Spectra of empirical autocorrelation matrices: A random-matrix-theory–inspired perspective

Jamali, Tayeb; Jafari, G. R.

doi:10.1209/0295-5075/111/10001

Cited by 16 publications

(14 citation statements)

References 27 publications

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“…This may contribute to a collective interest in the market to lead other stocks. This collective behavior illustrates leader of the market [30][31][32] which determines general market trends. Then, the fear of possible turning point in the market trend, causes investors to be careful about the stability of trend.…”

Section: Discussionmentioning

confidence: 83%

Growth Dynamics of Value and Cost Trade-off in Temporal Networks

Hasani¹,

Masoomi²,

Ardalankia³

et al. 2019

Preprint

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The question is: What does happen to the real-world networks which cause them not to grow permanently? The idea here is that real-world networks have to pay the cost of growth. We investigate the growth and trade-off between value and cost in the networks with cost and preferential attachment together. Since, the preferential attachment in BA model does not consider any stop against the infinite growth of networks, we introduce a modified version of preferential attachment of BA model. This idea makes sense because the growth of real networks may be finite. In the present study, by combining preferential attachment in the science of temporal networks (interval graphs), and, the first order differential equations of value and cost of making links, future equilibrium of an evolving network is illustrated. During the process of achieving a winning position, the variables against growth such as the competition cost, besides with the internally structural cost may emerge. At the end, by applying this modified model, we found the circumstances which a trade-off between value and cost emerge.

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Section: Discussionmentioning

confidence: 83%

Growth Dynamics of Value and Cost Trade-off in Temporal Networks

Hasani¹,

Masoomi²,

Ardalankia³

et al. 2019

Preprint

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“…The common approach in studying collective behavior of a market is based on RMT results and its deviation from market results. Recently, two of the authors introduced another criterion based on fractional Gaussian noises 14 . The eigenvalue distribution of markets differs from RMT's distribution; there are some eigenvalues out of the RMT bulk region.…”

Section: Forty Markets As a World Stock Marketmentioning

confidence: 99%

Emergence of world-stock-market network

Saeedian

Jamali

Kamali

et al. 2019

Physica A: Statistical Mechanics and its Applications

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In the age of globalization, it is natural that the stock market of each country is not independent form the other markets. In this case, collective behavior could be emerged form their dependency together. This article studies the collective behavior of a set of forty influential markets in the world economy with the aim of exploring a global financial structure that could be called world-stock-market network. Towards this end, we analyze the cross-correlation matrix of the indices of these forty markets using Random Matrix Theory (RMT). We find the degree of collective behavior among the markets and the share of each market in their structural formation. This finding together with the results obtained from the same calculation on four stock markets reinforce the idea of a world financial market. Finally, we draw the dendrogram of the cross-correlation matrix to make communities in this abstract global market visible. The dendrogram, drawn by at least thirty percent of correlation, shows that the world financial market comprises three communities each of which includes stock markets with geographical proximity.

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“…[10] investigated the structures of networks constructed from principal components of the empirical equal-time cross-correlation matrices of stock price fluctuations on the Tehran stock exchange and in the DJIA. [11] constructed an autocorrelation matrix of a time series and analyzed it based on the random-matrix theory approach and fractional Gaussian noises.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Principal Components in Stock Price Correlation

Souma¹

2021

Front. Phys.

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The following methods are used to analyze correlations among stock returns. 1) The meaningful part of the correlation is obtained by applying random matrix theory to the equal-time cross-correlation matrix of assets returns. 2) Null-model randomness is implemented via rotational random shuffling. 3) Principal component analysis and Helmholtz-Hodge decomposition are used to extract leading and lagging relationships among assets from the complex correlation matrix constructed from the Hilbert-transformed data set of asset returns. These methods are applied to price data for 445 assets from the S&P 500 from 2010 to 2019 (2,510 business days). Additional analysis and discussion clarify key aspects of leading and lagging relationships among business sectors in the market. Numerical investigation of these dataset reveals the possibility that leading and lagging relationships among business sectors may depend on gross market conditions.

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Spectra of empirical autocorrelation matrices: A random-matrix-theory–inspired perspective

Cited by 16 publications

References 27 publications

Growth Dynamics of Value and Cost Trade-off in Temporal Networks

Growth Dynamics of Value and Cost Trade-off in Temporal Networks

Emergence of world-stock-market network

Characteristics of Principal Components in Stock Price Correlation

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