Academic journal article Fuzzy Economic Review

Rough Sets and Discriminant Analysis Techniques for Business Default Forecasting

Academic journal article Fuzzy Economic Review

Rough Sets and Discriminant Analysis Techniques for Business Default Forecasting

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1. INTRODUCTION

Many studies have addressed the problem of bankruptcy prediction and most of them do their analysis through financial ratios. Beaver (1966) utilized t-tests to evaluate the predictive ability of various financial ratios using a pair-matched sample. In a landmark paper in the bankruptcy prediction area, Altman (1968) used multiple discriminant analysis on a pair-matched sample. Altman (1968) used five financial ratios to predict whether a company is a going concern. Altman's study is very accurate in its prediction of which companies are going to go bankrupt, and the study has held up very well even after almost 35 years.

In another major study Ohlson (1980) applied logistic regression in a much larger sample that did not involve pair-matching, and he found that the four statistically significant factors for identifying the probability of failure are the size of the company, measures of financial structure, measures of performance, and measures of current liquidity.

Lennox (1999) examined the causes of bankruptcy for a sample of UK listed companies for the period 1987 to 1994 and found that the most important determinants of bankruptcy are profitability, leverage, cash flow, company size, industry sector and the economic cycle. Lennox (1999) argues that well-specified logit and probit models can be more accurate for identifying failing companies than discriminant analysis.

Barniv and McDonald (1999) discuss several alternative techniques to probit models and logit models for predicting company failure. In recent years, many studies have developed and examined new methods for predicting bankruptcy. For example, Hwang et al. (2007) and Cheng et al. (2010) use semi-parametric methods to predict bankruptcy. Serrano-Cinca and Gutiérrez-Nieto (2013) use partial lease square discriminant analysis in bankruptcy prediction. In this context, Ravi Kumar and Ravi (2007) do a deep review of the papers issued on bankruptcy forecasting and classified them into two groups, according to the usage of statistical (like the papers cited up to now) or intelligent techniques. Within the latter, the authors distinguish the following techniques: neural networks, case-based reasoned techniques, decision trees, evolutionary approaches, operational research, rough sets, support vector machines, isotonic separation, fuzzy techniques and soft computing techniques.

Within the group of the papers that use fuzzy techniques, Alam et al. (2000) propose fuzzy clustering for identifying potentially failed banks and then compared it with neural networks. De Andrés et al. (2005) propose fuzzy rule based classifiers for bankruptcy prediction problem. They compare the performance of Linear Discriminant Analysis and logistic regression with multi-layer perception and fuzzy-rule based classifiers. Korol and Korodi (2011) propose a fuzzy model (that uses financial ratios as inputs) to forecast firms' bankruptcy up to three years before. Scherger et. al. (2014) focuses their analysis on a set of Small and Medium sized Enterprises (SMEs) from the construction sector. The authors simulate a fuzzy model to forecast firms' health and find out the reasons that generate problems (diseases) to it.

Ravi Kumar and Ravi (2007) state that, among the intelligent techniques, the neural networks has been the most used family to solve the problem of bankruptcy prediction. For example, Lin and McClean (2000) conducted a study on distressed and non-distressed quoted companies listed on the London Stock Exchange for the last 20 years. Lin and McClean used different techniques in their studies such as discriminant analysis, logistic regression, neural networks and decision trees. They conclude that the latter and neural networks provide the best results. It must be underlined that, during last years, an important line of research that combines neural networks with fuzzy logic has raised. Papers like the ones of Chen et al. …

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