Download or read book Predicting Default Probability Using Delinquency written by Asma Marouani and published by . This book was released on 2014 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Small businesses may be expected to be more likely to fail because they are more volatile, have less power in negotiations with financial and social partners, are more credit-rationed by credit managers, are less likely to benefit from their experience or 'learning effects', compared to large firms, and often operate in small markets. From this point of view, financial ratios seem to be irrelevant when modelling their default probabilities. This current research is an attempt to fine tune variables and to find more dynamic information to include in a point-in-time probability of default model.In this paper we explore the hypothesis that a firm's future default could be measured and explained solely by the historical data on the ability and willingness of a firm to pay its creditors. We use a credit scoring application to model default on a large data set of French Small and Medium-sized Enterprises (SMEs). We find that payment behavior data can be used to successfully predict SME bankruptcy in France and in a timely manner. New variables on late payment and delinquency are identified as alternatives to those usually used in failure models literature.

Download or read book An Empirical Analysis of Personal Bankruptcy and Delinquency written by David B. Gross and published by . This book was released on 2001 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper uses a new panel data set of credit card accounts to analyze credit card delinquency, personal bankruptcy, and the stability of credit risk models. We estimate duration models for default and assess the relative importance of different variables in predicting default. We investigate how the propensity to default has changed over time, disentangling the two leading explanations for the recent increase in default rates - a deterioration in the risk - composition of borrowers versus an increase in borrowers' willingness to default due to declines in default costs, including social, information, and legal costs. Even after controlling for risk-composition and other economic fundamentals, the propensity to default significantly increased between 1995 and 1997. By contrast, increases in credit limits and other changes in risk-composition explain only a small part of the change in default rates. Standard default models appear to have missed an important time-varying default factor, consistent with a decline in default costs.

Download or read book Determination of Default Probability in Auto Finance Through Predictive Analytics written by Huy D. Pham and published by . This book was released on 2017 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Banks and credit card lenders employ a system known as credit scoring to quantify the risk factors associated with each potential borrower. An excellent credit score usually assures the borrower’s ability and willingness to pay her/his loan. Due to the massive number of applications received daily as well as an increasing number of governmental regulatory requirements, credit scoring has become a standard in the banking industry. In this thesis, the concept of credit scoring and the theory and statistics behind it are explained thoroughly. In the application sections, different statistical methods, such as logistic regression, discriminant function analysis, binary decision tree analysis, and artificial neural networks are used to analyze real data collected from a credit bureau. The results and models developed from these different analyses are then compared to determine the best method for developing a credit score model. Due to the inherently large number of attributes associated with each loan borrower provided by the credit bureau, a principal component analysis is first used to reduce significantly the number of variables that will be considered for inclusion in the credit score model. Three selection methods such as forward selection, backward elimination, and stepwise regression are also utilized to determine which subset of variables is to be included in the final model. The conclusion of the thesis discusses the best method among the four mentioned statistical methods used to analyze the data, and reveals the best final credit score model for this study.

Download or read book Principles of Econometrics written by R. Carter Hill and published by John Wiley & Sons. This book was released on 2018-02-21 with total page 1808 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Econometrics, Fifth Edition, is an introductory book for undergraduate students in economics and finance, as well as first-year graduate students in a variety of fields that include economics, finance, accounting, marketing, public policy, sociology, law, and political science. Students will gain a working knowledge of basic econometrics so they can apply modeling, estimation, inference, and forecasting techniques when working with real-world economic problems. Readers will also gain an understanding of econometrics that allows them to critically evaluate the results of others’ economic research and modeling, and that will serve as a foundation for further study of the field. This new edition of the highly-regarded econometrics text includes major revisions that both reorganize the content and present students with plentiful opportunities to practice what they have read in the form of chapter-end exercises.

Download or read book Predicting and Pricing the Probability of Default written by Alessio Saretto and published by . This book was released on 2005 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we study how corporate bond defaults can be predicted using financial ratios and how the estimated default probability relates to the Fama-French factors, HML and SMB. We propose a default forecast model that outperforms existing models in correctly classifying both Default and Non-Default firms. Using the default probabilities generated by our model, we find evidences that support the interpretation of HML as a distress factor. Factor loadings are positively related to the firm default probabilities. HML is negatively correlated with changes in the level of aggregate financial distress.

Download or read book Trade Credit and Bank Credit written by Inessa Love and published by World Bank Publications. This book was released on 2005 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors study the effect of financial crises on trade credit in a sample of 890 firms in six emerging economies. They find that although provision of trade credit increases right after the crisis, it consequently collapses in the following months and years. The authors observe that firms with weaker financial position (for example, high pre-crisis level of short-term debt and low cash stocks and cash flows) are more likely to reduce trade credit provided to their customers. This suggests that the decline in aggregate credit provision is driven by the reduction in the supply of trade credit, which follows the bank credit crunch. The results are consistent with the "redistribution view" of trade credit provision, in which bank credit is redistributed by way of trade credit by the firms with stronger financial position to the firms with weaker financial stand "--World Bank web site.

Download or read book Audit Analytics in the Financial Industry written by Jun Dai and published by Emerald Group Publishing. This book was released on 2019-10-28 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes.

Download or read book Introduction to Linear Models and Statistical Inference written by Steven J. Janke and published by John Wiley & Sons. This book was released on 2005-09-15 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: A multidisciplinary approach that emphasizes learning by analyzing real-world data sets This book is the result of the authors' hands-on classroom experience and is tailored to reflect how students best learn to analyze linear relationships. The text begins with the introduction of four simple examples of actual data sets. These examples are developed and analyzed throughout the text, and more complicated examples of data sets are introduced along the way. Taking a multidisciplinary approach, the book traces the conclusion of the analyses of data sets taken from geology, biology, economics, psychology, education, sociology, and environmental science. As students learn to analyze the data sets, they master increasingly sophisticated linear modeling techniques, including: * Simple linear models * Multivariate models * Model building * Analysis of variance (ANOVA) * Analysis of covariance (ANCOVA) * Logistic regression * Total least squares The basics of statistical analysis are developed and emphasized, particularly in testing the assumptions and drawing inferences from linear models. Exercises are included at the end of each chapter to test students' skills before moving on to more advanced techniques and models. These exercises are marked to indicate whether calculus, linear algebra, or computer skills are needed. Unlike other texts in the field, the mathematics underlying the models is carefully explained and accessible to students who may not have any background in calculus or linear algebra. Most chapters include an optional final section on linear algebra for students interested in developing a deeper understanding. The many data sets that appear in the text are available on the book's Web site. The MINITAB(r) software program is used to illustrate many of the examples. For students unfamiliar with MINITAB(r), an appendix introduces the key features needed to study linear models. With its multidisciplinary approach and use of real-world data sets that bring the subject alive, this is an excellent introduction to linear models for students in any of the natural or social sciences.

Download or read book Validation of Risk Management Models for Financial Institutions written by David Lynch and published by Cambridge University Press. This book was released on 2022-12-31 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial models are an inescapable feature of modern financial markets. Yet it was over reliance on these models and the failure to test them properly that is now widely recognized as one of the main causes of the financial crisis of 2007–2011. Since this crisis, there has been an increase in the amount of scrutiny and testing applied to such models, and validation has become an essential part of model risk management at financial institutions. The book covers all of the major risk areas that a financial institution is exposed to and uses models for, including market risk, interest rate risk, retail credit risk, wholesale credit risk, compliance risk, and investment management. The book discusses current practices and pitfalls that model risk users need to be aware of and identifies areas where validation can be advanced in the future. This provides the first unified framework for validating risk management models.

Download or read book Forecasting Default Probability without Accounting Data written by Dean Fantazzini and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent default of the multinational giants Enron, Parmalat and Worldcom clearly showed how accounting data can be misleading and far away from the true financial situation of a company. When financial fraud takes place, the models that use accounting data to predict default probabilities cannot be used since their forecasts are completely unreliable. To avoid such problems, we propose a novel approach that uses stock prices only, and allows to model departures from normality in stock returns dynamics, too. The parametric bootstrap, based on a conditional marginal model, is used to estimate the distribution of these estimated probabilities and to construct confidence bands. We show an empirical example with quoted Russian stocks as well as with American, Italian and Russian defaulted stocks, whose financial statements were found to be irregular.

Download or read book The Oxford Handbook of Pricing Management written by Özalp Özer and published by Oxford University Press (UK). This book was released on 2012-06-07 with total page 977 pages. Available in PDF, EPUB and Kindle. Book excerpt: A definitive reference to the theory and practice of pricing across industries, environments, and methodologies. It covers all major areas of pricing including, pricing fundamentals, pricing tactics, and pricing management.

Download or read book Predicting Default Probabilities and Implementing Trading Strategies for Emerging Markets Bond Portfolios written by Stefania Ciraolo and published by . This book was released on 2002 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we address two main issues: the computation of default probability implicit in emerging markets bond prices and the impact on portfolio risks and returns of expected changes in default probability. Using a reduced-form model of the Duffie-Singleton (1999) type, weekly estimates of default probabilities for US Dollar denominated Global bonds of twelve emerging markets are extrapolated for the sample period 1997-2001. The estimation of a logit type econometric model shows that weekly changes of the default probabilities can be explained by means of some capital markets factors. Recursively estimating the logit model using rolling windows of data, out-of-sample forecasts for the dynamics of default probabilities are generated and used to form portfolios of bonds. The practical application provides interesting results, both in terms of testing the ability of a naive trading strategy based on model forecasts to outperform a quot;customized benchmarkquot;, and in terms of the model ability to actively manage the portfolio risk (evaluated in terms of VaR) with respect to a constant proportion allocation.

Download or read book Predicting Default Risk Under Asymmetric Binary Link Functions written by Yiannis Dendramis and published by . This book was released on 2019 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we propose the use of an asymmetric binary link function to extend the proportional hazard model for predicting loan default. The rationale behind this approach is that the symmetry assumption, that has been widely used in the literature, could be considered as quite restrictive, especially during periods of financial distress. In our approach we allow for a flexible level of asymmetry in the probability of default by the use of the skewed logit distribution. This enable us to estimate the actual level of asymmetry that is associated with the data at hand. We implement our approach to both simulated data and a rich micro dataset of consumer loan accounts. Our results provide clear cut evidence that ignoring the actual level of asymmetry leads to seriously biased estimates of the slope coefficients, inaccurate marginal effects of the covariates of the model, and overestimation of the probability of default. Regarding the predictive power of the covariates of the model, we have found that loan specific covariates, contain considerably more information about the loan default than macroeconomic covariates, which are often used in practice to carry out macroprudential stress testing.

Download or read book Predicting delinquency and crime written by Sheldon Glueck and published by . This book was released on 1960 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Consumer Credit Models written by Lyn C. Thomas and published by OUP Oxford. This book was released on 2009-01-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of credit scoring - the quantitative and statistical techniques to assess the credit risks involved in lending to consumers - has been one of the most successful if unsung applications of mathematics in business for the last fifty years. Now with lenders changing their objectives from minimising defaults to maximising profits, the saturation of the consumer credit market allowing borrowers to be more discriminating in their choice of which loans, mortgages and credit cards to use, and the Basel Accord banking regulations raising the profile of credit scoring within banks there are a number of challenges that require new models that use credit scores as inputs and extensions of the ideas in credit scoring. This book reviews the current methodology and measures used in credit scoring and then looks at the models that can be used to address these new challenges. The first chapter describes what a credit score is and how a scorecard is built which gives credit scores and models how the score is used in the lending decision. The second chapter describes the different ways the quality of a scorecard can be measured and points out how some of these measure the discrimination of the score, some the probability prediction of the score, and some the categorical predictions that are made using the score. The remaining three chapters address how to use risk and response scoring to model the new problems in consumer lending. Chapter three looks at models that assist in deciding how to vary the loan terms made to different potential borrowers depending on their individual characteristics. Risk based pricing is the most common approach being introduced. Chapter four describes how one can use Markov chains and survival analysis to model the dynamics of a borrower's repayment and ordering behaviour . These models allow one to make decisions that maximise the profitability of the borrower to the lender and can be considered as part of a customer relationship management strategy. The last chapter looks at how the new banking regulations in the Basel Accord apply to consumer lending. It develops models that show how they will change the operating decisions used in consumer lending and how their need for stress testing requires the development of new models to assess the credit risk of portfolios of consumer loans rather than a models of the credit risks of individual loans.

Download or read book Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and published by World Scientific. This book was released on 2020-07-30 with total page 5053 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

Download or read book Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning written by Segall, Richard S. and published by IGI Global. This book was released on 2022-01-07 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines.