Abstract:
Global financial indices play a prominent role in the prior identification of
future economic growth and shrinking trends, developing financial strategies,
and reviewing financial and economic policies. Annual financial indices
published by the International Monetary Fund [IMF] associated with the 2020
financial year gauging the development of financial institutions and markets
in terms of depth, accessibility, and efficiency were considered. In this study,
hierarchical clustering was carried out through two main approaches to
identify recurring economic and financial patterns and development trends
across countries and territories. Clustering was initially implemented with all
six financial indices using the traditional agglomerative nesting (AGNES)
algorithm. Subsequently, clustering was executed through AGNES after
conducting dimensional reduction in financial indices through principal
component analysis (PCA). Internal validation measures: connectivity,
Silhouette and Dunn index were improved by 46.45%, 39.48% and 96.55%,
respectively through AGNES with PCA (AGNES-PCA) compared to the
AGNES without PCA. All stability measures, including average proportion of
non-overlaps (APN), average distance (AD) between data points, average
distance between means (ADM), and figure of merit (FOM) were increased
by 91.55%, 230.81%, 26.78%, and 54.42% respectively during AGNES-PCA.
Unstable economies like Bangladesh, Sri Lanka, El Salvador, Estonia,
Honduras, Lebanon, Nigeria, Pakistan, Tunisia, Venezuela, and Zambia were
clustered into a common group during AGNES-PCA. This study is helpful in
effectively identifying the best clustering method for a group in stable and
unstable economies around the globe. Moreover, clustering financial indices
using AGNES-PCA could be used to identify future financial and economic
crises.
