Comparing the collective behavior of banking industry in emerging markets versus mature ones by random matrix approach

Vahabi, Hanie.; Namaki, Ali; Raei, Reza

doi:10.3389/fphy.2022.896303

Cited by 3 publications

(1 citation statement)

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“…In another study, the world stock market is analyzed with RMT [22]. Recent works also investigated various financial markets using RMT [23][24][25].…”

Section: Applying Rmt Approaches In Financial Marketsmentioning

confidence: 99%

Looking into the Market Behaviors through the Lens of Correlations and Eigenvalues: An Investigation on the Chinese and US Markets Using RMT

Tang,

Xiong,

Cheng

et al. 2023

Entropy

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This research systematically analyzes the behaviors of correlations among stock prices and the eigenvalues for correlation matrices by utilizing random matrix theory (RMT) for Chinese and US stock markets. Results suggest that most eigenvalues of both markets fall within the predicted distribution intervals by RMT, whereas some larger eigenvalues fall beyond the noises and carry market information. The largest eigenvalue represents the market and is a good indicator for averaged correlations. Further, the average largest eigenvalue shows similar movement with the index for both markets. The analysis demonstrates the fraction of eigenvalues falling beyond the predicted interval, pinpointing major market switching points. It has identified that the average of eigenvector components corresponds to the largest eigenvalue switch with the market itself. The investigation on the second largest eigenvalue and its eigenvector suggests that the Chinese market is dominated by four industries whereas the US market contains three leading industries. The study later investigates how it changes before and after a market crash, revealing that the two markets behave differently, and a major market structure change is observed in the Chinese market but not in the US market. The results shed new light on mining hidden information from stock market data.

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“…In another study, the world stock market is analyzed with RMT [22]. Recent works also investigated various financial markets using RMT [23][24][25].…”

Section: Applying Rmt Approaches In Financial Marketsmentioning

confidence: 99%

Looking into the Market Behaviors through the Lens of Correlations and Eigenvalues: An Investigation on the Chinese and US Markets Using RMT

Tang,

Xiong,

Cheng

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

Detrended partial cross-correlation analysis-random matrix theory for denoising network construction

Wang,

Zhang,

Wang

et al. 2024

Appl Intell

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Analysis of financial markets by coupled criticality approach

Adeli,

Namaki,

Shirkavand

et al. 2024

Int. J. Mod. Phys. C

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Analysis of the criticality in finance and economics is an important topic for scholars. The crises are conditions in which unpredictable behavior have been emerged. On the other hand, the contagion from one market to another one due to the complex nature of them is a crucial concept. Therefore, we should consider economies and financial markets as entangled systems that interact with each other, and studying one’s behavior without considering its interacting structures will not provide us with adequate information. In this paper, we have considered the critical states of some emerging and mature markets’ indices. S&P500 and Nikkei 225 as representatives of mature markets, and SSE Composite and TEDPIX as representatives of emerging markets have been analyzed. Coupled criticality emerges when two interacting systems reach their critical state due to their interactions. One of the efficient methods to study the coupled criticality is the bi-variate multifractal random walk model. This method is useful to assess the correlation of rare events and coupled criticality in entangled systems such as financial markets. The observations in this paper show that TEDPIX and S&P500 have higher critical states among others in most time lags by assessing criticality parameter explained in the model, and in longer time lags criticality parameter decreases in all indices. Coupled criticality parameter explained in the model is assessed between each index mentioned and TEDPIX. Results show that TEDPIX and S&P500 have the highest level of coupled criticality. To figure out how long-range time correlation between rare events in each index affect its critical behavior, the data of each index are shuffled, and the criticality parameter is calculated for the shuffled data. From the observations, it can be concluded that this matter has more substantial effect on mature markets. As it is known, shuffling removes the effect of the long-range time correlations. Therefore, the coupled criticality parameter is calculated for the shuffled S&P500 data and TEDPIX. Results show that the long-range correlation of S&P500 data has a significant effect on the coupled criticality behavior of S&P500 and TEDPIX.

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Comparing the collective behavior of banking industry in emerging markets versus mature ones by random matrix approach

Cited by 3 publications

References 50 publications

Looking into the Market Behaviors through the Lens of Correlations and Eigenvalues: An Investigation on the Chinese and US Markets Using RMT

Looking into the Market Behaviors through the Lens of Correlations and Eigenvalues: An Investigation on the Chinese and US Markets Using RMT

Detrended partial cross-correlation analysis-random matrix theory for denoising network construction

Analysis of financial markets by coupled criticality approach

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