Abstract
Performance measurement encourages Decision Making Units (DMUs) to improve their level of performance by comparing their current financial positions with that of their peers. Data Envelopment Analysis (DEA) is a widely used approach to performance measurement, though it is susceptible when the data is heterogeneous. The main objective of this study is to examine the performance of Mongolian listed companies by combining DEA and a k-medoid clustering method. Clustering facilitates the characterization and patterns of data and identification of homogenous groups. This study applies the integration of k-medoids and performance measurement. The research used 89 Mongolian companies’ financial statements from 2012 to 2015 - obtained from the Mongolian Stock Exchange website. The companies are grouped by k-medoids clustering, and efficiency of each cluster is evaluated by DEA. According to the silhouette method, the companies are classified into two clusters which are considered first cluster as small and medium-sized (80), and second cluster as big (9) companies. Both clusters are analyzed and compared by financial ratios. The mean efficiency score of big companies’ is much higher than that of small and medium-sized companies. Integrated results show that cluster-specific efficiency provides better performance than pre-clustering efficiency results.
Highlights
The basis of all types of analysis is data
The frontier scale of Data Envelopment Analysis (DEA) consists of constant return to scale (CRS) and variable return to scale (VRS)
This study evaluates the performance of Mongolian companies by integrating cluster analysis with DEA
Summary
The basis of all types of analysis is data. It is possible to face various data in everyday life, with or without any prior knowledge. Arora and Varshney (2016) compared k-means and k-medoids in their research. Their results proved that k-medoids is better than k-means; as execution time, sensitivity to outliers and space complexity of overlapping are all less. Mohammad, Zadegan, Mirzaie, and Sadoughi, (2013) examined ranked k-medoids They introduced a new k-medoids algorithm, which can find all Gaussian-shaped clusters. Jahangoshai, Rezaee, Jozmaleki, and Valipour (2018) integrated fuzzy C-means, DEA, and an artificial neural network. They obtained their data (from 2007 to 2012) from the Tehran Stock Market and used financial ratios as variables.
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