This paper is aimed at examining what ratios can determine financial performance of Mongolian companies which are divided into 6 major sectors to increase their competitiveness. This study analyzes the performance of companies in terms of profitability and its association with multiple determinants for 100 Mongolian joint stock companies ( JSC) listed in Mongolian Stock Exchange (MSE). Financial statements of those companies from MSE are evaluated by panel regression covering the period of 2012-2015. Return on Assets (ROA), Return on Equity (ROE), and Return on Sales (ROS) are chosen as performance indicators, while growth in sales, growth in profit, growth in assets, earnings per share, gross profit margin, cost to revenue ratio, return on costs, short-term debt to assets ratio, current assets to total assets ratio, long-term debt to total assets, quick ratio, current ratio, and cash ratio are used as explanatory variables. The panel regression results show that ROA has more determinants than ROE and ROS, such as earnings per share, return on costs have positive impacts, while short-term debts to total assets ratio and cost to revenue ratio have negative impacts. Growth in sales, earnings per share and costs to revenue ratio influence positively the financial performance of an organization by ROS, while return on cost has a positive effect on the financial performance measured by return on sale.
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.Keywords: financial performance, k-medoids clustering, data envelopment analysis, input efficiency, variable return to scale, decision making unit. JEL Classification: C38, C14, L25.ing (finding meaningful groups of objects that share common characteristics) and utility (to abstract the representative object from among many others in the same clusters) (Wu, 2012).Clustering techniques are divided into partitional and hierarchical types. The most popular and well-known partitional cluster technique is k-means, which is widely employed in research. Although k-means is a popular choice among partitional clusters, it is sensitive to outliers. On the contrary, the k-medoids algorithm is more robust and less sensitive to outliers. Research, which compared k-medoids with k-means, suggested the k-medoid was better in all aspects. For example, 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. Narayana and Vasumathi (2018) stated in their work that the k-medoids technique is more accurate and easier to understand than k-means clustering. Moreover, Patel and Singh (2013) studied a new approach for k-means and k-medoids algorithm and concluded that k-medoids improved accuracy. However, Arbin, Suhaimi, Mokhtar, and Othman (2016) evaluated k-means and k-medoids, and both methods were found to be good having mean errors less than three.The K-medoids algorithm, which was proposed by Kaufman and Rousseeuw (1987), was developed and investigated by various researchers from different fields. For example, Ho-Kieu, Vo-Van...
The aim of this paper is to examine the efficiency of Mongolian 100 public companies listed on Mongolian Stock Exchange (MSE) which are divided into 6 major sectors. This study conducts the performance of companies in terms of profitability by using three different output variables i.e., revenue, pretax profit and ROA (Return on Assets). In the beginning of this research, nine variables which are connected with profitability are chosen as output variables, while 24 variables expressing growth, financial structure, solvency, and turnover together with some fundamental financial data are chosen as input variables. 10 variables out of 24 input variables, which determine the financial structure, solvency, and profitability, are chosen as input variables based on the calculation of stepwise regression analysis. Stepwise regression, multi co-linearity analysis are made by SPSS and DEA (Data envelopment analysis) is evaluated by benchmarking package in R excel statistical program covering the period of 2012-2015. This paper uses the input-oriented version of DEA based on financial ratios and some crucial components of a financial statement. The results of DEA show that food and grocery sector was the most efficient, and mining sector was at the second place by its efficiency, while agriculture and service sector were the worse than other sectors.
Mongolia is the second largest landlocked country, which has unique economic condition. This paper aims to examine Mongolian economic growth from 2000 until 2016 and identify its determinants. The growth was studied based on the growth rate of National Domestic Product. Initially, 20 macroeconomic variables are chosen and tested for the economic growth determinators such as; unemployment rate, human capital index, import growth, inflation rate, export growth, and interest rate, etc. The results showed that the growth rate of dollar exchange, inflation rate, and the growth rate of export were the main factors (81.4%). Mongolian GDP per capita and poverty rate were compared with other Asian lower-middle-economies, which are classified in the same classification as Mongolia. An increment of average salary was adjusted by the inflation rate, which showed the purchasing power declined in 2015. Statistics of Central Bank of Mongolia, Central Intelligence Agency, World Bank’s statistics, and the statistics from National Statistics Office of Mongolia are used for the research. JEL Classification: H0, H30, H6, H70
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.