Anticipating Economic Market Crises Using Measures of Collective Panic

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Abstract

Predicting panic is of critical importance in many areas of human and animal behavior, notably in the context of economics. The recent financial crisis is a case in point. Panic may be due to a specific external threat or self-generated nervousness. Here we show that the recent economic crisis and earlier large single-day panics were preceded by extended periods of high levels of market mimicry—direct evidence of uncertainty and nervousness, and of the comparatively weak influence of external news. High levels of mimicry can be a quite general indicator of the potential for self-organized crises.

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Most cited references 13

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Economic networks: the new challenges.

Frank Schweitzer, Giorgio Fagiolo, Didier Sornette … (2009)

The current economic crisis illustrates a critical need for new and fundamental understanding of the structure and dynamics of economic networks. Economic systems are increasingly built on interdependencies, implemented through trans-national credit and investment networks, trade relations, or supply chains that have proven difficult to predict and control. We need, therefore, an approach that stresses the systemic complexity of economic networks and that can be used to revise and extend established paradigms in economic theory. This will facilitate the design of policies that reduce conflicts between individual interests and global efficiency, as well as reduce the risk of global failure by making economic networks more robust.

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Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market

Dror Kenett, Michele Tumminello, Asaf Madi … (2010)

What are the dominant stocks which drive the correlations present among stocks traded in a stock market? Can a correlation analysis provide an answer to this question? In the past, correlation based networks have been proposed as a tool to uncover the underlying backbone of the market. Correlation based networks represent the stocks and their relationships, which are then investigated using different network theory methodologies. Here we introduce a new concept to tackle the above question—the partial correlation network. Partial correlation is a measure of how the correlation between two variables, e.g., stock returns, is affected by a third variable. By using it we define a proxy of stock influence, which is then used to construct partial correlation networks. The empirical part of this study is performed on a specific financial system, namely the set of 300 highly capitalized stocks traded at the New York Stock Exchange, in the time period 2001–2003. By constructing the partial correlation network, unlike the case of standard correlation based networks, we find that stocks belonging to the financial sector and, in particular, to the investment services sub-sector, are the most influential stocks affecting the correlation profile of the system. Using a moving window analysis, we find that the strong influence of the financial stocks is conserved across time for the investigated trading period. Our findings shed a new light on the underlying mechanisms and driving forces controlling the correlation profile observed in a financial market.

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Dynamics of market correlations: Taxonomy and portfolio analysis

K Kaski, J. -P. Onnela, J. Kertész … (2003)

The time dependence of the recently introduced minimum spanning tree description of correlations between stocks, called the ``asset tree'' have been studied to reflect the economic taxonomy. The nodes of the tree are identified with stocks and the distance between them is a unique function of the corresponding element of the correlation matrix. By using the concept of a central vertex, chosen as the most strongly connected node of the tree, an important characteristic is defined by the mean occupation layer (MOL). During crashes the strong global correlation in the market manifests itself by a low value of MOL. The tree seems to have a scale free structure where the scaling exponent of the degree distribution is different for `business as usual' and `crash' periods. The basic structure of the tree topology is very robust with respect to time. We also point out that the diversification aspect of portfolio optimization results in the fact that the assets of the classic Markowitz portfolio are always located on the outer leaves of the tree. Technical aspects like the window size dependence of the investigated quantities are also discussed.

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Author and article information

Contributors

Lidia Adriana Braunstein: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2015

Publication date (Electronic): 17 July 2015

Volume: 10

Issue: 7

Electronic Location Identifier: e0131871

Affiliations

[1 ]New England Complex Systems Institute, Cambridge, MA, United States of America

[2 ]Universidade Estadual de Campinas, Campinas, SP, Brazil

[3 ]University of Massachusetts Dartmouth, Dartmouth, MA, United States of America

[4 ]Brandeis University, Waltham, MA, United States of America

IFIMAR, UNMdP-CONICET, ARGENTINA

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: DH ML MAMA DDC DB IRE YB. Performed the experiments: DH ML MAMA DDC DB IRE YB. Analyzed the data: DH ML MAMA DDC DB IRE YB. Contributed reagents/materials/analysis tools: DH ML MAMA DDC DB IRE YB. Wrote the paper: DH ML MAMA DDC DB IRE YB.

* E-mail: braha@ 123456necsi.edu

Article

Publisher ID: PONE-D-15-06936

DOI: 10.1371/journal.pone.0131871

PMC ID: 4506134

PubMed ID: 26185988

SO-VID: 8af7f57f-a213-4e62-b2c8-f92e3fb373c7

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

History

Date received : 13 February 2015

Date accepted : 8 June 2015

Page count

Figures: 10, Tables: 2, Pages: 27

Funding

The authors have no support or funding to report.

Custom metadata

Data Availability The primary dataset was not originally generated by the authors, and interested researchers can obtain the data independently from the third party providers specified below. All the historical return data is publicly available from Yahoo, Google and other online sources, including Capital IQ ( https://www.capitaliq.com), which was used for this purpose. The list of companies that we used, the Russell 3000, was obtained from Russell Investments ( https://www.russell.com). The robustness tests indicate that the use of this list is not essential to reproduction of the results, but can still be obtained from Russell in the same manner that the authors obtained it.

Anticipating Economic Market Crises Using Measures of Collective Panic

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Most cited references 13

Economic networks: the new challenges.

Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market

Dynamics of market correlations: Taxonomy and portfolio analysis

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