Constantin Zopounidis

Multicriteria classification and sorting methods: A literature review

Abstract The assignment of alternatives (observations/objects) into predefined homogenous groups is a problem of major practical and research interest. This type of problem is referred to as classification or sorting, depending on whether the groups are nominal or ordinal. Methodologies for addressing classification and sorting problems have been developed from a variety of research disciplines, including statistics/econometrics, artificial intelligent and operations research. The objective of this paper is to review the research conducted on the framework of the multicriteria decision aiding (MCDA). The review covers different forms of MCDA classification/sorting models, different aspects of the model development process, as well as real-world applications of MCDA classification/sorting techniques and their software implementations.

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.

Handbook of Multicriteria Analysis

Multicriteria analysis is a rapidly growing aspect of operations research and management science, with numerous practical applications in a wide range of fields. This book presents all the recent advances in multicriteria analysis, including multicriteria optimization, goal programming, outranking methods, and disaggregation techniques. The latest developments on robustness analysis, preference elicitation, and decision making when faced with incomplete information, are also discussed, together with applications in business performance evaluation, finance, and marketing. Finally, the interactions of multicriteria analysis with other disciplines are also explored, including among others data mining, artificial intelligence, and evolutionary methods.

Multicriteria decision aid in financial management

Abstract The financial decisions of an organization are usually included in the context of optimization. Concerning a long-term period, there are decisions related to the optimal allocation of funds, and decisions related to the optimal financial structure. In the short-term case, the decisions are related to the optimization of stocks, cash, accounts receivable, current liabilities, etc. The financial theory analyzes these decisions, mainly from an optimal point of view. The optimal character of such decisions has led researchers to propose operations research techniques to solve the problems that are inherent in such decisions. This paper examines the contribution of multicriteria analysis in solving financial decision problems in a realistic context. The paper also includes an extensive bibliography on the subject.

A Multicriteria Decision Aid Methodology for Sorting Decision Problems: The Case of Financial Distress

Sorting problems constitute a major part of real world decisions, where a set of alternative actions (solutions) must be classified into two or more predefined classes. Multicriteria decision aid (MCDA) provides several methodologies, which are well adapted in sorting problems. A well known approach in MCDA is based on preference disaggregation which has already been used in ranking problems, but it is also applicable in sorting problems. The UTADIS (UTilités Additives DIScriminantes) method, a variant of the UTA method, based on the preference disaggregation approach estimates a set of additive utility functions and utility profiles using linear programming techniques in order to minimize the misclassification error between the predefined classes in sorting problems. This paper presents the application of the UTADIS method in two real world classification problems concerning the field of financial distress. The applications are derived by the studies of Slowinski and Zopounidis (1995), and Dimitras et al. (1999). The obtained results depict the superiority of the UTADIS method over discriminant analysis, and they are also comparable with the results derived by other multicriteria methods.

Detecting Falsified Financial Statements: A Comparative Study using Multicriteria Analysis and Multivariate Statistical Techniques

Falsifying financial statements involves the manipulation of financial accounts by overstating assets, sales and profit, or understating liabilities, expenses or losses. This paper explores the effectiveness of an innovative classification methodology in detecting firms that issue falsified financial statements (FFS) and the identification of the factors associated to FFS. The methodology is based on the concepts of multicriteria decision aid (MCDA) and the application of the UTADIS classification method (UTilités Additives DIScriminantes). A sample of 76 Greek firms (38 with FFS and 38 non-FFS) described over ten financial ratios is used for detecting factors associated with FFS. A jackknife procedure approach is employed for model validation and comparison with multivariate statistical techniques, namely discriminant and logit analysis. The results indicate that the proposed MCDA methodology outperforms traditional statistical techniques which are widely used for FFS detection purposes. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of FFS and highlight the importance of financial ratios such as the total debt to total assets ratio, the inventories to sales ratio, the net profit to sales ratio and the sales to total assets ratio.

A multicriteria classification approach based on pairwise comparisons

Abstract Classification refers to the assignment of a set of alternatives into predefined homogenous classes. Most of the existing classification methodologies are based on absolute comparisons among the alternatives and some reference profiles (cut-off points) that discriminate the classes. This paper proposes a new approach that involves pairwise comparisons based on the multicriteria decision aid (MCDA) paradigm. The basis of the methodology is a preference relation that is used to perform pairwise comparisons among the alternatives. The criteria weights used to construct the preference relation are specified using a reference set of alternatives (training sample) on the basis of linear programming techniques.

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.

Operational tools in the management of financial risks

I: Multivariate Data Analysis and Multicriteria Analysis in Portfolio Selection. Proposal for the Composition of a Solvent Portfolio with Chaos Theory and Data Analysis D. Karapistolis, et al. An Entropy Risk Aversion in Portfolio Selection A. Scarelli. Multicriteria Decision Making and Portfolio Management with Arbitrage Pricing Theory Ch. Hurson, N. Ricci-Xella. II: Multivariate Data Analysis and Multicriteria Analysis in Business Failure, Corporate Performance and Bank Bankruptcy. The Application of the Multi-Factor Model in the Analysis of Corporate Failure E.M. Vermeulen, et al. Multivariate Analysis for the Assessment of Corporate Performance: The Case of Greece Y. Caloghirou, et al. Stable Set Internally Maximal: A Classification Method with Overlapping A. Couturier, B. Fioleau. A Multicriteria Approach for the Analysis and Prediction of Business Failure in Greece C. Zopounidis, et al. A New Rough Set Approach to Evaluation of Bankruptcy Risk S. Greco, et al. FINCLAS: A Multicriteria Decision Support System for Financial Classification Problems C. Zopounidis, M. Doumpos. A Mathematical Approach of Determining Bank Risks Premium J. Gupta, Ph. Spieser. III: Linear and Stochastic Programming in Portfolio Management. Designing Callable Bonds Using Simulated Annealing M.R. Holmer, et al. Towards Sequential Sampling Algorithms for Dynamic Portfolio Management Z. Chen, et al. The Defeasance in the Framework of Finite Convergence in Stochastic Programming Ph. Spieser, A. Chevalier. Mathematical Programming and Risk Management of Derivative Securities L. Clewlow, et al. IV: Fuzzy Sets and Artificial Intelligence Techniques in Financial Decisions. Financial Risk in Investment J. Gil-Aluja. The Selection of a Portfolio Through a Fuzzy Genetic Algorithm: The POFUGENA Model E. Lopez-Gonzalez, et al. Predicting Interest Rates Using Artificial Neural Networks Th. Politof, D. Ulmer. V: Multicriteria Analysis in Country Risk Evaluation. Assessing Country Risk Using Multicriteria Analysis M. Doumpos, et al. Author Index.