Cluster analysis of the economic activity of Slovak companies regarding potential indicators of earnings management

• Main Authors: Durica Marek, Svabova Lucia
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: SHS Web of Conferences
• Subjects: cluster analysis; nace classification; earnings management; earnings manipulation; financial ratios
• Online Access: https://www.shs-conferences.org/articles/shsconf/pdf/2021/03/shsconf_glob20_07018.pdf

Research background: All over the world, any information about the earnings manipulation is very important for all the stakeholders of the companies. Therefore, it is necessary to detect this situation in a certain way. The global practice has shown that it is appropriate to create detection models and it would be very useful to specify individual sectors or the groups of sectors of economic activities of companies. Purpose of the article: The article aims to the financial ratios of Slovak companies that are globally used in the detection of earnings management. Based on hierarchical cluster analysis we identify groups of economic activities (according to the international NACE classification) with similar financial characteristics. Methods: For efficient earnings manipulation detection, high-quality and up-to-date financial data is required. We used financial data of real Slovak companies from the year 2018 obtained from international database Amadeus. After a precise pre-preparation of the dataset, we use the standard clustering procedures. Using the analysis of the dendrogram, the groups of the companies with their economic activities are identified. Findings & Value added: The results of the analysis show that there exist logical groups of NACE categories of economic activity of companies with similar characteristics. Regarding potential earnings manipulation, companies in these groups are as similar as possible. Therefore, financial characteristics can be analyzed together, and more accurate detection models could be created for them.