This thesis investigates the anomalous underperformance of distressed stocks in the US computer and electronics industry. It shows that such anomaly can be explained by a parallel analysis of risk based rational pricing and profitability (earnings) levels to returns relationship propositions. For the 1990 to 2006 period, distressed stocks have on average underperformed their non-distressed counterparts. However, once the conditional relationship with profitability is taken into account, the distress risk is rewarded by a continuous positive return hence priced appropriately. In the computer and electronics industry growth stocks (low B/M) outperform on average value stocks (high B/M). The size factor has not been confirmed to be significant in explaining stock returns for this specific industry over the 1990 to 2006 period. The study also reveals that B/M and size factors do not proxy for distress risk. The B/M factor follows an inverted u-shape along the distress risk deciles axis. As result, stocks in low and high distress portfolios share similarly low B/M values. Cash flow based bankruptcy predictors estimated on a quarterly basis from a Cox proportional hazard model, that are used as proxy for a continuous distress risk factor in asset pricing tests, are able to predict bankruptcies at higher accuracy rates than the Z-Score as alternative measure.

In August, 2014, the Financial Accounting Standards Board issued an update concerning the disclosure of uncertainties about an entity’s ability to continue as a going concern. The standard requires an entities management to evaluate whether there is substantial doubt about the entity’s ability to continue as a going concern and to provide related footnote disclosures in certain circumstances. One consequence of this regulation is the need for guidance for audit testing of management’s assessments in each phase of the audit. This research evaluates the usefulness of bankruptcy prediction models as analytical tools in the planning stage of an audit for going concern assertions and questions the use of precision as the only measure of a model’s effectiveness. I use simulation to manipulate the fundamental accounting data within five bankruptcy prediction models, explore failure rates in an environment with materiality concerns, and consider the total change in market value due to simulated errors. Given the inherent limitations of the information environment and/or current prediction models, my results indicate auditors’ current failure rates are not an indication of audit failure. The results suggest that bright-line testing using bankruptcy prediction models are sensitive to materiality and that the cost trade-off between Type I and Type II errors is an important indicator of model choice.

Auditors must assess their clients' ability to function as a going concern for at least the year following the financial statement date. The audit profession has been severely criticized for failure to 'blow the whistle' in numerous highly visible bankruptcies that occurred shortly after unmodified audit opinions were issued. Financial distress indicators examined in this study are one mechanism for making such assessments. This study measures and compares the predictive accuracy of an easily implemented two-variable bankruptcy model originally developed using recursive partitioning on an equally proportioned data set of 202 firms. In this study, we test the predictive accuracy of this model, as well as previously developed logit and neural network models, using a realistically proportioned set of 14,212 firms' financial data covering the period 1981-1990. The previously developed recursive partitioning model had an overall accuracy for all firms ranging from 95 to 97% which outperformed both the logit model at 93 to 94% and the neural network model at 86 to 91%. The recursive partitioning model predicted the bankrupt firms with 33-58% accuracy. A sensitivity analysis of recursive partitioning cutting points indicated that a newly specified model could achieve an all firm and a bankrupt firm predictive accuracy of approximately 85%. Auditors will be interested in the Type I and Type II error tradeoffs revealed in a detailed sensitivity table for this easily implemented model.

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The main purpose of this paper is to provide evidence of financial leverage, liquidity, profitability, and firm size ability to predict bankruptcy of Swedish small and medium-sized enterprises (SMEs), and to create a bankruptcy prediction model for Swedish SMEs. The sample consists of 1086 Swedish SMEs, among which 543 did go bankrupt between 2015 and 2019. The paper employs logistic regression and Mann-Whitney U-test to test the hypotheses. The independent variables are derived from previous research and further filtered in a selection process, resulting in a final set of six variables. Financial leverage, liquidity, profitability, and firm size is found to have significantly predictive abilities to determine SME bankruptcy. The model has an overall classification accuracy of 77.6% out-of-sample and is able to classify 82.2% of the bankruptcies correctly out-of-sample.

Tyrimo objektas – įmonių finansinių sunkumų diagnozavimas. Darbo tikslas – sukurti įmonių finansinių sunkumų diagnozavimo modelį ir jį patikrinti pasirinktų įmonių pavyzdžiu. Tyrimo uždaviniai:  išanalizuoti ir susisteminti anksčiau sukurtus bankroto prognozavimo modelius ir jų testavimo rezultatus;  sudaryti įmonių finansinių sunkumų diagnozavimo modelį;  patikrinti sukurto modelio tinkamumą pasirinktų įmonių tarpe. Tyrimo metodai. Analizuojant bankroto prognozavimo modelius ir jų testavimo rezultatus atlikta mokslinės ir metodinės literatūros loginė ir lyginamoji analizė bei sintezė. Įmonių finansinių sunkumų diagnozavimo modeliui parengti skaičiuoti finansiniai santykiniai rodikliai bei atlikta statistinė analizė, rezultatai pateikti taikant monografinį metodą. Remiantis indukcijos metodu ir logine analize tikrintas sukurto finansinių sunkumų diagnozavimo modelio tinkamumas analizuotų įmonių tarpe. Tyrimo rezultatai. Pirmojoje darbo dalyje išanalizuota finansinių sunkumų esmė bei juos sąlygojančios priežastys, nustatytas finansinių sunkumų diagnozavimo būtinumas, išnagrinėti ir apibendrinti bankroto prognozavimo modeliai bei išskirti dažniausiai juose naudojami finansiniai santykiniai rodikliai. Antrojoje darbo dalyje sukurtas logistine analize paremtas finansinių sunkumų diagnozavimo modelis bei iškelta hipotezė, kad jis leidžia patikimai apskaičiuoti įmonių finansinių sunkumų tikimybę tirtų įmonių aibėje. Trečiojoje darbo dalyje sudarytas finansinių sunkumų... [toliau žr. visą tekstą] / Research object – diagnosis of company financial difficulties. Research aim - to create diagnostic model for company financial difficulties and to test it using cases of selected companies. Objectives:  to analyze and systematize previously developed bankruptcy prediction models and their test results;  to create a diagnostic model for company financial difficulties;  to analyze the relevance of the model with the selected companies. Research methods: logical and comparative analysis of scientific and methodical literature, statistical analysis, monographic method, induction method, logical analysis. Research results. The first part analyzes the financial difficulties and their underlying causes, establishes the necessity of the financial stress test model, analyzes and summarizes bankruptcy prediction models, and distinguishes the most frequently used financial ratios. The second part creates a logical analysis based diagnostic model for financial difficulties and presents hypothesis, that this model ensures reliable calculation of probability of company financial difficulties among the tested corporations. The third part uses randomly selected companies to test the created diagnostic model for financial difficulties. The developed model is suitable for the manufacturing companies, because it separates financially well standing companies from the ones that are having difficulties; however, it is not as suitable for the services and sales sector, because the model is only... [to full text]

Purpose: To investigate if Altman´s Z´-score model, which calculates financial distress, can be applied on companies established in Sweden and if the financial crisis in 2008 made previously healthy companies go bankrupt. Methodology: Quantitative studies with a positivistic foundation. Empirical data will be collected in order to examine if there is generalizability among the studied objects. Conclusions will be made by comparing the empirical data with the theoretical foundation. Financial distress in firms will be measured. Theoretical perspectives: Altman´s Z´-score model, designed to predict financial distress in private firms. Empirical foundation: A selection of 93 private firms that have gone bankrupt in the years 2008, 2009 or 2010. The firms selected all have a turnover that exceeds 20 million SEK. The years examined will be 2005 to 2009. Conclusion: As this study is carried out, the conclusion is that Altman´s Z´-score model cannot be applied on companies established in Sweden.

Darbo tikslas – sukurti modifikuotą bankroto diagnozavimo modelį ir jį patikrinti Lietuvos įmonių pavyzdžiu. Darbo uždaviniai: 1) ištyrus bankroto veiksnius, nustatyti jo atsiradimo priežastis; 2) atlikti teorinių bankroto tikimybės įvertinimo modelių analizę; 3) atlikus bankroto diagnozavimo modelių teorinę analizę, sukurti ir patikrinti modifikuotą bankroto diagnozavimo modelį. Darbo objektas – bankroto diagnozavimas. Tyrimo metodai: mokslinės literatūros analizė; loginė lyginamoji analizė bei sintezė; dokumentų, turinio analizė, apibendrinimo metodas; statistinė įmonių finansinių rodiklių analizė. Nagrinėjant įvairių autorių mokslinius veikalus apie bankroto diagnozavimo būtinumą, bankroto atsiradimo priežastis, ištirta bankroto tikimybė 6 atsitiktinai pasirinktose įmonėse, remiantis pinigų srautų analizės, E.I.Altmano modeliais bei sukurtas ir išanalizuotas modifikuotas bankroto diagnozavimo modelis. / Object of work – bankruptcy diagnostic. Aim of work – to create modificated bankruptcy diagnostic models and check its use in practise throught Lithuanian companies. Tasks of work: 1) Investigate bankruptcy elements and indentify reasons of bunkraptcy origin 2) To make theoretical analyzes of bankruptcy diagnostic models; 3) To create and develop modificated bankruptcy diagnostic model. The research methods: the analysis of scientific literature, the methods of logistic comparison, the methods of synthesis, content and documents methods, the methods of generalization, statistical financial analyzes. There were analyzed scientific works of various authors about nessecisity to predict bankropt, the reasons of bankruptcy nascency, investigated bankruptcy probability in 6 companies using cash flow, E.I.Altman models and was created and inquired modified bankruptcy model.

Denna uppsats berör ämnet konkurser och behandlar två olika sätt att förutsäga dessa. Dessa tillvägagångssätt är dels revisorers fortlevnadsvarningar, vilket är det tillvägagångssätt som används idag, och dels beräkning med en konkursprediktionsmodell. Syftet med denna studie är att diskutera möjligheten att förbättra träffsäkerheten hos revisorers fortlevnadsvarningar genom att tillämpa en standardiserad fortlevnadsvarning med hjälp av Z”-modellen. Möjligheten undersöks genom att jämföra träffsäkerheten mellan revisorer och Z”-modellen. För att bedöma lämpligheten hos Z”-modellen kartläggs även faktorer och nyckeltal som påverkar revisorers fortlevnadsbedömningar. Studien är av kvantitativ natur och den använda metoden är en dokumentstudie. Studiens urval är samtliga svenska aktiebolag med inledd konkurs under året 2017 och som vid deras senaste bokslut hade en omsättning överstigande tio miljoner kronor samt hade en revisor. Dessa bolag summerar till 336 stycken. De studerade bolagen underkastades en innehållsanalys av årsredovisningarna och tillhörande revisionsberättelser. Från årsredovisningarna inhämtades de siffror som sedan beräknades med hjälp av Z”-modellen, och från revisionsberättelserna inhämtades revisorernas uttalanden om bolagen. Denna data var grunden till hela analysen där träffsäkerheten i att förutsäga en konkurs jämfördes mellan de två olika tillvägagångssätten; revisorernas fortlevnadsvarningar kontra Z”- modellen. Resultatet visar att Z”-modellen är bättre på att förutsäga konkurser än vad revisorer är. Skillnaden i träffsäkerhet analyserades och förklarades med hjälp av teorier som pekar på att revisorerna och deras subjektiva bedömningar kan medföra bias och en underskattning av ett företags negativa siffror. Z”-modellen är å andra sidan objektiv varför dessa problem ej uppstår, vilket verkar medföra en bättre träffsäkerhet. Resultatet visar även att ett statistiskt signifikant samband finns mellan revisorers fortlevnadsvarningar och Z”-modellen. Detta indikerar att revisorer beaktar liknande nyckeltal som Z”-modellen. En faktor som, av studien att döma, påverkar revisorers träffsäkerhet är revisorns tillhörande revisionsbyrå. Detta förklarades av att de olika byråerna använder olika tumregler. / This paper deals with the subject of bankruptcies and deals with two different ways of predicting these. These approaches are partly the auditor's going concern warnings, which is the approach used today, and partly the calculation by a bankruptcy prediction model. The purpose of this study is to discuss the possibility to improve the accuracy of auditors’ going concern warnings by applying a standardized going concern warning with the help of the Z”-model. The possibility is examined by comparing the accuracy in predicting bankruptcies between auditors’ going concern warnings and the Z”-model. Furthermore, to evaluate the suitability of the model, factors and financial ratios that affects the auditors’ judgements are mapped. The method used was of quantitative nature and was a document study. The sample of the study is all Swedish companies that began bankruptcy during the year of 2017 and had a turnover of more than SEK ten million in the last fiscal year and had an auditor. These companies totaled 336. The companies studied were subjected to a content analysis by analyzing the annual reports and associated audit reports. From the annual reports, the figures were then calculated using the Z”- model, and from the audit reports, the auditors' statements about the companies were obtained. This data was the basis for the whole analysis, where the accuracy of predicting bankruptcy was compared between the two different approaches; Z”-model versus auditors' going concern warnings. The result shows that the Z”-model is better in predicting bankruptcy than the auditors. The difference was analyzed and explained using theories that indicate that the auditors and their subjective assessments may lead to bias and an underestimation of a company's negative figures. The Z”-model, on the other hand, is objective why these problems probably do not occur, which ultimately seems to lead to an overall better accuracy. Furthermore, the result shows that a statistically significant relationship exists between the auditors’ going concern warnings and the Z”-model. This indicates that the auditors asses similar financial ratios as the Z”-model. One factor that seems to affect the auditors’ accuracy is the auditor’s audit firm. This was explained by the different firms’ heuristics.

The main aim of the master thesis is to compare of different methodologies of financial health models and bankruptcy prediction models and their cause to company classification. The work deals with the applicability of models on the sample of 45 prosperous companies and 45 companies that were initiating in insolvency process. Sample contain about 33 % companies from building industry, 33 % retail, 16,7 % manufacturing industry and 16,7 % of the other industries mainly services. The special kind of contingency table - the confusion matrix - is used in the methodology to calculate sensitivity, specificity, negative predictive, false positive rate, accuracy, error and other classification statistics. Overall model accuracy is obtained as a difference between accuracy and error. Dependencies of models are acquired based on Pearson´s correlation coefficient. The changes (removing of grey zone and testing new cut-off points) in models are tested in the sensitivity analysis. In practise part there are about 12 financial models calculated (Altman Z´, Altman Z´´, Index IN99, IN01 and IN05, Kralicek Quicktest, Zmijewski model, Taffler model and its modification, Index Creditworthiness, Grunwald Site Index, Doucha´s Analysis). Only two financial indicators (ROA and Sales / Assets) in results were important as crucial part for more than one model. Then are classifications of companies in models determined. It shows that the best models according to overall accuracy are Zmijewski and Altman´s Z´´. On the other hand the worst models are index IN99 and both versions of Taffler´s model. The classification is not caused excessively by extreme values, year of the model creation or country of the origin (hypothesis 1). Based on results it is suggested that the bankruptcy prediction is an accurate forecaster of failure up to three years prior to bankruptcy in most examined models (hypothesis 2). It is observed that the type of model and industry influence the classification of models. In the end, the changes based on sensitivity analysis in the worst companies are made. All of three changes have increased overall classification accuracy of models.