An Analysis of the Usefulness of Debt Defaults and Going Concern Opinions in Bankruptcy Risk Assessment

1998 ◽  
Vol 13 (3) ◽  
pp. 351-371 ◽  
Author(s):  
Benjamin P. Foster ◽  
Terry J. Ward ◽  
Jon Woodroof
Keyword(s):  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Loan Default ◽  
Bankruptcy Risk ◽  
Explanatory Variables ◽  
Going Concern ◽  
Going Concern Opinion ◽  
Loan Defaults ◽  
The Impact ◽  
Covenant Violations

This study extends the research of Hopwood et al. (1994) and Mutchler et al. (1997) by empirically investigating the relationships between loan defaults, violation of loan covenants, going-concern opinions, and bankruptcy in bankruptcy prediction models. One objective of this study is to empirically test the ability of loan defaults/accommodations and loan covenant violations to assess the risk of bankruptcy. Another objective of this study is to investigate the impact of failing to control for these two distress events on results from tests of the usefulness of going-concern opinions in assessing bankruptcy risk. Results suggest that loan default/accommodation and loan covenant violation are both significant explanatory variables of bankruptcy at the time of the last annual report before the event. While a going-concern opinion variable appears to significantly explain bankruptcy, it is not significant when included in a model with loan default/accommodation and covenant violation variables. Consequently, our results suggest that researchers should include both loan default/accommodation and covenant violation as control variables when using bankruptcy to test the usefulness of going-concern opinions.

scholarly journals The Impact of Bankruptcy Prediction, Company’s Financial Condition, Previous Year Audit Opinion, Firm Size and Audit Tenure Towards Auditor’s Going Concern Opinion

2016 ◽  
Vol 1 (1) ◽  
pp. 51-58
Author(s):  
Lisa Cellica ◽  
Ratnawati Kurnia
Keyword(s):  
Firm Size ◽  
Stock Exchange ◽  
Secondary Data ◽  
Jel Classification ◽  
Bankruptcy Prediction ◽  
Audit Opinion ◽  
Going Concern ◽  
Financial Condition ◽  
Going Concern Opinion ◽  
The Impact

Objective – The auditor is responsible for obtaining sufficient audit evidence about the accuracy and proper use of the going concern assumption from the company’s management through its financial statements. These evidence are used for the purpose of deciding whether there are material uncertainties about the entity's ability to maintain the continuity of its business. Thus, the objective of this paper is to examine the impact of bankruptcy prediction, company’s financial condition, previous year audit opinion, firm size and audit tenure towards Auditor’s going concern opinion. Methodology/Technique – The object of this paper is the service companies listed on the Indonesia Stock Exchange for the period of 2011-2014. This paper uses secondary data and samples taken were determined based on the purposive sampling method. The regression logistic is used to analyse data. Findings – The results of this research show that bankruptcy prediction, company’s financial condition, previous year audit opinion, firm size, and audit tenure all simultaneously, have a significant impact towards Auditor’s going concern opinion, particularly Previous Year Audit Opinion. Novelty – This paper provides insights into the factors affecting auditors in providing a going concern opinion in the case of Indonesian companies. Type of Paper: Empirical Keywords: Bankruptcy Prediction; Company’s Financial Condition; Previous Year Audit Opinion, Firm Size; Audit Tenure; Auditor’s Going Concern Opinion. JEL Classification: D81, M42.

scholarly journals PENGARUH PENERAPAN GOOD CORPORATE GOVERNANCE, OPINION SHOPPING, KUALITAS AUDIT, DAN AUDIT CLIENT TENURE TERHADAP PENERIMAAN OPINI AUDIT GOING CONCERN AUDITING

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Panggah Wira Angkasa ◽  
Dewi Indriasih ◽  
Baihaqi Fanani
Keyword(s):  
Logistic Regression ◽  
Research Result ◽  
Institutional Ownership ◽  
Audit Opinion ◽  
Quality Audit ◽  
Going Concern ◽  
Opinion Shopping ◽  
Going Concern Opinion ◽  
Infrastructure Services ◽  
The Impact

The Impact of Good Governance, Opinion Shopping, Quality Audit and Audit Client Tenure Application towards Going Concern Opinion Audit Acceptance (Empirical Studies on Infrastructure Services Company, Utility, and Transportation which Registered at Indonesian Stock Exchange (ISE) during 2013 – 2017 Period). Essay. Tegal: Economic & Business Faculty, Pancasakti University Tegal. 2018. The aim of this research is to finding out the impact of institutional ownership, independent commissioner, committee audit, opinion shopping, quality audit, audit client tenure towards going concern’s opinion audit on infrastructure services company, utility, and transportation which registered at ISE during 2013 – 2017 period. The population in this research are infrastructure services company, utility, and transportation which registered at ISE during 2013 – 2017 period and the sample determination by using purposive sampling method, so within the result obtained 15 company’s samples. The data analysis method used is logistic regression analysis. Based on logistic regression analytic, the research result concluded that institutional ownership (0,109), audit committee (0,429), opinion shopping (0,607), and quality audit (0,998) are not affecting the going concern opinion audit. Meanwhile, the independent commissioner (0,006), and audit client tenure (0,004) are affecting the going concern opinion audit. Keywords: going concern, opinion audit, institutional ownership, independent commissioner, committee audit, opinion shopping, quality audit, audit client tenure

scholarly journals Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear

2017 ◽  
Vol 43 (3) ◽  
pp. 74-81 ◽  
Author(s):  
Bartosz Szeląg ◽  
Lidia Bartkiewicz ◽  
Jan Studziński ◽  
Krzysztof Barbusiński
Keyword(s):  
Prediction Models ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Municipal Sewage ◽  
Support Vector ◽  
Prediction Errors ◽  
Explanatory Variables ◽  
Input Variables ◽  
The Impact

AbstractThe aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.

scholarly journals The Effect of Financial Distress, Firm Growth, and Previous Year’s Opinion on The Firm’s Going Concern Opinion

2021 ◽  
Vol 58 (1) ◽  
pp. 247-258
Author(s):  
Amiruddin, Grace T. Pontoh, Marina Lauren
Keyword(s):  
Financial Distress ◽  
Firm Growth ◽  
Stock Exchange ◽  
Secondary Data ◽  
Substantial Impact ◽  
Audit Opinion ◽  
Going Concern ◽  
Service Companies ◽  
Going Concern Opinion ◽  
The Impact

This research aims to examine and determine the impact of financial distress, firm growth, and opinion on previous year to firms‘going concern. The study was carried on service companies that are listed on Indonesia Stock Exchange during 2015-2017. A total of 210 samples were selected using the purposive sampling method. This research utilizes secondary data in the form of the firm’s financial statements and independent auditor’s reports. This research utilized logistic regression analysis to process the data. Results showed that financial distress and previous year’s opinion has significantly affect the firm’s going concern audit opinion while the firm growth has no substantial impact on the firm’s going concern audit opinion. Simultaneously, financial distress, firm growth, and previous year's opinion significantly affected the firm's going concern opinion.

A Study of the Relationship between a Going Concern Opinion and Its Financial Distress Metrics

2017 ◽  
Vol 14 (2) ◽  
pp. 17-28 ◽  
Author(s):  
Vikram Desai ◽  
Joung W. Kim ◽  
Rajendra P. Srivastava ◽  
Renu V. Desai
Keyword(s):  
Search Engine ◽  
Financial Distress ◽  
Primary Objective ◽  
Financial Viability ◽  
Going Concern ◽  
Going Concern Opinion ◽  
One Year ◽  
The Impact ◽  
First Time ◽  
The Relationship

ABSTRACT The primary objective of this paper is to employ search engine technology to investigate the relationship between first-time going concern opinions (GCOs) and the financial viability of the GCO recipients using delisting as a criterion rather than bankruptcy. The paper also investigates the impact of client distress factors on auditors' propensity to issue GCOs. The search engine enables us to examine the entire population of 10-K filings from 1995 to 2015 and also to obtain delisting data, which are not readily available in commercial databases. Contrary to prior research, we find that the survival rate of first-time GCOs is much lower when we use delisting as a measure of financial viability. Around 26 percent of the companies that receive their first GCOs are delisted within a period of one year of the audit opinion date, and 50 percent of the companies that receive their first GCOs are delisted within a period of three years. The bankruptcy rate of first-time GCO companies within one year is around 9 percent. Such evidence may prove useful to the PCAOB's effort to expeditiously assess the intended benefit of GCOs. In addition, we find that the propensity of auditors to issue GCOs varies for each distress factor.

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.

Possibilities and limitations of the use of bankruptcy prediction models for insolvency prevention

2021 ◽  
pp. 73-94
Author(s):  
Olga Lvova
Keyword(s):  
Financial Reporting ◽  
Financial Distress ◽  
Financial Stability ◽  
Prediction Models ◽  
Organizational Factors ◽  
Bankruptcy Prediction ◽  
Operating Conditions ◽  
Advantages And Disadvantages ◽  
Qualitative Variables ◽  
The Impact

The paper provides the solution to the problem of an integrated classification of existing bankruptcy prediction based on the content analysis of 270 relevant foreign and Russian publications issued within a period of 1910-2020. The author identifies two main groups of models– normative and positive, with the latter categorized into expert, mixed and objective including traditional statistical models and artificial intelligent techniques; and considers the specific features of certain predicting models, their advantages and disadvantages. He then reveals the economic content of such models and the set of ratios applied for identifying company’s financial distress with the following conclusions: approaches to the variables selection are rarely justified, indicators are usually borrowed from previous models or generated automatically by the database configuration; the accounting approach to bankruptcy forecasting based on financial ratios prevails and has serious limitations for Russian companies; the most significant market, value and qualitative variables indicating a decline in the business financial stability are highlighted. Significant limitations of the general use of bankruptcy prediction models for making decisions aimed at insolvency prevention are identified: the inability to anticipate the impact of informal factors that are irregular, unable to extrapolate, and affect companies in different ways; the need to take into account the economic conditions of the national economy, financial reporting standards, and the level of availability of diverse data; the impossibility of creating a universal indicative basis to identify decline of sustainability of any business due to the high volatility of operating conditions in Russia. Bayesian methods and nowcasting, as well as the development of forecasting models for certain industries, are promising areas for the development of modern approaches to bankruptcy prediction, but the fundamental activity for preventing insolvency is not forecasting by models, but the implementation of continuous monitoring of the overall business performance in relation to influencing market, operational, investment, financial, managerial and organizational factors, taking into account significant qualitative variables.

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