A Comparison of Clustering Algorithms in Categorizing Economic Events Based on the Behavior of Exchange Rates

Abstract

Cluster analysis is used to identify dissimilar subgroups of objects out of a set of objects based on a combination of rules. In the light of cluster analysis, it is possible to treat dissimilar individuals in an appropriate manner by taking their dissimilarity into consideration. This will be resulted in enhancing the accuracy and efficiency of estimation and prediction models. This study aims to evaluate the performance of different partitioning methods namely, k-means, k-medoids (PAM) and fuzzy and hierarchical methods namely, agglomerative nesting and divisive analysis in grouping the economic events affecting the foreign exchange market. Cluster analysis performed on economic indicators data set depicts the structure of clusters resulted from all algorithms are the same except the single linkage of agglomerative nesting. Poor quality of the clustering structure formed by the single linkage method is confirmed by the lower value of average silhouette width. Comparatively high value of agglomerative coefficient associated with the ward’s method reveals the better performance of clustering compared to other linkages. Economic indicators under study are found to be clustered in three groups as performing high, moderate and low impact on the movements of exchange rates. High impact of economic indicators on the exchange rates is reflected by the high volatility at release time and shorter prevailing time of the impact after the release.