Using discriminant analysis for credit decision

• Author: Dinca G., Bociu M.
• Pages: 277-288

Bulletin of the Transilvania University of Braşov

Series V: Economic Sciences • Vol. 8 (57) No. 2 - 2015

Using discriminant analysis for credit decision

Gheorghiţa DINC1, Mdlina BOCIU2

Abstract: This paper follows to highlight the link between the results obtained applying

discriminant analysis and lending decision. For this purpose, we have carried out the

research on a sample of 24 Romanian private companies, pertaining to 12 different

economic sectors, from I and II categories of Bucharest Stock Exchange, for the period

2010-2012. Our study works with two popular bankruptcy risk’s prediction models, the

Altman model and the Anghel model. We have double-checked and confirmed the results of

our research by comparing the results from applying the two fore-mentioned models as well

as by checking existing debt commitments of each analyzed company to credit institutions

during the 2010-2012 period. The aim of this paper was the classification of studied

companies into potential bankrupt and non-bankrupt, to assist credit institutions in their

decision to grant credit, understanding the approval or rejection algorithm of loan

applications and even help potential investors in these companies.

Key-words: discriminant analysis, bankruptcy risk, the Z score function, the A score

function, lending decision

1. Introduction

Discriminant analysis is a classification algorithm able to predict the categories a

new item with similar characteristics can be placed in (Sueyoshi, 2006).

Why is the discriminant analysis important? Because it can be applied in

many areas, for example the decision to grant credit to individuals and legal entities,

the decision to invest financial resources in a company’s shares of stock or bonds as

well as each individual’s social life.

How can we use discriminant analysis in the lending decision? This paper

aims to apply discriminant analysis on a sample of 24 Romanian companies from 12

different areas to identify the categories in which they fit and how this rating

influenced or may influence the decision to grant credit to these companies. Our

study started from the premise of using this analysis by the credit institutions.

1 Faculty of Economic Sciences and Business Administration, Transilvania University of Brasov,

Romania, gheorghita.dinca@unitbv.ro

2 Faculty of Economic Sciences and Business Administration, Transilvania University of Brasov,

Romania, master student