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U.S. stock market interaction network as learned by the Boltzmann machine
KTH, School of Engineering Sciences (SCI), Applied Physics, Nanostructure Physics. KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.ORCID iD: 0000-0003-2810-9203
KTH, Centres, Nordic Institute for Theoretical Physics NORDITA. The Kavli Institute for Systems Neuroscience, NTNU, Trondheim, Norway.
KTH, Centres, Nordic Institute for Theoretical Physics NORDITA. Institute for Materials Science, Los Alamos National Laboratory, Los Alamos, NM, United States.
2015 (English)In: European Physical Journal B: Condensed Matter Physics, ISSN 1434-6028, E-ISSN 1434-6036, Vol. 88, no 12, 1-14 p.Article in journal (Refereed) Published
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Abstract [en]

We study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented results show that binarization preserves the correlation structure of the market. Properties of distributions of external fields and couplings as well as the market interaction network and industry sector clustering structure are studied for different historical dates and moving window sizes. We demonstrate that the observed positive heavy tail in distribution of couplings is related to the sparse clustering structure of the market. We also show that discrepancies between the model’s parameters might be used as a precursor of financial instabilities.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. Vol. 88, no 12, 1-14 p.
Keyword [en]
Statistical and Nonlinear Physics
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-181853DOI: 10.1140/epjb/e2015-60282-3ISI: 000371893400002Scopus ID: 2-s2.0-84949229986OAI: oai:DiVA.org:kth-181853DiVA: diva2:901556
Note

QC 20160208

Available from: 2016-02-08 Created: 2016-02-05 Last updated: 2016-04-01Bibliographically approved

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Citation style
  • apa
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