(Dis)integration levels across global stock markets: A multidimensional scaling and cluster analysis. Issue 22 (1st December 2015)

• Record Type: Journal Article
• Authors: Esmalifalak, Hamidreza; Ajirlou, Ali Irannezhad; Behrouz, Sahar Pordeli; Esmalifalak, Maryam
• Is Part Of: Expert systems with applications. Volume 42:Issue 22(2015)
• Page Start: 8393
• Page End: 8402
• Publication Date: 2015-12-01
• Subjects: (Dis)integration levels -- Global stock market (GSM) -- Multidimensional scaling (MDS) -- Agglomerative Hierarchical Clustering (AHC); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.06.053
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A visual inspection over 41 global stock indexes is conducted. We use MDS and AHC methods to detect (dis)integration levels and clusters. For each level and cluster, the abnormal indexes are detected. Using most integrated indexes, we report their linkage in 12 different time periods. Abstract: Following the globalization of the economy, interests in examining the financial linkages of economies have been increased, especially in the wake of the global financial crisis. In light of the benefits of international portfolio diversification, it is not surprising that there has been a strong interest among researchers to capture presence and extent of global stock market GSM integration. This study addresses to the feasibility and benefits of two visual data interpretation methods, based on multidimensional scaling and cluster analysis, in GSM integration context. The explanatory power of applied methods has become stronger by putting both integration and disintegration (anomaly) detection strategies. Empirically, in the first phase, we detect (dis)integration levels and clusters among 41 indexes from 40 countries. In the second phase, we choose most integrated indexes, detected in the first phase, to see whether their linkages are progressing, stable or regressing during different time series (12 seasons). In phase one and two, the topological properties of global stock networks are expressed in two-dimensional plots (dendrogram, heat map, frequency map). Based on the results, we found proposed methods capable of capturing (1) hierarchy of interrelated clusters embedded in a complex network of objects (2) different (dis)integration levels among selected global stock indexes and (3) abnormal seasons and indexes considering time and regional horizons.