scholarly journals Financial performance of Turkish banks: a cluster analysis

Pressacademia ◽  
2021 ◽  
Vol 8 ◽  
pp. 184-189
Author(s):  
Melike Betul Tavsanli ◽  
Tarkan Hamlaci
Keyword(s):  
Cluster Analysis ◽  
Financial Performance

scholarly journals Environmental, social, governance (ESG), and financial performance of European banks

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Oana Marina Bătae ◽  
Voicu Dan Dragomir ◽  
Liliana Feleagă
Keyword(s):  
Cluster Analysis ◽  
Financial Performance ◽  
Banking Sector ◽  
Research Question ◽  
Large Population ◽  
Audit Fees ◽  
Future Research ◽  
Thomson Reuters ◽  
Geographical Regions ◽  
European Banks

Research Question: How do ESG and financial performance indicators vary according to different classifications of European banks? Motivation: Banks’ ESG performance and its relationship with corporate financial performance represents a field of continuous interest for researchers and practitioners. The results of previous studies are still mixed, either positive, negative, or even neutral. What’s new? The novelty of this paper is represented by the statistical comparison of variables that measure the ESG and financial performance of European banks based on three classifications that we propose (i.e. the geographical regions of Europe, functional currency, and cluster analysis on GDP and population of European countries, respectively). To the best of our knowledge, there are no studies applied to the banking sector, analyzing the selected variables between groups of banks according to the aforementioned classifications. So what? We contribute to the field by extending Thomson Reuters’ grouping of banks (Emerging and Developed Europe) with three more classifications. The comparison of ESG and financial performance data contributes to practice by highlighting which parts of Europe contain the banks with the highest and respectively the lowest values of ESG and financial performance, controversies, and audit fees. Therefore, the results will help investors, policymakers, regulatory bodies, bank managers, and auditors to acknowledge the significant differences within Europe and adopt appropriate measures that could improve the financial and sustainability performance of banks. Data: We collect data from Thomson Reuters Eikon, World Bank statistics, and EuroVoc for 108 European banks (81 from Developed Europe and 27 for Emerging Europe) for 2018, the most recent year on which all information is available. Tools: We conduct a cluster analysis on the macroeconomic variables of the study: the GDP per capita and the population. We used group tests and the ANOVA test as methods in analyzing the results. Findings and Contribution: We contribute with a quantitative study that fills the gap in the literature regarding significant differences that are obtained in ESG and financial performance of banks classified as Developed Europe versus Emerging Europe; Eurozone versus non-Euro countries; Western, CEE, Northern, and Southern banks; small GDP – large population and large GDP – small population clusters. Our methodology will improve future research in adopting better and more transparent classifications of companies analyzed at an international level.

scholarly journals How to measure organizational performance in search for factors of competitiveness

2013 ◽  
Vol 61 (2) ◽  
pp. 451-461 ◽  
Author(s):  
Jana Pokorná ◽  
Ondřej Částek
Keyword(s):  
Cluster Analysis ◽  
Financial Performance ◽  
Organizational Performance ◽  
Financial Indicators ◽  
Forward Selection ◽  
Return On Assets ◽  
Factors Affecting ◽  
Sequential Floating Forward Selection

The aim of this paper is to find an appropriate method of expressing a company’s performance in order to offer it to researchers for the purpose of subsequent searches for factors affecting corporate competitiveness.Of the possible approaches to performance measuring, and after considering their advantages and limitations, we have chosen long-term financial indicators, Assets Growth and Return on Assets, because each of these indicators represents one of two possible strategies to improve financial performance.This article thus presents the alternatives that are offered for that purpose as well as several means of using selected indicators (cluster analysis, etc.). While verifying the suitability of the various means, we assumed that the better the financial performance is expressed, the higher the accuracy of methods seeking competitiveness factors will be under otherwise similar conditions.We have employed the Sequential Floating Forward Selection (SFFS) as the appropriate factors seeking method, which has already been used for similar types of tasks in other fields. The best results of expressing a company’s performance were achieved using the method of adding the standardized values of both indicators.

scholarly journals An Exploratory Study of Financial Performance in CEE Countries

2020 ◽  
Author(s):  
Stefania Cristina Curea ◽  
Lucian Belascu ◽  
Ana-Maria Barsan
Keyword(s):  
Cluster Analysis ◽  
Financial Performance ◽  
Performance Indicators ◽  
Global Financial Crisis ◽  
Country Of Origin ◽  
Homogeneous Groups ◽  
Return On Equity ◽  
Global Performance ◽  
Business Approach ◽  
The Global Financial Crisis

Our research investigates the performance of companies from Central and Eastern European (CEE) countries in the period after the Global Financial Crisis of 2007-2009 with the aim of identifying the driving factors behind accounting- and market-based performance. We include in the analysis companies from various industries in CEE countries that are European Union members and we study their performance between 2008-2016 over the following areas of performance:  liquidity,  solvency and indebtedness, operational profitability, global performance (through Return on assets and Return on equity), returns available to shareholders and market-based performance (through price/book value and Tobin Q ratio). Employing the hierarchical and non-hierarchical k-means cluster analysis companies are segmented into various homogeneous groups using various financial performance indicators as variables, Euclidian distances and the Ward amalgamation method. Furthermore, the resulting clusters have been grouped according to the country of origin and industry. Our findings show that specific groups of companies in these countries share common attributes, as evidenced by their performance indicators, which do not seem to be entirely based on their countries of origin and industry. Moreover, our exploration of CEE companies’ performance dynamics after 2008 evidences the increased competition in all industries particularly after 2009, as well as businesses’ need to adjust their activities after the losses incurred during the crisis period, but these phenomena is present with different intensities depending on country of origin and industry. At the same, we note the enhancement of global performance through improvements in the operational performance instead of financial leverage and indebtedness, which is a sound business approach by CEE companies. Keywords: financial performance, Central and Eastern Europe (CEE), statistical cluster analysis, return to investors

scholarly journals Evaluation of the Financial Performance of the Municipalities in Slovakia in the Context of Multidimensional Statistics

2021 ◽  
Vol 14 (12) ◽  
pp. 570
Author(s):  
Jozef Lukáč ◽  
Katarína Teplická ◽  
Katarína Čulková ◽  
Daniela Hrehová
Keyword(s):  
Cluster Analysis ◽  
Financial Performance ◽  
Management Practices ◽  
Public Organizations ◽  
Financial Indicators ◽  
Financial Health ◽  
Managerial Control ◽  
Clustering Method ◽  
Financial Situation ◽  
Multidimensional Statistics

In some studies, only financial aspects are emphasized, but we also see cases of assessing the financial health of municipalities through socio-economic indicators. Public organizations worldwide have had to increase their financial performance by adopting management practices. Nonetheless, financial performance might be mostly predicted by contingencies that are not within direct managerial control. The purpose of this paper is to identify clusters of municipalities on the basis of agglomerate cluster analysis, the results of which will point to the financial situation of the municipalities in the selected region. The main aim of this contribution is to identify the location of the municipalities of the chosen self-governing region of Slovakia using the clustering method by selected financial indicators. Individual clusters have similar properties and they differ from the characteristics of businesses in other clusters. The results show that organizational and environmental contingencies affect financial performance, but a significant amount of variation in financial performance is unexplained—indicating that management creates better financial health in the municipality and creates a clearer budget for the management, employees, and residents of the municipality.

The impact of corporate governance on financial performance of Indian hotels: cluster analysis

2022 ◽  
Vol 15 (1) ◽  
pp. 24
Author(s):  
Najib H.S. Farhan ◽  
Faozi A. Almaqtari ◽  
Mosab I. Tabash ◽  
Eissa A. Al Homaidi
Keyword(s):  
Cluster Analysis ◽  
Corporate Governance ◽  
Financial Performance ◽  
The Impact

scholarly journals Financial performance of Hungarian and Romanian retail food small businesses

2020 ◽  
Vol 122 (11) ◽  
pp. 3451-3471
Author(s):  
Veronika Fenyves ◽  
Kinga Emese Zsido ◽  
Ioan Bircea ◽  
Tibor Tarnoczi
Keyword(s):  
Cluster Analysis ◽  
Financial Performance ◽  
Small Businesses ◽  
Financial Analysis ◽  
Univariate Analysis ◽  
Individual Characteristics ◽  
Limiting Factor ◽  
Food Retailing ◽  
Content Type ◽  
Retail Food

PurposeChanges in food retailing (globalization, concentration) have negative impacts on smaller, “traditional” food retail businesses. Their market share decreasing year by year. The purpose of this study is to examine and compare the financial performances of these businesses under the given circumstances and current economic environment in a Hungarian and a Romanian county.Design/methodology/approachThe study is based on two complete databases, including all companies that behoove retail food activity (considering the NACE cod) in the counties of Hajdu-Bihar (Hungary) and Cluj (Romania). The database analyzed contains the financial statements for five consecutive years for 212 and 690 businesses. Databases were examined by the most typical financial indicators using the multivariate and univariate analysis of variance and the k-medoid cluster analysis methods.FindingsThe results of the analysis have shown that there are differences in the number of retail food companies in the case of two counties, both in number and in financial performance. Companies in Hajdú-Bihar county perform better in terms of financial ratios than those in Cluj county. The groups created by k-medoids cluster analysis are relatively well distinguished in the case of Hajdú-Bihar county, while the picture is much more mixed in the case of Kolozs county. However, it is also important to note that the companies analyzed should generally perform better to survive.Research limitations/implicationsAmong the limitations of the study, it is important to note that the findings are relevant only to the two counties examined. Another limiting factor is that quite several companies had to be excluded from the analysis due to missing data or outliers.Practical implicationsThe study presents for the corporate decision-makers the current performance of the companies of the sector examined in the two counties. The results of the study highlight the business areas of concern in management. The findings show that they need to change this performance to strengthen their market position. We believe that it is not enough to complain about the expansion of the supermarket chains, but they should take appropriate actions to improve their situation. Based on the results of the study, it can be concluded that there is a need to improve the financial efficiency of retail food companies in both counties to survive in the long run. This improvement is essential because retailers can play an important role in smaller settlements and narrower residential environments.Originality/valueComparative analysis of retail food companies in similar counties in these two neighboring countries has not been conducted using complex financial analysis. The study revealed the common and/or individual characteristics of these companies.

scholarly journals Clustering of agricultural enterprises

2013 ◽  
Vol 61 (2) ◽  
pp. 289-296 ◽  
Author(s):  
Michaela Beranová ◽  
Marcela Basovníková ◽  
Dana Martinovičová
Keyword(s):  
Cluster Analysis ◽  
Financial Performance ◽  
Economic Value ◽  
Value Added ◽  
Economic Value Added ◽  
Agricultural Commodities ◽  
Agricultural Enterprises ◽  
Operating Profit ◽  
Agricultural Business ◽  
Business Entities

Agricultural business is a very specific branch which is characterized by very low financial performance while this characteristic is given mainly by external factors as market pricing of agricultural commodities on one side, and production costs of agricultural commodities on the other side. This way, agricultural enterprises recognize negative values of gross margin in the Profit and Loss Statement but positive value of operating profit after even there are items of costs which are deducted. These results are derived from agricultural production subsidies which are recognized as income in the P/L Statement. In connection with this fact, the government subsidies are a substantial component of financial performance of agricultural enterprises.Primary research proceeded on the statistical sample of one hundred agricultural companies, has shown that also other specifics influencing financial performance of these businesses exist here. In order to determine the influences, the cluster analysis has been applied at using more than 10 variables. This approach has led to construction of clusters (groups) of agricultural business entities with different characteristics of the group. The objective of this paper is to identify the main determinants of financial performance of agricultural enterprises and to determine their influences under different economic characteristics of these business entities. For this purpose, the regression analysis has been subsequently applied on the groups of companies coming out from the cluster analysis. Besides the operating profit which is the main driving force of financial performance measured with the economic value added (EVA) in agricultural enterprises, also capital structure and cost of capital have been observed as very strong influences on financial performance but these factors have different directions of their influence on the economic value added under different financial characteristics of agricultural enterprises.

The impact of corporate governance on financial performance of Indian hotels: cluster analysis

2022 ◽  
Vol 15 (1) ◽  
pp. 24
Author(s):  
Najib H.S. Farhan ◽  
Faozi A. Almaqtari ◽  
Mosab I. Tabash ◽  
Eissa A. Al Homaidi
Keyword(s):  
Cluster Analysis ◽  
Corporate Governance ◽  
Financial Performance ◽  
The Impact

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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