(Dis)integration levels across global stock markets: A multidimensional scaling and cluster analysis

Esmalifalak, Hamidreza; Ajirlou, Ali Irannezhad; Behrouz, Sahar Pordeli; Esmalifalak, Maryam

doi:10.1016/j.eswa.2015.06.053

Cited by 17 publications

(7 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…When twostep clustering is performed for k=3, CAC 40 and FTSE MIB form one group; DAX, NIKKEI 225 and S&P 500 form another group, and FTSE 100 and S&P TSK Composite form the third group for the related period. Results partly showed similarity by the study of [8] since the dendrograms which was obtained through the average linkage method. …”

mentioning

confidence: 92%

“…They found that the proposed methods were able to capture (a) hierarchy of interrelated clusters embedded in a complex network of objects; (b) different integration and disintegration levels amongst selected equity indices and (c) abnormal seasons and indices in terms of time and regional horizons [8]. Lahmiri (2016) investigated if cluster structures were similar by analyzing the listed firms on Casablanca Stock Exchange adopting hierarchical clustering to different sectors namely transportation, construction, banking, insurance, communications, financing, distribution and food for three market regimes so-called ups and downs, falling and rising trends during 2009, 2010, and 2011 time periods.…”

mentioning

confidence: 99%

“…Recently, a broad cluster analysis on global equity markets was carried out by Esmalifalak et al (2015) by using weekly returns of 41 indices from 40 countries over the period of March 2011 and December 2013. They found that the proposed methods were able to capture (a) hierarchy of interrelated clusters embedded in a complex network of objects; (b) different integration and disintegration levels amongst selected equity indices and (c) abnormal seasons and indices in terms of time and regional horizons [8].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Clustering in key G-7 stock market indices: an innovative approach

Peker¹,

Aktan²,

Tvaronavičienė³

2017

MMI

View full text Add to dashboard Cite

CLUSTERING IN KEY G-7 STOCK MARKET INDICES: AN INNOVATIVE APPROACHIntroduction and background. Integration among the major stock exchanges of the financial world has always been attracted attentions due mainly to globalization efforts and innovations in the sector that the financial markets have become more integrated and complicated in today's head to head competitive investment environment. However, so-called integrated and information-rich markets do not continuously follow the same patterns due mainly to the local market conditions where advance quantitative methods through econometric models are required using to forecast the behaviors. Moreover, understanding those models is almost impossible for individuals eager to making better investment decisions [4; 16; 17; 18].Clustering, which was first used as a term by Tyron (1939), is a widely held multivariate analysis which is practiced in a number of fields such as biology, medical sciences computer vision, sociology, urban planning, marketing, and so forth [7; 9; 13; 33; 34; 36;]; for example, Li et al., (2015) distinctly grouped the sports gamblers according to the demographic and behavioral characteristics in China who bought sports lottery tickets more than once in a year by developing a typology based upon the scale of assessing problem gambling [22].The method, which is relatively new and innovative approach in empirical finance, like factor analysis and multi-dimensional scaling, can be used to facilitate the selection process in portfolio management to solve abovementioned problem. In this analysis, objects or variables are separated into a small number of similar categories and relationships between objects and subjects are explored without a dependent variable being identified [5; 30]. In finance, cluster analysis unlike factor analysis is applied seldomly. The finance literature documents a small number of studies applying different clustering methods to given financial classification problems for instance, [1; 3; 6; 14; 26; 28;] and more recently, [8; 15; 19; 25]. Stock exchanges of G-7 countries can be seen as a leading group not only for their market capitalizations within the world's total value of equities but also for the solidity in today's

show abstract

mentioning

confidence: 92%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Clustering in key G-7 stock market indices: an innovative approach

Peker¹,

Aktan²,

Tvaronavičienė³

2017

MMI

View full text Add to dashboard Cite

show abstract

“…From the methodological perspective, many studies have considered modeling the correlation networks between stock returns (Kristoufek & Vosvrda, 2013;Mantegna & Stanley, 2000). An alternative approach focuses on the stocks' complex network structure via the hierarchical linkages among stocks (BAYDİLLİ, BAYIR, & TÜRKER, 2017;Boginski, Butenko, & Pardalos, 2006;Esmalifalak, Ajirlou, Behrouz, & Esmalifalak, 2015;Kantar, Deviren, & Keskin, 2011;Mantegna, 1999;Ulusoy et al, 2012). These networks and the extracted information are analyzed and compared, using clusters, cliques and connectivity.…”

Section: Introductionmentioning

confidence: 99%

Market Moods and Network Dynamics of Stock Returns: The Bipolar Behavior

Ajirlou

Esmalifalak

et al. 2019

Journal of Behavioral Finance

Self Cite

View full text Add to dashboard Cite

We show that a simple mood-separable preference in a network study of stock returns captures a variety of stylized facts regarding stocks' provisional (ab)normal behavior. These behaviors are articulated in a multi-state complete Euclidean network model that specifies the existence, direction and magnitude of a self-organized dynamics for each individual stock during abnormal market moods. In the empirical setting, we apply suggested model along with two established visual approaches (MDS and AHC) for benchmark purposes. Results reveal different levels of erratic return dynamics for each stock and the entire market in different abnormal market moods. We model and interpret these self-organized dynamics as evidence of stocks' and market's bipolar behavior.

show abstract

“…This development was encouraged by the globalization of the economy and, especially in the wake of the global financial crisis (Esmalifalak et al, 2015). Nowadays there even exist studies about the role social media plays in stock movement predition (Nguyen et al, 2015).…”

Section: Resummentioning

confidence: 99%

Pattern Recognition Applied to Chart Analysis. Evidence From Intraday International Stock Markets

Michniuk¹

View full text Add to dashboard Cite

(Dis)integration levels across global stock markets: A multidimensional scaling and cluster analysis

Cited by 17 publications

References 40 publications

Clustering in key G-7 stock market indices: an innovative approach

Clustering in key G-7 stock market indices: an innovative approach

Market Moods and Network Dynamics of Stock Returns: The Bipolar Behavior

Pattern Recognition Applied to Chart Analysis. Evidence From Intraday International Stock Markets

Contact Info

Product

Resources

About