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Using Principal Component Analysis in Assessing Client’s Creditworthiness

  • Anna SiekelováEmail author
  • Lucia Svabova
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

A company that provides trade credit must take into account the creditworthiness of its customers. The creditworthiness of customer is largely affected by its ability to repay trade credit properly and on time. The company usually follows the main financial indicators against which it receives a basic overview of the customer’s creditworthiness. The basic indicators include indicators of activity, liquidity, indebtedness, and profitability. Within each group there are a number of indicators that can be monitored. Recommendations by authors who deal with this topic may also differ. There can be hidden relationships between the various. Monitoring two indicators among which exists a strong correlation is useless. The same amount of information can be obtained by monitoring only one of them. The aim of this paper is assessing the existence of hidden relationships between indicators that are most often recommended for the evaluation of the client’s creditworthiness. There are many methods of analysis hidden relationships. Choice of the appropriate method depends on the type and number of variables. In our group the individual objects, i.e., businesses, are described by more than two quantitative variables, so we choose principal component analysis to describe hidden relationships. It is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Keywords

Principal component analysis Correlation matrix Kaiser–Meyer–Olkin test for sampling adequacy Trade credit Creditworthiness 

Notes

Acknowledgment

This research was financially supported by the Slovak Research and Development Agency—Grant NO. APVV-14-0841: Comprehensive Prediction Model of the Financial Health of Slovak Companies.

References

  1. Corejova, T., & Al Kassiri, M. (2016). Knowledge-intensive business services as important services for innovation and economic growth in Slovakia. In CBU International Conference on Innovations in Science and Education (CBUIC) (pp. 42–47). Prague.Google Scholar
  2. Kadlecik, K., & Markovic, P. (2015). Hodnotenie kreditného rizika odberateľa. Bratislava: Wolters Kluwer.Google Scholar
  3. Kliestik, T., & Majerova, J. (2015). Selected issues of selection of significant variables in the prediction models. In Financial Management of Firms and Financial Institutions: 10th International Scientific Conference (pp. 537–543). Ostrava.Google Scholar
  4. Kliestik, T., Majerova, J., & Lyakin, A. N. (2015). Metamorphoses and semantics of corporate failures as a basal assumption of a well-founded prediction of a corporate financial health. In Economics and Social Science: 3rd International Conference on Economics and Social Science (ICESS 2015) (pp. 150–154). Paris.Google Scholar
  5. Petrach, F., & Vochozka, M. (2016). Optimization of a company’s capital structure: Global problem of the corporate finance and its possible solutions. In 16th International Scientific Conference on Globalization and its Socio-Economic Consequences (pp. 1696–1703). Rajecké Teplice.Google Scholar
  6. Salek, J. (2005). Account receivable management. Hoboken, NJ: Wiley.Google Scholar
  7. Stankovičová, I., & Vojtková, M. (2007). Viacrozmerné štatistické metódy s aplikáciami. Bratislava: Iura Edition.Google Scholar
  8. Svabova, L., & Durica, M. (2016). Korelačná analýza prediktorov použitých v bankrotných predikčných modeloch na Slovensku. Ekonomicko manažerské spektrum, 10(1), 2–11.Google Scholar
  9. Weissova, I., & Durica, M. (2016). The possibility of using prediction models for monitoring the financial health of Slovak companies. In 8th International Scientific Conference Managing and Modelling of Financial Risks (pp. 1062–1070). Ostrava.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Operation and Economics of Transport and Communications, Department of EconomicsUniversity of ZilinaZilinaSlovakia

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