The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Econometrics of Financial Markets
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2012) · Princeton University Press eBooks · doi:10.2307/j.ctt7skm5 · Cited by 5,875
Portfolio Selection: Efficient Diversification of Investments
Stuart, A., & Markowitz, H. M. (1959) · OR · doi:10.2307/3006625 · Cited by 5,534
The Econometrics of Financial Markets
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997) · doi:10.1515/9781400830213 · Cited by 5,008
CAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C. (2001) · The Philosophical Review · doi:10.1215/00318108-110-4-639 · Cited by 4,741
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
Modern Portfolio Theory and Investment Analysis.
Smith, K. V., Elton, E. J., & Gruber, M. J. (1982) · The Journal of Finance · doi:10.2307/2327857 · Cited by 3,012
An Introduction to Econophysics: Correlations and Complexity in Finance
Mantegna, R. N., Stanley, H. E., & Chriss, N. A. (2000) · Physics Today · doi:10.1063/1.1341926 · Cited by 2,297
Self-citationHierarchical structure in financial markets
Mantegna, R. N. (1999) · The European Physical Journal B · doi:10.1007/s100510050929 · Cited by 2,069
Self-citationNoise Dressing of Financial Correlation Matrices
Laloux, L., Cizeau, P., Bouchaud, J.-P., & Potters, M. (1999) · Physical Review Letters · doi:10.1103/physrevlett.83.1467 · Cited by 1,241
Shipley, B. (2000) · Cited by 1,084
Cited by
Multifractal analysis of financial markets: a review
Jiang, Z.-Q., Xie, W.-J., Zhou, W.-X., & Sornette, D. (2019) · Reports on Progress in Physics · doi:10.1088/1361-6633/ab42fb · Cited by 423
Investigating the structure of semantic networks in low and high creative persons
Kenett, Y. N., Anaki, D., & Faust, M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00407 · Cited by 392
Castañeda, G., Chávez-Juárez, F., & Guerrero, O. A. (2018) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2018.07.017 · Cited by 40
Metadata
- Authors
- Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-12-20
- Volume
- 5
- Issue
- 12
- Pages
- e15032
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W1964040105
- Cited by
- 362
- References
- 28
- FWCI
- 23.91
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market. https://app.cochat.ai/papers/10.1371/journal.pone.0015032
- The paper
- Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E. (2010). Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market. PLoS ONE. https://doi.org/10.1371/journal.pone.0015032
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.