The Chinese Correction of February 2007: How financial hierarchies change in a market crash

Teh, Boon Kin; Goo, Yik Wen; Lian, Tong Wei; Ong, Wei Guang; Choi, Wen Ting; Damodaran, Mridula; Cheong, Siew Ann

doi:10.1016/j.physa.2015.01.024

Cited by 13 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assets considered in the empirical studies come from different regions and markets, often from the publication authors' own geographical area. For example, Asian and Chinese researchers focus on Chinese stocks [145,3,146,147,148,54,149,150,51,151]. Besides the Chinese markets, some other Asian markets are investigated: Japan [73,152,153], Korea [154,155,156,63,157,158], Vietnam [131,132], India [130,159],…”

Section: Appendix a The Ecosystem Of Correlations Network And Hierarc...mentioning

confidence: 99%

A review of two decades of correlations, hierarchies, networks and clustering in financial markets

Marti,

Nielsen,

Bińkowski

et al. 2017

Preprint

View full text Add to dashboard Cite

We review the state of the art of clustering financial time series and the study of their correlations alongside other interaction networks. The aim of the review is to gather in one place the relevant material from different fields, e.g. machine learning, econophysics, statistical physics, econometrics, behavioral finance. We hope it will help researchers to use more effectively this alternative modeling of the financial time series. Decision makers may also be able to leverage its insights. Finally, we also hope that this review will form the basis of an open toolbox to study correlations, hierarchies, networks and clustering in financial markets.

show abstract

Section: Appendix a The Ecosystem Of Correlations Network And Hierarc...mentioning

confidence: 99%

A review of two decades of correlations, hierarchies, networks and clustering in financial markets

Marti,

Nielsen,

Bińkowski

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Complex network theory has been adopted in some works to monitor evolutionary behaviors of financial systems [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. For instance, Song et al [5] convert crosscorrelations between a total of 57 industry portfolios in the American stock market into a series of planar maximally filtered graphs (a network embedded in a highdimensional surface) to represent the states of the corresponding successive durations, and find that mutual entropy between successive states reaches a peak at a financial crisis.…”

Section: Introductionmentioning

confidence: 99%

Immediate causality network of stock markets

Zhou¹,

Qiu²,

Gu³

et al. 2018

EPL

View full text Add to dashboard Cite

A financial system contains many elements networked by their relationships. Extensive works show that topological structure of the network stores rich information on evolutionary behaviors of the system such as early warning signals of collapses and/or crises. Existing works focus mainly on the network structure within a single stock market, while a collapse/crisis occurs in a macro-scale covering several or even all markets in the world. This mismatch of scale leads to unacceptable noise to the topological structure, and lack of information stored in relationships between different markets. In this work by using the transfer entropy we reconstruct the influential network between ten typical stock markets distributed in the world. Interesting findings include, before a financial crisis the connection strength reaches a maxima, which can act as an early warning signal of financial crises; The markets in America are mono-directionally and strongly influenced by that in Europe and act as the center; Some strongly linked pairs have also close correlations. The findings are helpful in understanding the evolution and modelling the dynamical process of the global financial system. keywords: stock market network; transfer entropy matrix PACS numbers: 89.75.-k, 05.40.-a, 05.45.-a

show abstract

“…Inspired by a Soup-of-Groups (SOG) model description of statistical fusion-fission processes in fault planes, Cheong et al were able to forecast the timings, magnitudes, and locations of large Taiwanese earthquakes [ 19 ]. Furthermore, in a recent paper [ 20 , 21 ], we tracked robust clusters of stocks in the Singapore Exchange over 2008 and 2009, and found that these routinely merged to form larger clusters, and also disintegrated into smaller clusters. In particular, one of the clusters grew steadily into a giant cluster as we approached the October 2008 crash.…”

Section: Introductionmentioning

confidence: 99%

The Asian Correction Can Be Quantitatively Forecasted Using a Statistical Model of Fusion-Fission Processes

Teh

Cheong

2016

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.

show abstract

The Chinese Correction of February 2007: How financial hierarchies change in a market crash

Cited by 13 publications

References 52 publications

A review of two decades of correlations, hierarchies, networks and clustering in financial markets

A review of two decades of correlations, hierarchies, networks and clustering in financial markets

Immediate causality network of stock markets

The Asian Correction Can Be Quantitatively Forecasted Using a Statistical Model of Fusion-Fission Processes

Contact Info

Product

Resources

About