Augustinos I. Dimitras

Business failure prediction using rough sets

A large number of methods like discriminant analysis, logit analysis, recursive partitioning algorithm, etc., have been used in the past for the prediction of business failure. Although some of these methods lead to models with a satisfactory ability to discriminate between healthy and bankrupt firms, they suAer from some limitations, often due to the unrealistic assumption of statistical hypotheses or due to a confusing language of communication with the decision makers. This is why we have undertaken a research aiming at weakening these limitations. In this paper, the rough set approach is used to provide a set of rules able to discriminate between healthy and failing firms in order to predict business failure. Financial characteristics of a large sample of 80 Greek firms are used to derive a set of rules and to evaluate its prediction ability. The results are very encouraging, compared with those of discriminant and logit analyses, and prove the usefulness of the proposed method for business failure prediction. The rough set approach discovers relevant subsets of financial characteristics and represents in these terms all important relationships between the image of a firm and its risk of failure. The method analyses only facts hidden in the input data and communicates with the decision maker in the natural language of rules derived from his/her experience. ” 1999 Elsevier Science B.V. All rights reserved.

A survey of business failures with an emphasis on prediction methods and industrial applications

Abstract The considerable interest in the prediction of business failures is reflected in the large number of studies presented in the literature. Various methods have been used to construct prediction models. This paper provides a review of the literature and a framework for the presentation of this information. Articles can be classified according to the country, industrial sector and period of data, as well as the financial ratios and models or methods employed. Relationships and research trends in the prediction of business failure are discussed.

Prediction of company acquisition in Greece by means of the rough set approach

Abstract This paper presents a new approach to forecast the acquisition of a firm in Greece based on the rough set theory. A sample of acquired firms and a sample of equivalent non-acquired firms are considered and the objective is to create patterns which would be able to distinguish between the two classes of firms, based upon differences in their financial characteristics (financial ratios). For this purpose, the rough set approach is used. The information about the firms is organized in a financial information table. In this table, financial characteristics of the firms correspond to condition attributes and the classification is defined by a decision attribute telling if a firm has been acquired or not. The rough set approach enables one to discover minimal subsets of condition attributes (financial ratios) ensuring an acceptable approximation of the classification of the firms analyzed and to derive decision rules from the financial information table which can be used to best distinguish in the future between acquired and non-acquired firms. A comparison of the rough set approach with the discriminant analysis on the same set of data shows an advantage of the new approach.

Multicriteria Decision Aid Methods for the Prediction of Business Failure

List of Tables. List of Figures. Prologue. I: Business Failure Research: Some Statistics, Methods, Models and Variables. II: Multicriteria Decision Aid Methodology. III: ELECTRE TRI Method and Business Failure Prediction. IV: Rough Sets and Business Failure Prediction. V: Preference Disaggregation Method and Business Failure Prediction. Conclusions. References.

Multicriteria Decision Aid Methodology

The start of the of Multicriteria Analysis’s history is set to the work of Pareto (1896) where the problem of the aggregation of criteria into a single criterion was examined. The monocriterion approach was at first adopted in decision analysis. This approach, according to Roy and Vanderpooten (1996), “consisted of defining a single criterion which should represent the effectiveness of the system under study”. The need for incorporating a variety of criteria in real world’s decision problems led to the decision-aid approach called Multicriteria Analysis (MA) that has been under development for the last 27 years. This approach proceeds to the generalization of the classic optimization paradigm in the field of operations research.

ELECTRE TRI Method and Business Failure Prediction

The ELECTRE TRI method belongs to the wide family of ELECTRE methods based in the concept of outranking. The particularity of the ELECTRE family is that it refuses the possibility of total compensation between the alternative’s performance on the criteria, and then to accept incomparability and intransitivity.

Rough Sets and Business Failure Prediction

Rough set theory was introduced by Pawlak (1982). The philosophy of the method is based on the assumption that with every object some information (data, knowledge) can be associated. Objects characterized by the same information are indiscernible in view of the available information. The indiscernibility relation generated in this way is the mathematical basis for the rough set theory.

Preference Disaggregation Method and Business Failure Prediction

One of the well known approaches in Multicriteria Decision Aid is the regression based method UTA (UTilites Additives). Jacquet-Lagreze and Siskos (1978, 1982) were the first to present this method. The UTA method has been already applied in the areas of commercial strategy by Siskos (1986), evaluation of venture capital investment by Siskos and Zopounidis (1987), evaluation of country risk (Cosset et al., 1992), business financing (Siskos et al., 1994), portfolio management (Hurson and Zopounidis, 1995; Zopounidis et al., 1995), etc. In this chapter, after the presentation of the UTA method, the use of its variants UTASTAR for ranking problems and UTADIS for sorting problems in business failure, is illustrated by the study of Zopounidis (1987), through the MINORA System, and the study of Zopounidis and Doumpos (1997) respectively.

Business Failure Research: Some Statistics, Methods, Models and Variables

Corporate distress and relative predictions have been a major theme for numerous researchers and practitioners in the last decades. The relevant literature adopts a variety of terms to describe corporate distress. The most commonly used terms are “failure”, “insolvency”, “default” and “bankruptcy”. All of them assign economic and financial problems and although they are used somehow optionally, these terms provide a slightly different definition connected with the specific interest or condition of the firms under examination.