scholarly journals Discriminant Methods for Bankruptcy Prediction - Theory and Applications

Ekonomika ◽  
2005 ◽  
Vol 72 ◽  
Author(s):  
Józef Pociecha
Keyword(s):  
Discriminant Analysis ◽  
Discriminant Function ◽  
Analytical Form ◽  
Bankruptcy Prediction ◽  
Bankruptcy Risk ◽  
Linear Discriminant ◽  
Estimation Problems ◽  
Methodological Problems ◽  
Polish Economy ◽  
The Stability

Discriminant analysis consists of assigning an individual to two (or more) distinct populations, on the basis of observations of several characters of the individuals and a sample of observations of these characters from the populations. R. A. Fisher suggested a linear function of variables representing different characters, called linear discriminant function, for classifying an individual into one of the two populations. E. I. Altman adapted this approach to identify bankruptcy risk of corporations. Altman’s model of bankruptcy was estimated for various countries, thereby for Polish economy. Some results of estimation and interpretation of Altman’s model for Polish economy are presented in the paper. Methodological problems of discriminant analysis, especially fulfilling the basic assumptions, the analytical form of the discriminant function, the stability of the model and the estimation problems are also discussed.

scholarly journals Bankruptcy Prediction: The Case of the Greek Market

Forecasting ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 505-525
Author(s):  
Angeliki Papana ◽  
Anastasia Spyridou
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Emerging Economies ◽  
Statistical Models ◽  
Bankruptcy Prediction ◽  
The Other ◽  
Linear Discriminant ◽  
Business Failures ◽  
Using Data ◽  
Of Greece

Financial bankruptcy prediction is an essential issue in emerging economies taking into consideration the economic upheaval that can be caused by business failures. The research on bankruptcy prediction is of the utmost importance as it aims to build statistical models that can distinguish healthy firms from financially distressed ones. This paper explores the applicability of the four most used approaches to predict financial bankruptcy using data concerning the case of Greece. A comparison of linear discriminant analysis, logit, decision trees and neural networks is performed. The results show that discriminant analysis is slightly superior to the other methods.

scholarly journals ESTIMATING THE PROBABILITY OF MISCLASSIFICATIONS IN TWO-GROUPS DISCRIMINANT ANALYSIS

2004 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
I W. MANGKU
Keyword(s):  
Discriminant Analysis ◽  
Discriminant Function ◽  
Comparative Studies ◽  
Linear Discriminant Function ◽  
Classification Rule ◽  
Survey Study ◽  
Linear Discriminant ◽  
Empirical Estimators

This paper is a survey study on estimation of the pro- bability of misclassifications in two-groups discriminant analysis using the linear discriminant function as the classification rule. Here we consider two groups of estimators, namely parametric esti- mators and empirical estimators. The results of some comparative studies on the performances of the considered estimators are also discussed.

scholarly journals Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis

2020 ◽  
Vol 13 (3) ◽  
pp. 58 ◽  
Author(s):  
Nicoleta Bărbuță-Mișu ◽  
Mara Madaleno
Keyword(s):  
Discriminant Analysis ◽  
Firm Performance ◽  
Performance Indicator ◽  
Generalized Method Of Moments ◽  
Principal Component ◽  
Dynamic Panel Data ◽  
Bankruptcy Prediction ◽  
Risk Scores ◽  
Estimation Model ◽  
Bankruptcy Risk

Assessment and estimation of bankruptcy risk is important for managers in decision making for improving a firm’s financial performance, but also important for investors that consider it prior to making investment decision in equity or bonds, creditors and company itself. The aim of this paper is to improve the knowledge of bankruptcy prediction of companies and to analyse the predictive capacity of factor analysis using as basis the discriminant analysis and the following five models for assessing bankruptcy risk: Altman, Conan and Holder, Tafler, Springate and Zmijewski. Stata software was used for studying the effect of performance over risk and bankruptcy scores were obtained by year of analysis and country. Data used for non-financial large companies from European Union were provided by Amadeus database for the period 2006–2015. In order to analyse the effects of risk score over firm performance, we have applied a dynamic panel-data estimation model, with Generalized Method of Moments (GMM) estimators to regress firm performance indicator over risk by year and we have used Tobit models to infer about the influence of company performance measures over general bankruptcy risk scores. The results show that the Principal Component Analysis (PCA) used to build a bankruptcy risk scored based on discriminant analysis indices is effective for determining the influence of corporate performance over risk.

scholarly journals Bankruptcy Prediction Models Based on Value Measures

2020 ◽  
Vol 14 (1) ◽  
pp. 6
Author(s):  
Andrzej Jaki ◽  
Wojciech Ćwięk
Keyword(s):  
Discriminant Function ◽  
Prediction Models ◽  
Statistical Significance ◽  
Bankruptcy Prediction ◽  
Warning Signs ◽  
Discriminant Functions ◽  
Corporate Risk Management ◽  
Bankruptcy Risk ◽  
Corporate Risk ◽  
Diagnostic Variables

In the existing studies devoted to predicting bankruptcy, the authors of such models only used book measures. Considering the fact that the evolution of corporate measure efficiency (in addition to book measures) brought into existence and exposed the importance of cash measures, market measures, and measures based on the economic profit concept, it is justified to carry out research into the possibility of using these measures as variables within the discriminant function. The studied dataset was divided into a training set and a testing set based on two variants of the sample division. The assessment of the statistical significance of the built discriminant functions as well as the diagnostic variables was conducted using the STATISTICA package. The research was conducted separately for each variant. In the first step, a total of 30 discriminant models were created. This enabled us to select 20 diagnostic variables that were considered within the two models that were characterised by the highest predictive abilities—one for each variant. The discriminant function that was estimated for the first variant was based on the use of eight diagnostic variables, and 13 diagnostic variables were used in the function that was estimated for the second variant. The conducted analysis has proven that shareholder value measures are a useful tool that can be applied for the needs of corporate risk management in the area of the assessment of a firm’s bankruptcy risk. Using two variants of the division of the research sample into the training and testing sets, it turned out that the division affects the predictive efficiency of the discriminant functions. At the same time, the obtained findings tend to claim that the presence of the value measures from all four of the studied groups in the output set of the diagnostic variables is necessary for possibly building the most efficient tool for the early warning signs of bankruptcy risk.

Comparative Analysis of Selected Methods for Estimating the Prediction Error of Classifie

2016 ◽  
Vol 63 (4) ◽  
pp. 449-463
Author(s):  
Sergiusz Herman
Keyword(s):  
Comparative Analysis ◽  
Discriminant Analysis ◽  
Error Rate ◽  
Prediction Error ◽  
Bankruptcy Prediction ◽  
True Error ◽  
Specific Population ◽  
Test Set ◽  
Linear Discriminant ◽  
Independent Test

Classification is an algorithm, which assigns studied companies, taking into consideration their attributes, to specific population. An essential part of it is classifier. Its measure of quality is especially predictability, measured by true error rate. The value of this error, due to lack of sufficiently large and independent test set, must be estimated on the basis of available learning set.The aim of this article is to make a review and compare selected methods for estimating the prediction error of classifier, constructed with linear discriminant analysis. It was examined if the results of the analysis depends on the sample size and the method of selecting variables for a model. Empirical research was made on example of problem of bankruptcy prediction of join-stock companies in Poland.

A Note on Huberty and Wisenbaker’s “Views of Variable Importance”

1997 ◽  
Vol 22 (3) ◽  
pp. 309-322 ◽  
Author(s):  
D. Roland Thomas
Keyword(s):  
Discriminant Analysis ◽  
Discriminant Function ◽  
Operational Definition ◽  
Variable Importance ◽  
Linear Discriminant Function ◽  
Linear Discriminant ◽  
Additional Information ◽  
Definition Of

This article investigates criteria for assessing variable importance in MANOVA and descriptive discriminant analysis. Two criteria suggested by Huberty and Wisenbaker (1992) are examined, namely, (a) contribution to linear discriminant function scores and (b) contribution to grouping variable effects. Thomas and Zumbo (1996) have shown that the first criterion can be operationalized using discriminant ratio coefficients (DRCs). It is shown in this article that DRCs also provide an operational definition of grouping variable effects. Thus, it is proposed that the two criteria be amalgamated and called the contribution to grouping effects and discriminant scores. The F-to-remove indexes used by Huberty and Wisenbaker can then be regarded as operational definitions of a separate criterion, namely, the amount of additional information contributed to group discrimination.

Quadratic Discriminant Analysis with Covariance for Stock Delineation and Population Differentiation: A Study of Beaked Redfishes (Sebastes mentella and S. fasciatus)

1985 ◽  
Vol 42 (10) ◽  
pp. 1672-1676 ◽  
Author(s):  
R. K. Misra
Keyword(s):  
Discriminant Analysis ◽  
Fisheries Management ◽  
Discriminant Function ◽  
Population Differentiation ◽  
Quadratic Discriminant Analysis ◽  
Morphometric Data ◽  
Linear Discriminant ◽  
Vital Importance ◽  
Quadratic Discriminant Function ◽  
Sebastes Mentella

Stock delineation is of vital importance in fisheries management programs. Linear discriminant function (LDF) has been employed extensively in population differentiation studies but is of severely restricted usefulness when populations differ in their dispersion matrices. Quadratic discriminant function (QDF) is the appropriate analysis to employ in these situations. Here, I analyzed morphometric data of beaked redfishes (Sebastes mentella and S. fasciatus) by a recently developed conditional QDF.

The Research on the EC Individual Credibility Based on Linear Discriminant Analytic Hierarchy Process

2011 ◽  
Vol 219-220 ◽  
pp. 112-115
Author(s):  
Zun Qi Yang ◽  
Hai Lin
Keyword(s):  
Discriminant Analysis ◽  
Analytic Hierarchy Process ◽  
Linear Discriminant Analysis ◽  
Discriminant Function ◽  
Linear Discriminant Function ◽  
Simulation Test ◽  
Analytic Hierarchy ◽  
Linear Discriminant ◽  
Hierarchy Process

The paper gives the linear discriminant function (LDF) based on the theory of linear discriminant analysis (LDA) combined with computing weights of the different indicators by analytic hierarchy process (AHP) to predict a new e-commerce customer’s individual credibility level and analyzes the result of a simulation test to justify the theory in this research.

scholarly journals ANALISIS DISKRIMINAN PADA KLASIFIKASI DESA DI KABUPATEN TABANAN MENGGUNAKAN METODE K-FOLD CROSS VALIDATION

2017 ◽  
Vol 6 (2) ◽  
pp. 106 ◽  
Author(s):  
IDA AYU MADE SUPARTINI ◽  
I KOMANG GDE SUKARSA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Discriminant Analysis ◽  
Covariance Matrix ◽  
Discriminant Function ◽  
Rural Areas ◽  
Urban Areas ◽  
Cross Validation ◽  
Quadratic Discriminant Analysis ◽  
Linear Discriminant ◽  
Quadratic Discriminant Function ◽  
Fold Cross Validation

Tabanan Regency is one of the eight regencies and one municipality in Bali Province. Administratively, it is divided into 10 districs and  villages. There are rural areas and urban areas in the regions. Discriminant analysis is a technique related to the separation of objects into different groups that have been set previously. The purpose of this research is to classify villlages in Tabanan Regency into urban or rural groups with discriminant analysis. Linear discriminant analysis assumes that the covariance matrix of the two groups are equals, if the assumption of equality of covariance matrix is violated, quadratic discriminant analysis can be used for classification. This research uses k-fold crosss validation method for calculating the accuracy of quadratic discriminant function where . Quadratic discriminant function is obtained by  with the smallest APER value (). All of classification results are stable and consistence.

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