Business Failure Prediction Models Research Articles

A Three‐Country Comparison of the Business Success versus Failure Prediction Model

Why do some businesses succeed and others end up bankrupt? There is great discrepancy in the literature as to which variables do in fact lead to success, thus, there currently is no theory. To move the field in that direction, this study tests the Lussier 15‐variable business success versus failure prediction model in Chile with a sample of 234 small businesses—131 failed and 103 successful. Results support the model's validity in Chile. Thus, the model has been tested with significant results in three very different parts of the world; first in United States (North America), then in Croatia (Central Eastern Europe), and now in Chile (South America). The model will reliably predict a group of businesses as failed or successful more accurately than random guessing in all three countries over 96 percent of the time.

Business failure prediction using decision trees

AbstractAccurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high‐profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non‐parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd.

The Low P/E Effect and Abnormal Returns for Australian Industrial Firms

While the low P/E effect has been examined rather extensively in international markets particularly in the US, there is a notable absence of Australian market-based P/E studies. This research examines the relationship between the investment performance of Australian Industrial common stock and their P/E ratios in an attempt to uncover potential for a P/E based trading strategy. The excess and differential returns of P/E ranked portfolios containing 1310 Industrial firms over a 9 year period (January 1998 to December 2006) are examined. The results show the existence of a low P/E effect in the Australian capital market. Furthermore, the superior returns of low P/E stocks increase when a consensus of two business failure prediction models is applied to the portfolio of low P/E stocks. The statistically significant risk-adjusted returns afforded to hypothetical investors over the sample period (up to 12?% per annum), not only provide support for a P/E based trading strategy, but also suggest a violation of the semi-strong form of the Efficient Market Hypothesis.

Fraudulent financial reporting detection and business failure prediction models: a comparison

PurposeThe purpose is to explore the differences and similarities between fraudulent financial reporting detection and business failure prediction (BFP) models, especially in terms of which explanatory variables and methodologies are most effective.Design/methodology/approachIn total, 52 financial variables were identified from previous studies as potentially significant. A number of Taiwanese firms experienced financial distress or were accused of fraudulent reporting in 2005. Data on these firms and their contemporaries were obtained from the Taiwan Economic Journal data bank and Taiwan Stock Exchange Corporation. Financial variables were calculated for the years 2003 and 2004. Three well‐known data mining algorithms were applied to build detection/prediction models for this sample: logistic regression, neural networks, and classification trees.FindingsMany of the variables are effective at both detecting fraudulent financial reporting and predicting business failures. In terms of overall accuracy, logistic regression outperforms the other two algorithms for detecting fraudulent financial reporting. Whether logistic regression or a decision tree is best for BFP depends on the relative opportunity cost of misclassifying failing and healthy firms.Originality/valueThe financial factors used to detect fraudulent reporting are helpful for predicting business failure.

A note on performance measures for business failure prediction models

This note briefly describes three performance measures that can be used in business failure prediction models: the unweighted error rate (UER), D-max and the Gini-coefficient. The use of these measures (and the mathematical relationship between them) is illustrated with numerical examples. We hope that this note may help the reader to better understand (and possibly use) these classification criteria.

Developing a business failure prediction model via RST, GRA and CBR

The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. The widely applied methods to predict the risk of business failure were the classic statistical methods, data mining techniques and machine learning techniques. In addition to diagnosis and classification, Case Based-Reasoning (CBR) is an inductive machine learning method that can be applied to replace statistical models. Concerning the fact that the attribute extraction and weighting approach could enable CBR to retrieve the most similar case correctly and effectively, this paper proposes a Hybrid Failure Prediction (HFP) model by applying Rough Set Theory (RST) and Grey Relational Analysis (GRA) as data preprocessors to strengthen the effectiveness of CBR predicting capability. After exploring the data from TEJ database and comparing it with three models, CBR, RST-CBR, and HFP, the results show that our model is the most accurate and effective model in predicting business failure.

Predicting business failure: an application of multicriteria decision aid techniques in the case of small UK manufacturing firms

Small firms play an important role in most modern economies. Nevertheless, they are not without their problems. Therefore, the development of business failure prediction models for small firms is quite important. In the present study we employ three multicriteria decision aid (MCDA) techniques for the development of failure prediction models for small UK firms. The MCDA models are developed using a ten-fold cross validation and benchmarked against models developed with discriminant analysis and logit analysis. All models achieve satisfactory classification accuracies while including only a small number of input variables, hence avoiding the commonly encountered problem of data availability when dealing with small firms. Also we examine two non-financial variables – the age of the firm and auditors' opinion – but the classification results are only marginally affected.

35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems

Over the last 35 years, business failure prediction has become a major research domain within corporate finance. Numerous corporate failure prediction models have been developed, based on various modelling techniques. The most popular are the classic cross-sectional statistical methods, which have resulted in various ‘single-period’ or static models, especially multivariate discriminant models and logit models. To date, there has been no clear overview and discussion of the application of classic statistical methods to business failure prediction. Therefore, this paper extensively elaborates on the application of (1) univariate analysis, (2) risk index models, (3) multivariate discriminant analysis, and (4) conditional probability models in corporate failure prediction. In addition, because there is no clear and comprehensive analysis in the existing literature of the diverse problems related to the application of these methods to the topic of corporate failure prediction, this paper brings together all problem issues and enlarges upon each of them. It discusses all problems related to: (1) the classical paradigm (i.e. the arbitrary definition of failure, non-stationarity and data instability, sampling selectivity, and the choice of the optimisation criteria); (2) the neglect of the time dimension of failure; and (3) the application focus in failure prediction modelling. Further, the paper elaborates on a number of other problems related to the use of a linear classification rule, the use of annual account information, and neglect of the multidimensional nature of failure. This paper contributes towards a thorough understanding of the features of the classic statistical business failure prediction models and their related problems.

Business failure prediction: A comparison of classification methods

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 firms. 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.

Serial correlation, non‐stationarity and dynamic performance of business failures prediction

Outlines previous research on business failure prediction models and investigates the impact of serial correlation and non‐stationarity in financial variables on models based on linear discriminant analysis, logit and cumulative sums using 1974‐1991 data from a sample of failed and non‐failed US firms, plus a similar 1992 sample. Presents and discusses the time series behaviour of the explanatory variables, the estimation of the three types of models and their error rates over time. Concludes that models based on variables with strong positive serial correlation deteriorate over time in their forecasting power; and calls for research to develop stationary models.

Business failure prediction using the UTADIS multicriteria analysis method

Business failure prediction is one of the most essential problems in the field of financial management. The research on developing quantitative business failure prediction models has been focused on building discriminant models to distinguish among failed and non-failed firms. Several researchers in this field have proposed multivariate statistical discrimination techniques. This paper explores the applicability of multicriteria analysis to predict business failure. Four preference disaggregation methods, namely the UTADIS method and three of its variants, are compared to three well-known multivariate statistical and econometric techniques, namely discriminant analysis, logit and probit analyses. A basic (learning) sample and a holdout (testing) sample are used to perform the comparison. Through this comparison, the relative performance of all the aforementioned methods is investigated regarding their discriminating and predicting ability.

A retail failure prediction model

Financial ratios have long been used for business analysis. Researchers have formulated business failure prediction models utilizing financial ratios. This study focuses on the use of financial ratios to discriminate between failed and non-failed firms in the retail industry. Using a matched sample of sixty-six failed and sixty-six non-failed retail firms obtained from the COMPUSTAT database, multiple discriminant analysis was utilized to develop a retail prediction model which accurately classified 78 per cent of the sample firms as failed or non-failed. Hotelling's T 2 test determined that the mean vector of the discriminant function for failed firms differed from that of non-failed firms. The model was further validated using a jackknife procedure and split sample analysis. Failed or non-failed classification accuracy was found to be significantly better than chance.

Prediction of Business Failure: A Funds Flow Approach

The purpose of this paper is to assess the usefulness of financial ratios derived from working capital‐based funds flow information to predict the failure of US industrial firms. Unlike cash‐based funds flow ratios, used in the previous papers, capital‐based funds ratios are less volatile, therefore they are expected to be better predictors of business failure. Moreover, the paper utilizes a more general definition of business failure than the legal definition. The analysis is carried out using a stepwise logit procedure. The results indicate that working capital‐based funds flow measures are superior to cash‐based funds flow measures in business failure prediction models.

A Predictive Model in the Restaurant Industry Through Ratio Analysis

ABSTRACT Business failure is an integral and necessary element of the free enterprise system. It is the market's way of eliminating inefficient, non-innovative firms, thereby reallocating resources more efficiently. The earliest and best known of business failure prediction models was developed by Airman and Altman et al. Altaian et al. modified the original model to include retail firms analyzed on an equal basis with manufacturers. One remaining research question was whether the model can be applied to specific industry groups, particularly whether a model developed for manufacturing industries may be legitimately used for service industries.

Predicting Shifts in the Mean of a Multivariate Time Series Process: An Application in Predicting Business Failures

Abstract A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firm's slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists for the development of business failure prediction models that assess the financial condition of firms sequentially over time. This article addresses this need by presenting a sequential business failure prediction model.

Predicting Shifts in the Mean of a Multivariate Time Series Process: An Application in Predicting Business Failures

Abstract A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firm's slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists for the development of business failure prediction models that assess the financial condition of firms sequentially over time. This article addresses this need by presenting a sequential business failure prediction model.

The success of business failure prediction models: An international survey

This paper surveys and discusses numerous studies, both published and unpublished, that have attempted to construct and test business failure models outside the United States. Failure risk models are one of the few types of financial models that have been applied pervasively on an international level. A considerable flow of literature has gone relatively unnoticed due to the fact that the reference source is not well known or the source material is not available in English. It is the purpose of this paper to document these efforts and thereby increase the discourse and knowledge about such studies and others not surveyed here. This survey complements the other company related studies found in this Special Issue.