals assume one of two possible opinion states ±1 regarding buying or selling an asset. The model exhibits fundamental qualitative and quantitative real-world market features such as the distribution of logarithmic returns with fat tails, clustered volatility, and the long-term correlation of returns. We use Student’s t distributions to fit the histograms of logarithmic returns, showing a gradual shift from a leptokurtic to a mesokurtic regime depending on the fraction of fundamentalist agents. Furthermore, we compare our results with those concerning the distribution of the logarithmic returns of several real-world financial indices. National Academy of Sciences 2022-11-29 2022-12-06 /pmc/articles/PMC9894230/ /pubmed/36445969 http://dx.doi.org/10.1073/pnas.2201573119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Granha, Mateus F. B. Vilela, André L. M. Wang, Chao Nelson, Kenric P. Stanley, H. Eugene Opinion dynamics in financial markets via random networks |
title | Opinion dynamics in financial markets via random networks |
title_full | Opinion dynamics in financial markets via random networks |
title_fullStr | Opinion dynamics in financial markets via random networks |
title_full_unstemmed | Opinion dynamics in financial markets via random networks |
title_short | Opinion dynamics in financial markets via random networks |
title_sort | opinion dynamics in financial markets via random networks |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894230/ https://www.ncbi.nlm.nih.gov/pubmed/36445969 http://dx.doi.org/10.1073/pnas.2201573119 |
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