Using Principal Component Analysis in Assessing Client’s Creditworthiness
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.
KeywordsPrincipal component analysis Correlation matrix Kaiser–Meyer–Olkin test for sampling adequacy Trade credit Creditworthiness
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.
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