Non-failed Firms Research Articles

Influence of earnings management on forecasting corporate failure

This paper studies the relationship between corporate failure forecasting and earnings management variables. Using a new threshold model approach that separates samples into different regimes according to a threshold variable, the authors examine regimes to evaluate the prediction capacities of earnings management variables. By proposing this threshold model and applying it innovatively, this research reveals boundaries within which earnings management variables can yield superior corporate failure forecasting. The inclusion of earnings management variables in corporate failure models improves failure prediction capacities for firms that manipulate substantial earnings. Furthermore, an accruals-based variable improves predictions of failed firms, but the real activities-based variable improves predictions of non-failed firms. These findings highlight the importance of indicators of the magnitude of earnings management and the tools used to improve the performance of corporate failure models. The proposed model can determine the predictive power of particular explanatory variables to forecast corporate failure.

Analyzing business-failure-process risk: evidence from Finland

PurposeThe purpose of this study is to analyze the business-failure-process risk from two perspectives. First, a simplified model of the loss-generation process in a failing firm is developed to show that the linear system embedded in accounting makes financial ratios to depend linearly on each other. Second, a simplified model of the development of the risk during the failure process is developed to introduce a new concept of failure-process-risk line (FPRL) to assess the systematic failure risk of a firm. Empirical evidence from Finnish firms is used to test two hypotheses.Design/methodology/approachThis study makes use of simple mathematical modeling to depict the loss-generation process and the development of failure risk during the failure process. Hypotheses are extracted from the mathematical results for empirical testing. Time-series data originally from 13,082 non-failing and 515 failing Finnish are used to test the hypotheses. Analysis of variance F statistics and Mann–Whitney U test are used in testing of the hypotheses.FindingsThe findings show that the linear time-series correlations are generally higher in failing than in non-failing firms because of the loss-generation process. The FPRL depicted efficiently the systematic failure-process risk through the beta coefficient. Beta coefficient efficiently discriminated between failing and non-failing firms. The difference between the last-period risk estimate and FPRL was largely determined by the approximated growth rate of the periodic failure risk.Research limitations/implicationsThe loss-generation process is based on a simple cash-based approach ignoring the growth of the firm. In future research, the model could be generalized to a growing firm in an accrual-based framework. The failure-process risk is assumed to grow at a constant rate. In further studies, more general models could be applied. Empirical analyses are based on simple statistical methods and tests. More advanced methods could be used to analyze the data.Practical implicationsThis study shows that failure process makes the time-series correlation between financial ratios to increase making their signals of failure consistent and allowing the use of static classification models to assess failure risk. The beta coefficient is a useful tool to reflect systematic failure-process risk. In addition, it can be used in practice to warn a firm about ongoing failure process.Originality/valueTo the best of the author’s knowledge, this is the first study analyzing systematically business-failure-process risk. It is first in introducing a mathematical loss-generation process and the FPRL based on the beta coefficient assessing the systematic failure risk.

Bankruptcy prediction using multivariate grey prediction models

PurposeIn the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).Design/methodology/approachAs the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).FindingsExperimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.Practical implicationsAmong artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.Originality/valueApplying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

İŞLETME BAŞARISIZLIĞININ TAHMİN EDİLMESİNDE NAKİT AKIM TABLOSUNUN KULLANIMI: GELİŞMEKTE OLAN BİR PİYASADAN BULGULAR

In the competitive economic environment, cash flow statement reveals prominent information about firms. The aim of the present study is to examine the usefulness of cash flow statement in the prediction of business failure by using a sample including 66 firms listed on Borsa Istanbul from 2010 to 2018. A total of 33 non-failed firms are matched with 33 failed firms based on size and industry. In the present study, logistic regression models are constructed for each of three years prior to business failure. The empirical findings of this study indicate that cash flow measures are strong predictors of business failure.

The use of accounting anomalies indicators to predict business failure

Most of the studies that try to predict business failure assume that accounts give a true and fair view of the financial position of a company, without considering that managers can discretionarily apply accounting rules or even perform accounting fraud. This paper takes a set of financial ratios especially designed to detect accounting anomalies as bankruptcy predictors. These ratios are not very common in bankruptcy prediction studies, but they come from creative accounting studies. The ratios try to identify abnormal depreciation figures, exaggerated receivables or deteriorating financial conditions preceding aggressive accounting practices. The empirical study has been performed from a sample of 51,337 public and private European companies, during the period 2012–2016. The analysis techniques applied were logistic regression and decision trees, allowing to obtain rules to predict the status of failed or non-failed. It is found that several indicators proposed in the literature as earnings management indicators present statistically significant differences between failed and non-failed firms, but they do not have enough predictive power to incorporate them into prediction models. However, an index developed to measure accounting anomalies exhibits high discriminatory power, similar to that of the classical financial ratios. The construction of the index and its application to private firm sample provide the main contribution of the paper, as the results suggest slightly better forecast accuracy only for the private firm sample. The inclusion of indicators to detect accounting anomalies should be considered when developing new models to predict bankruptcy, especially in private companies.

A Study of Applicability of Bankruptcy Prediction Models for Small Businesses

The paper investigates predictive ability of existing bankruptcy prediction models suitable for small business by using dates of accounting report of Russian’s firms. Combination of financial ratios analysis with bankruptcy prediction models’ testing made it possible to identify the models showing high predictive ability. Study also provides applicable model for small business. The study is based on financial statements data about one hundred enterprises of small businesses, divided two types: failed and non-failed firms. The results of the study would be useful to many users such as financial analysts, board of small enterprises, lenders, auditors, tax inspectors.

Explaining the causes of business failure using audit report disclosures

This paper examines the ability of audit report disclosures to explain the causes of business failure. Despite incremental interest in organizational failure, much of the existing literature has used accounting ratios to foresee why firms fail. We hypothesise that the audit report can also be employed for this purpose because it provides information regarding any material uncertainty relating to events that may warn users about possible causes of business default. Using a matched sample of 808 failed and non-failed firms, our results suggest that audit report disclosures significantly explain the causes of business failure. Moreover, these findings are consistent with the results of studies that integrate both deterministic and voluntaristic perspectives into the examination of the antecedents of organizational failure, as disclosures about both external and internal factors are mentioned in the audit report and contribute to assessing default. Managers, auditors, regulators and other users may consider the audit report to be useful as a tool to anticipate business failure.

A new tool for failure analysis in small firms: frontiers of financial ratios based on percentile differences (PDFR)

ABSTRACTThis paper proposes an innovative methodology based on the use of differences between percentiles to compute the scores and distances to failure of a specific firm or group of firms. This approach is based on significant differences between the group of failed firms and the population to which the failed firms belong (meaning the same industry, period and geographical zone selected) and eliminates the effects of correlation between the factors selected to compute the scores. The use of accounting ratios, which can be computed using data available in the mandatory financial statements, and the homogenisation of these variables using percentiles make percentile difference frontier of ratios a tool specially oriented to small and medium-sized enterprises (SMEs). Our results for the selection of the most discriminant variables are consistent with those of previous studies, and the hit rates of failed and non-failed firms outperform those of the commonly used traditional methodologies. In addition, the proposed methodology enables us to compute distances to failure of both individual firms and groups of firms. Finally, this methodology identifies which of the financial drivers used are strengths or weaknesses for the specific firm or group of firms under study for purposes of a potential reorganisation.

Prediction of Business Failure and Fraudulent Financial Reporting: Evidence from Malaysia

Fraud has been of concern for many corporate managers and regulators since many businesses have been victim of business failures. Business failure corporations have reported and become an element of fraudulent financial reporting.Therefore, the first aim of this study is to examine whether a collective prediction tool can be used to predict business failure and fraudulent financial reporting.The second and more important objective of this study is to examine whether business failure companies are associated with fraudulent financial reporting. The collective prediction tool is based on ratio analysis, Beneish M-score model and Z-score model. Using publicly available information in the annual reports of 24 failed firms matched with 24 non-failed firms listed on Bursa Malaysia, relevant information are extracted and applied in the three models to assist in predicting business failure and detecting fraudulent financial reporting. A total of 10 ratios, cash conversion cycle, Beneish M-score model and Altman's Z-score model were identified for examination as potential predictors of business failure and fraudulent financial reporting. Based on the results, the model was accurate in classifying the total sample of approximately 96 per cent as predictors of business failure and approximately 83.3 per cent as predictors of fraudulent financial reporting as well as predicting the relationship between business failures and fraudulent financial reporting. Hence, it can be concluded that these model functions effectively and can be adopted by regulators, bankers, management, internal and external auditors and to the forensic accountants so that proper preventive or corrective action can be taken to mitigate fraud at its inception.

Financial Institution Failure Prediction Using Adaptive Neuro-Fuzzy Inference Systems: Evidence from the East Asian Economic Crisis

This paper introduces the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) into the area of finance for Thai firms. This study started with collecting financial data from 82 finance companies and 15 commercial banks operating in the period 1992-1997, before the East Asian economic crisis occurred. Financial data on failed and non-failed firms were then examined to develop fuzzy rules based on CAMEL variables. ANFIS is applied to the area of finance for Thai firms for constructing failure prediction models. These models show that prediction accuracy is greater than 90 percent for one to five years prior to failure, indicating the robustness of models over time. In experiments, models yield more accurate forecasting than a logistic model that has been used in the area of finance for Thai firms. The purpose of this study is to present thatmodels using ANFIS are better suited for financial data sets with high nonlinearity than a logistic model.

The Accessibility Of The Italian Bankruptcy Procedures: An Empirical Analysis

The importance of efficient bankruptcy procedures is widely recognized and the acute recent economic crisis has emphasized the noteworthiness of this topic, which international literature has analyzed in several ways, focusing the attention on different variables, such as accessibility of bankruptcy procedures, their costs and duration, incentives and/or penalties for debtor and creditors related to the onset of a procedure, and so on. The aim of the paper is the analysis of the Italian bankruptcy procedures both in an ex post and in ex ante perspective. The ex post perspective is based on official statistical data annually published by the ISTAT (the Italian Office of Statistics), investigating costs and duration of bankruptcy procedures as well as average ratio of claims reimbursed to the creditors. The period investigated is 2000–2007. The ex ante perspective is based on two non-paired samples of failed and non-failed firms, investigating their financial statements of 2003–2008. More specifically, non-failed firms are firms for which a bankruptcy petition was submitted but the court did not order relief, rejecting the petition. The main expected result is that the Italian bankruptcy procedures are complex and not accessible enough.

Does the Capitalization of Intangible Assets Increase the Predictability of Corporate Failure?

SYNOPSIS: The value relevance of intangible assets is now well documented in the literature, leading to calls for standard setters to adopt more flexible reporting rules for these assets. In this study, I evaluate the merits of intangibles capitalization from a bankruptcy and default risk perspective, which has not been previously considered in the literature. The study is conducted in a unique reporting environment, where managers have had considerable discretion to capitalize a wide range of intangibles over an extended period. Three main results are reported. First, failing firms capitalize intangible assets more aggressively than non-failed firms over the 16-year sample period, but particularly over the five-year period leading up to firm failure. Second, drawing on the accounting choice literature, I find that managers’ propensity to capitalize intangible assets has a strong statistical association with earnings management proxies, particularly among failing firms. Finally, voluntary capitalization of intangibles has strong discriminating and predictive power in a firm failure model, even after controlling for several other factors.

Development of a Financial Health Indicator Based on Companies’ Annual Accounts

This document describes the development of a financial health indicator based on companies' financial statements. This indicator is conceived as a weighted combination of variables, which is obtained through a model discriminating between failing firms and non-failing firms. The definition of failure is based on a legal criterion, namely that a is considered to have failed if it has faced bankruptcy or judicial administration in the past. Based on the model results, companies are positioned in financial health classes, which are intended to be included in the company files designed by the Central Balance Sheet Office.

Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry

This paper applies the Taiwan electronics industry data to detect the discriminatory powers of Logit, KMV, and zero-price probability (ZPP) models that represent respectively the regressive fitting model, the option-based pricing model, and the GARCH time series simulation model. In our circumstances, according to cumulative accuracy profile, receiver operating characteristic, and even Brier score, the KMV performs the worst. The disadvantages for KMV are that the equity market exists some nonlinear characteristics, the unknown market value of asset affected by the change of capital structure is not exogenous, and the failure point is difficult to be estimated correctly. Besides, KMV is however too simple to model the fluctuation of the equity value as the GARCH does. On the other hand, the Logit performs above average. To preclude over-fitting and keep model parsimonious, two significant factors are extracted from as many as forty financial variables for the logistic regression on binary failure data. The result of Logit training has perfect discrimination. However, for the post-sample data, the fitting to categorical but not ordinal data makes Logit have the divergent failure predicted probabilities and highest Briser Score. In practical, ZPP GARCHNorm uses just equity value to predict firm failure but it performs remarkably well supposing that downward price trend or volatility persistence in stock price changes is appropriately caught. It implies that the distorted signals such as overreaction of traders and insider trading would definitely impair the ZPP GARCHNorm. Nevertheless, the larger type I error than type II error in all models indicates that the prediction of non-failed firms should be more examined further than that of failed firms.

Beyond business failure prediction

Business failures are depressing events which not only decimate the benefits of stakeholders but also affect the continuing development of economy and society. In order to reduce the impact of business failure, various models of business failure prediction have been developed. Although failure prediction models currently achieve a collective average accuracy of more than 85%, few persons can bear a risk of less than 100% accuracy under the present conditions of economic crisis. It is of particular interest that current failure prediction models have tended to adopt the technique of matching up failed and non-failed firms. This method, however, seems to have merely led to further complications. This paper proposes a method which directly explores the features of failed firms rather than researching pairs of failed and non-failed firms. To this end, automatic clustering techniques and feature selection techniques are employed for this study.

코스닥기업의 부실예측에 대한 실증 분석

Bankruptcy of firms in Korea can cause distress of financial institutions because these institutions have disterssed bond. Accordingly, social and economical spill-over effects by these results are very big. Even after the difficult times of IMF crisis had ended, bankruptcy of information-based small-medium companies and venture firms listed on the KOSDAQ has been continued. In this context, this study developed and adopted failure prediction models for which discriminant analysis was used. Samples of this study was 81 firms respectively for both failed and non-failed firms listed on the KOSDAQ between the year of 2000 and 2007. The results of this study are as follows. First, the accuracy of classification of the model by years was , and the accuracy of classification of the mean model was . Among the models, the mean model of -one year, -two years, and -three years was highest in accuracy of classification (80.4%). Second, accuracy of prediction of final model adopted on validation samples showed 85% before one year of bankruptcy. The results of this study may be significant in that the results may be used as early warning system for bankruptcy prediction of KOSDAQ firms.

Aggregate Economy Risk and Company Failure: An Examination of UK Quoted Firms

Considerable attention has been directed in the recent finance and economics literature to issues concerning the effects on company failure risk of changes in the macroeconomic environment. This paper examines the accounting ratio-based and macroeconomic determinants of insolvency exit of UK large industrials during the early 1990s with a view to improve understanding of company failure risk. Failure determinants are revealed from estimates based on a cross-section of 369 quoted firms, which is followed by an assessment of predictive performance based on a series of time-to-failure-specific logit functions, as is typical in the literature. Within the traditional for cross-sectional data studies framework, a more complete model of failure risk is developed by adding to a set of traditional financial statement-based inputs, the two variables capturing aggregate economy risk - one-year lagged, unanticipated changes in the nominal interest rate and in the real exchange rate. Alternative estimates of prediction error are obtained, first, by analytically adjusting the apparent error rate for the downward bias and, second, by generating holdout predictions. More complete, augmented with the two macroeconomic variables models demonstrate improved out-ofestimation-sample classificatory accuracy at risk horizons ranging from one to four years prior to failure, with the results being quite robust across a wide range of cut-off probability values, for both failing and non-failed firms. Although in terms of the individual ratio significance and overall predictive accuracy, the findings of the present study may not be directly comparable with the evidence from prior research due to differing data sets and model specifications, the results are intuitively appealing. First, the results affirm the important explanatory role of liquidity, gearing, and profitability in the company failure process. Second, the findings for the failure probability appear to demonstrate that shocks from unanticipated changes in interest and exchange rates may matter as much as the underlying changes in firm-specific characteristics of liquidity, gearing, and profitability. Obtained empirical determinants suggest that during the 1990s recession, shifts in the real exchange rate and rises in the nominal interest rate, were associated with a higher propensity of industrial company to exit via insolvency, thus indicating links to a loss in competitiveness and to the effects of high gearing. The results provide policy implications for reducing the company sector vulnerability to financial distress and failure while highlighting that changes in macroeconomic conditions should be an important ingredient of possible extensions of company failure prediction models.

Forecasting Financial Problems in Emerging Capital Markets

The advent of the Asian Financial Crisis (AFC) in the Southeast Asia in 1997 is an appealing case for research work in assessing corporate financial distress. From international perspective, AFC is a product of contagion effect that spread from Thailand and consequently to the other Asian countries. Domestically, the AFC has resulted a sudden economic slump and corporate failures in these economies. This paper examines the corporate failure before the 1997 Asian Financial Crisis in three emerging capital markets namely Malaysia, Singapore and Thailand, and develops, tests, and analyses a model for classifying and predicting financial distress. A failure classification model based on multiple discriminant analysis was utilised to classify listed corporations from these countries for the 1980 to 1996 period. The model is tested on a sample of 33 Malaysian, 17 Singaporean and 52 Thailand failed firms and similar number of non-failed firms in the respective countries as a control sample. The failure prediction model developed successfully discriminates between failed and non-failed listed firms at the rate of 86%, 82% and 71% of Malaysian, Singaporean and Thailand firms respectively. Further validation of findings show that the predictive accuracy was significantly better than chance. © EuroJournals Publishing, Inc. 2006.

Bankruptcy Prediction During the IMF Crisis: Evidence from Malaysian Listed Industrial Companies

This paper develops bankruptcy prediction model of considerable efficiency for firms listed and traded in a small developing economy. New findings reported in this paper help extend bankruptcy prediction literature to a different market setting. We find there are three relevant variables: total liabilities; current asset turnover; and cash ratios. The model correctly classified about nine-tenth of failed and non-failed firms in the original and holdout samples. The model predicts failure four years ahead. We also introduce a further sample comprising of those firms that had experienced financial distress between 1997-1998 (during the Asian Financial Crisis period) to assess model superiority. The predictive accuracy of the model reveals that it was significantly better than chance. Thus, these findings are somewhat different from those in the literature.

Identification of corporate distress in UK industrials: a conditional probability analysis approach

Multivariate discriminant analysis (MDA) has long been used to classify failing and non-failing firms with high accuracy rates, although a number of methodological flaws are well known. The alternative approach based on conditional probability analysis (CPA) models have been applied to forecast mergers and acquisitions and extended to the prediction of corporate failure. This is used here to distinguish between distressed and non-distressed companies in the UK industrial sector for the period 1985–1994. Results show that the CPA model is both efficient and consistent, has high accuracy levels and avoids the biased sampling problems that have been identified in MDA studies.