The purpose of this doctoral thesis is to create a new bankruptcy prediction model and also to design how to use this model for the purposes of credit risk measuring. The starting-point of this work is the analysis of traditional bankruptcy models. It was found out that the traditional bankruptcy model are not enough effective in the current economic conditions and it is necessary to create a new ones. Based on the identified deficiencies of the traditional models a set of two new model series was created. The first series of the created models is based on the use of parametric methods, and the second one is based on the use of newer nonparametric approach. Moreover, a set of factors which are able to identify an imminent bankruptcy was analyzed. It was found, that significant signs of imminent bankruptcy can be identified even five years before the bankruptcy occurs. Based on these findings a new model was created. This model incorporates variables of static and even dynamic character for bankruptcy prediction purposes. The overall classification accuracy of this model is 92.27% of correctly classified active companies and 95.65% of correctly classified bankrupt companies.

Assessing the financial health of organizations remains a topic of great interest to economists, financial institutions, and invested stakeholders. For more than a century, research into financial distress has focused primarily on traditional applications of statistical analysis; however, modern advances in computational efficiency have created a significant opportunity for more sophisticated approaches. This thesis investigates the application of artificial intelligence on company bankruptcy prediction. The proposed neural network model is evaluated using the Polish Companies Bankruptcy dataset and yields a 5-year prediction accuracy of 96.5% and an AUC (area under receiver operating characteristic curve) measure of 92.4%.

[pt] O objetivo desta pesquisa é propor um modelo estatístico que possa estimar a probabilidade de ocorrência de falências ou concordatas em empresas de Internet. Após as recentes e drásticas perdas de capital em investimentos nas empresas desta nova indústria, instituições financeiras, pessoas físicas e todos os investidores desejam ter o conhecimento da real situação financeira das empresas denominadas pontocom. Esta pesquisa selecionou empresas norte-americanas que pediram falência ou concordata nas Cortes Norte-Americanas de Falências,entre 1999 e 2001, e empresas que não o fizeram, por amostragem de conveniência, que possuem ações listadas em bolsa e operam no e-commerce, isto é que vendem seus produtos ou serviços através da Internet. Utilizou, ainda, as demonstrações financeiras destas empresas para identificar, por intermédio de um teste T de amostras independentes, as variáveis mais significantes na discriminação dos dois grupos de empresas observados na amostra: o de empresas falidas e o de não-falidas. Analisadas as distribuições estatísticas das variáveis,o modelo de regressão logística demonstra ser o mais apropriado à pesquisa, por não possuir a premissa de normalidade multivariada. A conclusão final da pesquisa é a proposição de um modelo estatístico que indica a probabilidade de uma empresa de Internet falir ou não, com índice R2 de Nagelkerke de 0,887, percentual máximo de acerto na classificação de 97,4 por cento e que utiliza ainda não utilizadas em pesquisas anteriores similares. / [en] The objective of this research is to propose a statistical model that could estimate the probability of occurrence of bankruptcy for Internet companies. After the recent and drastic losses of investment capital in companies in this new sector of the economy, financial institutions, individuals and all investors wish to know the real financial position of these companies called dotcom. This research selected American companies that have filed a petition under the United States Bankruptcy Code, between 1999 and 2001, and companies which have not done it, by convenience sampling, that list their shares on stock markets and operate in e-commerce, i.e. companies that sell their products or services through the Internet. The financial statements of these companies were also used to identify,by analyzing a T test of independent samples, the most significant variables for discriminating the two observed groups in the sample: the bankrupt and the nonbankrupt companies. After analyzing the variables statistical distributions, a logistic regression model revealed to be the more appropriate for the research, for not having the multivariate normality assumption. The conclusion of this research proposes a statistical model which indicates the probability of an Internet company becoming bankrupt or not, with a Nagelkerke R Squared of 0,887, and an overall percentage of correct prediction of 97,4 percent. The model uses several variables not previously included in similar previous financial difficulties prediction models.

A causa delle conseguenze che il fenomeno comporta, sia sul piano finanziario sia sul fronte dell'economia reale, l'analisi e la previsione delle insolvenze societarie continua a rappresentare un argomento attuale nell'ambito della ricerca economica. I recenti sforzi condotti dal Comitato di Basilea verso la diffusione di criteri di valutazione del rischio di credito più precisi ed oggettivi, hanno ulteriormente accresciuto l'importanza della materia. L'obiettivo del presente studio è l'analisi del fenomeno del fallimento sul territorio italiano, al fine di valutare quali variabili sono più efficaci nell'individuazione di una situazione di dissesto dell'impresa. Per l'analisi si sono sviluppati dei modelli di previsione delle insolvenze in grado di individuare i segnali early warning di dissesto finanziario. L'analisi econometrica è basata su un campione ampio ed originale di fallimenti rilevati negli anni 2003 e 2004: a tal fine sono stati costituiti dei campioni comparabili di imprese fallite e non fallite ed è stato verificato, mediante l'applicazione di una metodologia logit, il potere previsivo di diversi indici di bilancio e di variabili di tipo non finanziario. I risultati ottenuti sono stati validati su un campione hold-out. L'analisi si evidenzia l'importanza delle caratteristiche del settore di attività nel determinare la forma del processo di fallimento: i modelli sector specific ottengono risultati migliori rispetto ai modelli generali stimati. Inoltre, alcuni fattori comuni ai diversi settori di attività si dimostrano particolarmente efficaci nella previsione dei dissesti aziendali: l'età, il livello di leverage e la composizione del debito d'impresa, così come la sua redditività. / Due to the consequences that the phenomenon entails both on the financial and real sides of the economy, the analysis and prediction of corporate failures continue to be a current topic in economic research. The recent efforts laid by the Basel Committee towards the diffusion of more precise and objective ways of assessing credit risk have further increased the importance of this matter. The purpose of the study is to analyse the bankruptcy phenomenon among Italian firms, in order to assess what firm-specific and industry variables are more important in determining corporate failure events. We develop a bankruptcy prediction model that aims at detecting early signals of financial distress. The econometric analysis is based on a wide and unique sample of recent failure events: comparable sets of bankrupt and non-bankrupt firms are identified and several prior balance-sheet and economic indicators are tested for their power in predicting failure probabilities in a logit modelling framework; model performances are cross-validated on hold-out samples. The analyses provide evidence of the importance of industry membership in determining and shaping corporate failure processes: sector-specific models produce a better assessment of financial distress than general ones. Also, some common factors emerge as important predictors of corporate collapse across different industries: age, gearing and the composition of a firm's debt, as well as its capability of generating profits.

Submitted by Roberto Shanrey Chieh (robertochieh@gmail.com) on 2018-08-24T13:44:12Z No. of bitstreams: 1 Dissertação MPE - Roberto Chieh - v23.pdf: 961097 bytes, checksum: 37ad879de70e42544f67413eccdf0401 (MD5) / Rejected by Joana Martorini (joana.martorini@fgv.br), reason: Prezado Roberto, boa tarde. Por gentileza a justar os seguintes itens: . Retirar o acento da palavra "Getúlio" . Na folha de assinaturas, retirar a frase "Folha de Aprovação" Após a realização dos ajustes, peço que faça uma nova submissão. Att, Joana Martorini on 2018-08-24T17:41:38Z (GMT) / Submitted by Roberto Shanrey Chieh (robertochieh@gmail.com) on 2018-08-24T17:49:02Z No. of bitstreams: 1 Dissertação MPE - Roberto Chieh - v24.pdf: 961253 bytes, checksum: 1b330f244eb5945c4c15d9d2165215e2 (MD5) / Approved for entry into archive by Joana Martorini (joana.martorini@fgv.br) on 2018-08-24T17:57:16Z (GMT) No. of bitstreams: 1 Dissertação MPE - Roberto Chieh - v24.pdf: 961253 bytes, checksum: 1b330f244eb5945c4c15d9d2165215e2 (MD5) / Approved for entry into archive by Isabele Garcia (isabele.garcia@fgv.br) on 2018-08-27T13:48:01Z (GMT) No. of bitstreams: 1 Dissertação MPE - Roberto Chieh - v24.pdf: 961253 bytes, checksum: 1b330f244eb5945c4c15d9d2165215e2 (MD5) / Made available in DSpace on 2018-08-27T13:48:01Z (GMT). No. of bitstreams: 1 Dissertação MPE - Roberto Chieh - v24.pdf: 961253 bytes, checksum: 1b330f244eb5945c4c15d9d2165215e2 (MD5) Previous issue date: 2018-07-31 / O objetivo dessa dissertação é identificar os melhores modelos para prever falência de empresas dos setores de mineração, óleo e gás no período entre 1998 e 2017. Em termos metodológicos, buscou-se estimar um modelo de regressão logística para prever as falências das empresas por meio de indicadores financeiros. Estimam-se modelos com dados anuais e trimestrais utilizando informações dos últimos três, dois e um ano anteriores às falências, contados a partir de um ano antes da formalização da falência. Conclui-se que o melhor modelo é aquele que utiliza as informações mais recentes, do último ano, e com dados trimestrais. As variáveis de patrimônio líquido sobre passivo total e fluxo de caixa de investimentos sobre passivo total se destacaram dentre os demais indicadores, sendo somente a primeira significativa em todos os modelos. O melhor modelo teve 79,1% de acerto geral e 85,5% de acerto para as empresas que faliram. / The objective of this study is to identify the best models for predicting bankruptcy of companies from the mining, oil and gas industries between 1998 and 2017. It was estimated a logistic regression model to predict business failure given their financial indicators. It was estimated models with yearly and quarterly information figures using figures from the last three years, last two years, and also last one year prior to the year just before the formalization of the bankruptcy event. The results show that the best model is the one using the most recent information, from the last one year, and using quarterly available data. The ratios total equity to total liabilities and cash flow from investments to total liabilities are the most important indicators to predict bankruptcy, even though only the first one is significant in all models. The best model correctly predicted 79.1% among all firms and 85.5% of the firms that went bankrupt.

The aim of this study is to evaluate feasibility of using Bayes classifiers for predicting corporate bankruptcies. The results obtain show that Bayes classifiers do reach comparable results to then more commonly used methods such the logistic regression and the decision trees. The comparison has been carried out based on Czech and Polish data sets. The overall accuracy rate of these so called naive Bayes classifiers, using entropic discretization along with the hybrid pre-selection of the explanatory attributes, reaches 77.19 % for the Czech dataset and 79.76 % for the Polish set respectively. The AUC values for these data sets are 0.81 and 0.87. The results obtained for the Polish data set have been compared to the already published articles by Tsai (2009) and Wang et al. (2014) who applied different classification algorithms. The method proposed in my study, when compared to the above earlier works, comes out as quite successful. The thesis also includes comparing various approaches as regards the discretisation of numerical attributes and selecting the relevant explanatory attributes. These are the key issues for increasing performance of the naive Bayes classifiers

Research question: This thesis analyzes the relationship between Altman’s Z’-and Z’’ score model in order to investigate the suitability of the models on the swedish small and medium companies during 2015-2019. Furthermore, analysis of previous research key figures has been examined in more detail to identify which of the individual key figures can be categorized as an early warning signal. Purpose: The purpose with this study is to explore which and when early warning signals can be read in annual reports between inactive and active Swedish tourist companies and to investigate the relationship between the accuracy of bankruptcy prediction models between active and bankrupt companies. Method: The study uses a quantitative method with a deductive approach, z-test and onesided analysis of variance ANOVA to analyze the accuracy of bankruptcy prediction models and identify how the key figures differ between active and inactive companies. Conclusion: The study shows that Altman's Z 'and Z' scores predict bankruptcies better than specify continued operations, are best suited for active companies and have the highest accuracy one year in advance than a longer period. On the other hand, 7 out of 15 key figures examined have identified significant average value differences between active and bankrupt companies, where some had a higher value for bankrupt companies and others had lower ones. / Problemställning: I denna studien har relationssamband mellan Altmans Z’- och Z’’- scoremodell analyserats för att undersöka hur modellerna lämpar sig på den svenska små- och medelstora konkur-respektive aktiva turistföretag mellan 2015-2019. Ytterligare har analys av tidigare forsknings nyckeltal undersökts närmare för att identifiera vilka av de enskilda nyckeltalen kan kategoriseras som en tidig varningssignal. Syfte: Studiens avsikt är att utforska vilka och när tidiga varningssignaler kan utläsas i årsredovisningar mellan inaktiva och aktiva svenska turistföretag samt undersöka relationssambandet gällande konkursprediktionsmodellers träffsäkerhet mellan aktiva- och konkursföretag. Metod: I studien används en kvantitativ metod med en deduktiv ansats, z-test och ensidig variansanalys ANOVA för att analysera konkursprediktionsmodellers träffsäkerhet samt identifiera hur nyckeltalen skiljer sig mellan aktiva och konkursföretag. Slutsats: Studien visar att Altmans Z”och Z’-score förutser konkurser bättre än preciserar fortsatt verksamhet, lämpar sig bäst på aktiva företag samt har den högsta träffsäkerhet ett år i förväg än längre period. Däremot har 7 av 15 undersökta nyckeltal identifierat signifikanta medelvärdesskillnader mellan aktiva och konkursföretag där några hade ett högre värde gällande konkursföretag och andra hade lägre.

This diploma thesis deals with the issue of bankruptcy prediction of small and medium-sized enterprises operating in the manufacturing industry in selected Central European countries. The theoretical part of the thesis defines the concepts related to the prediction of bankruptcy and methods of model creation. The analytical part of the work includes testing the accuracy of selected bankruptcy model by other authors and creating a new bankruptcy model. The accuracy of the newly created model is then compared with the accuracy of selected models by other authors.

This thesis deals with evaluating the financial situation of the company LIPOELASTIC a.s. through deployment of specific tools of financial analysis. The data necessary for financial analysis comes from financial statements of the company. Structure of the thesis is divided onto three parts. Theoretical part contains a set of crucial terms, methods and workflows which are important for designing the individual parts of financial analysis. These methods are practically applied in the practical part. Last part includes the proposals and measures for enhancing financial situation of the company.

[pt] Os estudos de modelos de previsão de falência tiveram seu início há quase 90 anos, sempre com o intuito de ser uma ferramenta de gestão útil para analistas e gestores das empresas. Embora as primeiras pesquisas sejam antigas, o assunto continua atual. Diversos setores da economia passaram, ou passam, por crises ao longo do tempo e não foi diferente para a indústria de aviação. Nesse contexto, o presente trabalho usou dados históricos de indicadores financeiros das empresas aéreas americanas de um período de três décadas para elaborar quatro modelos de previsão de falência e comparar suas performances preditivas com o Modelo Z-Score. Todas as elaborações foram calibragens do Modelo Z-Score, usando técnicas de simulação e estatística. Duas usaram Análise Discriminante Múltipla (MDA) e duas utilizaram Bootstrap junto com MDA. Um par de cada método utilizou as variáveis originais do Modelo Z-Score e o outro par apresentou sugestão de novo conjunto de variáveis. Os resultados mostraram que o modelo de previsão mais preciso, com 75,0 porcento de acerto na amostra In-Sample e 79,2 porcento na Out-of-Sample, utilizou o conjunto original de variáveis e as técnicas Bootstrap e MDA. / [en] Studies of bankruptcy prediction models started almost 90 years ago, with the intention of being a useful management tool for analysts and managers. Although the first researches are ancient, the subject remains current. Several sectors of the economy have experienced, or are experiencing, crises over time and the aviation industry is no exception. In this context, the present work used historical data of financial indicators of American airlines over a period of three decades to develop four models of bankruptcy forecast and compared their predictive performances with the Z-Score Model. All proposed models were calibrations of the Z-Score model, using simulation and statistical techniques. Two models were generated using Discriminant Analyzes Multiple (MDA) and two using Bootstrap along with MDA. A pair of each method used the original variables of the model s Z-Score and the other pair presented a novel set of variables. Results showed that the most accurate forecasting model, with 75.0 percent accuracy in-sample and 79.2 percent out-of-sample, used the original variables of the model s Z-Score and the Bootstrap e MDA techniques.