Forming Appropriate Peer Groups for Bank Research: A Cluster Analysis of Bank Financial Statements

2018 ◽  
Author(s):  
Ken B. Cyree ◽  
Travis Davidson ◽  
John D. Stowe
Keyword(s):  
Cluster Analysis ◽  
Peer Groups ◽  
Financial Statements

scholarly journals Risk analysis of companies’ activities on the basis of non-financial and financial statements

2021 ◽  
Vol 7 (4) ◽  
pp. 180-199
Author(s):  
Karina Nazarova ◽  
Kostiantyn Bezverkhyi ◽  
Volodymyr Hordopolov ◽  
Tetiana Melnyk ◽  
Natalіia Poddubna
Keyword(s):  
Cluster Analysis ◽  
Risk Analysis ◽  
Financial Reporting ◽  
Economic Activity ◽  
Food Industry ◽  
Liquidity Risk ◽  
Financial Statements ◽  
Financial Reports ◽  
Coverage Ratio ◽  
Disclosure Of Information

Purpose. The purpose of the article is to study the degree of disclosure of information about the risks of economic activity of enterprises in non-financial statements and to find ways to improve the organization and methods of analysis of such risks based on the financial statements of companies. Methodology / approach. The methodological basis of the study is a systematic approach, methods of generalization, comparison, abstraction, analysis, synthesis, induction and deduction, bibliometric analysis, cluster analysis, as well as methods of integrated economic analysis. The publications from the Scopus database, for 1988–01.04.2021, processed by VOSviewer software were the source of data for bibliographic and cluster analysis. The materials of the research on the state of disclosure of information about the risks of domestic enterprises of the food industry are based on their financial statements and management report. Results. The article analyzes the state and degree of disclosure of information about the risks of economic activity in non-financial reports of domestic food industry enterprises. It is established that the enterprises of the studied industry most often provide information about the following risks: economic, currency, financial, political, legal, judicial, interest, personnel, price, commercial, as well as liquidity risk and market risk. Originality / scientific novelty. Theoretical, methodological and organizational principles of risk analysis of companies based on non-financial and financial reports have been further developed. For the first time, we proposed our own approach to the methodology of analysis of such risks, based on financial reporting indicators. In particular, such analytical indicators include: financial risk – solvency, financial leverage; credit risk – investment coverage ratio, return on equity, return on assets; liquidity risk – coverage ratio, quick liquidity ratio, absolute liquidity ratio. Practical value / implications. The practical value of the research is that the results obtained by the authors will contribute to the disclosure of information about the risks of economic activity of enterprises in non-financial statements. Analysis of the status and level of disclosure of information about the risks of domestic food industry enterprises in non-financial reports showed that most companies provided information about the following risks: economic, currency, financial, political, legal, judicial, interest, personnel, price, commercial, and risk liquidity and the risk of changes in market conditions. As a result, the author's methodological approach to the analysis of internal risks of the studied industry (credit, financial risks, liquidity risk) is proposed. The proposed methodology is based on the indicators of financial reporting and is part of a comprehensive risk analysis of the enterprise for the purposes of the risk management system.

Applications of Data Analytics: Cluster Analysis of Not-for-Profit Data

2021 ◽  
Author(s):  
Zamil S. Alzamil ◽  
Deniz Appelbaum ◽  
William Glasgall ◽  
Miklos A. Vasarhelyi
Keyword(s):  
Cluster Analysis ◽  
Data Analytics ◽  
Design Science ◽  
Science Research ◽  
Financial Statements ◽  
Financial Reports ◽  
For Profit ◽  
Not For Profit ◽  
Research Initiative ◽  
Survey Results

ABSTRACT: Since data analytics enables data exploration and the uncovering of hidden relationships, in this study we use cluster analysis to gain more insight into governmental data and financial reports. This research initiative is performed using the Design Science Research (DSR) methodology, where we develop and apply an appropriate artifact. We apply our artifact to two different datasets: the first uses the U.S. states’ financial statements, and the second utilizes survey results from the Volcker Alliance about states’ budgeting performance. In both applications, we demonstrate how clustering may be used on governmental data to gain new insights about financial statements and budgeting. This study contributes to the literature in two ways: First, the two applications bring advanced data mining techniques into the not-for-profit domain; and second, the results provide guidance for auditors, academics, regulators, and practitioners to use clustering to gain more insights.

scholarly journals Using Kohonen networks in the analysis of transport companies in the Czech Republic

2019 ◽  
Vol 61 ◽  
pp. 01010 ◽  
Author(s):  
Tomáš Krulický
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Czech Republic ◽  
Financial Statements ◽  
Transport Sector ◽  
The Czech Republic ◽  
Operating Profit ◽  
Kohonen Networks ◽  
Kohonen Map ◽  
Further Development

The transport sector has a significant impact on the performance of the Czech economy. Transport companies, of course, have their own specificities, whether they deal with ecology or the financial and economic situation. It is precisely the economic position of a transport company that needs to be analysed in order to identify the need for change, to predict the further development of such company. For analysis, a variety of methods is used, of which artificial neural networks are a very interesting and effective tool. The aim of this paper is to make a cluster analysis of transport companies operating in the Czech Republic based on this tool. The data of the financial statements of transport companies in the Czech Republic in 2016 are taken into account. Only some items from the financial statements are selected for analysis. The file is then subjected to a cluster analysis, specifically using the Kohonen networks – Statistica software. In accordance with the methodology of the contribution, the data is divided into three sets - training, testing and validation. Companies were divided into clusters in the 10x10 Kohonen Map. Some clusters are significant in terms of number of companies. These clusters are further analysed. Specific conclusions are made: A larger company generates, on average, a higher operating profit, larger companies achieve higher ROE and, in the case of a larger company, the financial leverage acts more positively.

scholarly journals The application of Kohonen networks for identification of leaders in the trade sector in Czechia

Equilibrium ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 739-761 ◽  
Author(s):  
Jaromír Vrbka ◽  
Elvira Nica ◽  
Ivana Podhorská
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Czech Republic ◽  
Value Added ◽  
Financial Statements ◽  
Data Set ◽  
The Czech Republic ◽  
Return On Equity ◽  
Kohonen Networks ◽  
Trade Sector

Research background: The trade sector is considered to be the power of economy, in developing countries in particular. With regard to the Czech Republic, this field of the national economy constitutes the second most significant employer and, at the same time, the second most significant contributor to GNP. Apart from traditional methods of business analyzing and identifying leaders, artificial neural networks are widely used. These networks have become more popular in the field of economy, although their potential has yet to be fully exploited. Purpose of the article: The aim of this article is to analyze the trade sector in the Czech Republic using Kohonen networks and to identify the leaders in this field. Methods: The data set consists of complete financial statements of 11,604 enterprises that engaged in trade activities in the Czech Republic in 2016. The data set is subjected to cluster analysis using Kohonen networks. Individual clusters are subjected to the analysis of absolute indicators and return on equity which, apart from other, shows a special attraction of individual clusters to potential investors. Average and absolute quantities of individual clusters are also analyzed, which means that the most successful clusters of enterprises in the trade sector are indicated. Findings & Value added: The results show that a relatively small group of enter-prises enormously influences the development of the trade sector, including the whole economy. The results of analyzing 319 enterprises showed that it is possible to predict the future development of the trade sector. Nevertheless, it is also evident that the trade sector did not go well in 2016, which means that investments of owners are minimal.

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.

Measuring “Language Access Profiles” in Deaf and Hard-of-Hearing Children With the DHH Language Exposure Assessment Tool

2020 ◽  
pp. 1-25
Author(s):  
Matthew L. Hall ◽  
Stephanie De Anda
Keyword(s):  
Cluster Analysis ◽  
Exposure Assessment ◽  
Hard Of Hearing ◽  
Assessment Tool ◽  
Communication Mode ◽  
Developmental Potential ◽  
Language Access ◽  
Retest Reliability ◽  
Language Exposure ◽  
Test Retest Reliability

Purpose The purposes of this study were (a) to introduce “language access profiles” as a viable alternative construct to “communication mode” for describing experience with language input during early childhood for deaf and hard-of-hearing (DHH) children; (b) to describe the development of a new tool for measuring DHH children's language access profiles during infancy and toddlerhood; and (c) to evaluate the novelty, reliability, and validity of this tool. Method We adapted an existing retrospective parent report measure of early language experience (the Language Exposure Assessment Tool) to make it suitable for use with DHH populations. We administered the adapted instrument (DHH Language Exposure Assessment Tool [D-LEAT]) to the caregivers of 105 DHH children aged 12 years and younger. To measure convergent validity, we also administered another novel instrument: the Language Access Profile Tool. To measure test–retest reliability, half of the participants were interviewed again after 1 month. We identified groups of children with similar language access profiles by using hierarchical cluster analysis. Results The D-LEAT revealed DHH children's diverse experiences with access to language during infancy and toddlerhood. Cluster analysis groupings were markedly different from those derived from more traditional grouping rules (e.g., communication modes). Test–retest reliability was good, especially for the same-interviewer condition. Content, convergent, and face validity were strong. Conclusions To optimize DHH children's developmental potential, stakeholders who work at the individual and population levels would benefit from replacing communication mode with language access profiles. The D-LEAT is the first tool that aims to measure this novel construct. Despite limitations that future work aims to address, the present results demonstrate that the D-LEAT represents progress over the status quo.

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