Prediction of business failure: a comparison of discriminant and logistic regression analyses (original) (raw)
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Global Business and Economics Review, 2015
The application of statistics in business is essential in order to make decisions in a rigorous and reliable way. One of the fields where forecasting methods are important focuses on business failure. In a comparative study, discriminant analysis and logistic regression are applied on a sample of small and medium-sized firms with head offices in Castilla y León (Spain) in order to predict business failure using a set of financial ratios as independent variables to enter the corresponding models. The achieved results show that there are some differences in the variables becoming significant in each method, but factors related to resources generation are common to both. The classification results reveal that the two methods are appropriate to predict business failure, but logistic regression turns out to be somewhat better, since the percentages of correctly classified firms are higher.
The British Accounting Review, 2006
Over the last 35 years, the topic of business failure prediction has developed to a major research domain in corporate finance. A gigantic number of academic researchers from all over the world have been developing corporate failure prediction models, based on various modelling techniques. The 'classic cross-sectional statistical' methods have appeared to be most popular. Numerous 'single-period' or 'static' models have been developed, especially multivariate discriminant models and logit models.
Predicting business failures in non-financial turkish companies
2015
Cataloged from PDF version of article.The prediction of corporate bankruptcies has been widely studied in the finance literature. This paper investigates business failures in non-financial Turkish companies between the years 2000 and 2015. I compare the accuracies of different prediction models such as multivariate linear discriminant, quadratic discriminant, logit, probit, decision tree, neural networks and support vector machine models. This study shows that accounting variables are powerful predictors of business failures one to two years prior to the bankruptcy. The results show that three financial ratios: working capital to total assets, net income to total assets, net income to total liabilities are significant in predicting business failures in non-financial Turkish companies. When the whole sample is used, all five models predict the business failures with at least 75% total accuracy, where the decision tree model has the best accuracy. When the hold-out samples are used, n...
Business failure prediction using statistical techniques: A review
2012
Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly in financial investment and lending. The potential value of such models has been recently emphasised by the extremely costly failure of high profile businesses in both Australia and overseas, such as HIH (Australia) and Enron (USA). Consequently, there has been a significant increase in interest in business failure prediction from both industry and academia.
The Evaluation of the Success Rate of Corporate Failure Prediction in a Five-Year Period
Journal of Competitiveness, 2020
The development of bankruptcies in the Czech Republic is closely related to the impact of the global financial economic crisis, which, among other things, has also affected the competitiveness of Czech companies to a great extent. The future state of overall company financial health can be determined through prediction models. This paper discusses the history of financial analysis and the most widely used models, with the main purpose of the paper to compare the accuracy of various prediction models and to decide which model has the highest prediction success rate. The sample consisted of the total of 90 Czech companies, out of which 1/2 were companies in bankruptcy and 1/2 were non-bankrupt companies. Ratio indicators of given models were calculated from balance sheets as well as profit and loss statements for a five-year period. The reliability of the accurate classification of accounting units is verified by a confusion matrix. The highest total success rate of classification was achieved by Zmijevski model, which had the highest predictive value. Another partial objective of the paper is to determine whether the accuracy rate of the bankruptcy models changes with branches within which the companies operate. The hypothesis about differences between the branches is confirmed. The most statistically significant differences were shown between Wholesale and Retail and the Processing Industry, with the results of models varying among different branches. The results show that taking into account the branches the company is operating in is advisable for selecting prediction models.
Business failure prediction: A comparison of classification methods
Operational Research, 2002
Business failure prediction is one of the most essential problems in the field of finance. The research on developing business failure prediction models has been focused on building classification models to distinguish among failed and non-failed finns. Such models are of major importance to financial decision makers (credit managers, managers of firms, investors, etc.); they serve as early warning systems of the failure probability of a corporate entity. The significance of business failure prediction models has been a major motivation for researchers to develop efficient approaches for the development of such models. This paper considers several such approaches, including multicriteria decision aid (MCDA) techniques, linear programming and performs a thorough comparison to traditional statistical techniques such as linear discriminant analysis and logit analysis. The comparison is performed using a sample of 144 US firms for a period of up to five years prior to failure.
Predicting corporate failure: some empirical evidence from the UK
Benchmarking: An International Journal, 2009
Purpose -The purpose of this paper is to use relevant financial information of private medium-sized failed and non-failed manufacturing firms in the UK, during the period 1994-2004 to determine whether corporate failure can be predicted by developing a Z-score model. Design/methodology/approach -Multiple discriminant analysis is used to develop the Z-score to support the notion that Z-score is an innovation to overcome the numerous difficulties associated with using single ratios to measure companies' health or risk of failure. Findings -This paper advances the notion that the net profit margin is superior to the gross profit margin in discriminating between failed and non-failed UK manufacturing companies in terms of its significant contribution to the Z-score, though the latter exceeds the former slightly using the univariate analysis. Originality/value -This research contributes to the area of benchmarking by providing a method to more accurately predict corporate failure.
Predicting Corporate Failure: Empirical Evidence for the UK
The main purpose of this paper is the development and validation of a failure classification model for UK public industrial companies using current techniques: logit analysis and Neural Networks. Our dataset consists of 51 matched-pairs of failed and nonfailed UK public industrial firms over the period 1988-1997. Prediction models are developed for up to three years prior to the failure event. The models are validated using an out of sample period ex-ante test and the Lachenbruch technique.
Business Failure Prediction for Slovak Small and Medium-Sized Companies
Sustainability, 2020
Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficultie...