8,824 research outputs found

    Scoring Models of Bank Credit Policy Management

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    The aim of this paper is to present how credit scoring models can be used in financial institutions, in this case in banks, in order to simplify credit lending. Unlike traditional models of credit analysis, scoring models provides valuation based on numerical score who represent clients’ possibility to fulfil their obligation. Using credit scoring models, bank can create a numerical snapshot of consumers risk profile. One of the most important characteristic of scoring models is objectivity where two clients with the same characteristics will have the same credit rating. This paper presents some of credit scoring models and the way that financial institutions use them

    Credit ratings and credit risk

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    This paper investigates the information in corporate credit ratings. We examine the extent to which firms' credit ratings measure raw probability of default as opposed to systematic risk of default, a firm's tendency to default in bad times. We find that credit ratings are dominated as predictors of corporate failure by a simple model based on publicly available financial information (`failure score'), indicating that ratings are poor measures of raw default probability. However, ratings are strongly related to a straightforward measure of systematic default risk: the sensitivity of firm default probability to its common component (`failure beta'). Furthermore, this systematic risk measure is strongly related to credit default swap risk premia. Our findings can explain otherwise puzzling qualities of ratings.Credit Rating, Credit Risk, Default Probability, Forecast Accuracy, Systematic Default Risk

    Predicting successful "PER" reorganizations: Testing the applicability of Altman Z-Score on Portuguese companies

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    The main objective of this dissertation is to test the applicability of Altman's bankruptcy prediction model of private companies in differentiating between companies that enter into “PER” and are successful in having an approved recovery plan and those that are unsuccessful. For this, the universe of companies that entered “PER” between May 2012 and December 2016 was collected, which translated into a viable sample of work consisting of 2,189 companies. The conclusions obtained allow us to state that when applied to the selected sample, the Altman model for private companies, in its initial formulation does not allow a proper distinction between the two groups of companies identified. Through the re-estimation of the Altman model, it was possible to obtain models that achieved results that were more positive. However, it is not possible to state that the model could robustly differentiate between companies with an approved plan and those without a plan approved without a significant error margin.A presente dissertação tem como principal objectivo testar a aplicabilidade do modelo de previsĂŁo de falĂȘncias de Altman para empresas privadas na diferenciação entre as empresas que entram em “PER” e conseguem obter um plano de recuperação aprovado e aquelas que nĂŁo o conseguem. Para tal, foi recolhido o universo de empresas que entraram em “PER” entre Maio de 2012 e Dezembro de 2016, no que se traduziu numa amostra viĂĄvel de trabalho constituĂ­da por 2,189 empresas. As conclusĂ”es obtidas permitem afirmar que quando aplicado Ă  amostra seleccionada, o modelo de Altman para empresas privadas, na sua formulação inicial, nĂŁo permite distinguir correctamente entre os dois grupos de empresas identificados. AtravĂ©s da re-estimação dos coeficientes do modelo de Altman, foi possĂ­vel obter modelos que obtivessem melhores resultados sem, no entanto, se poder afirmar que se obteve um modelo que cumprisse robustamente com o objectivo proposto de diferenciação entre empresas com plano aprovado e sem plano aprovado

    Critical factors for insolvency prediction: Towards a theoretical model for the construction industry

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    © 2016 Informa UK Limited, trading as Taylor & Francis Group. Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency factors for a high-performance CI-IPM, this study used three approaches. Firstly, systematic review was used to identify all existing factors. Secondly, frequency of factor use and accuracy of models in the reviewed studies were analysed to establish the important factors. Finally, using a questionnaire survey of CI professionals, the importance levels of factors were validated using the Cronbach's alpha reliability coefficient and significant index ranking. The findings show that the important quantitative factors are profitability, liquidity, leverage, management efficiency and cash flow. While important qualitative factors are management/owner characteristics, internal strategy, management decision making, macroeconomic firm characteristics and sustainability. These factors, which align with existing insolvency-related theories, including Porter's five competitive forces and Mintzberg's 5Ps (plan, ploy, pattern, position and perspective) of strategy, were used to develop a theoretical framework. This study contributes to the debate on the need to amalgamate qualitative and quantitative factors to develop a valid CI-IPM

    A Back Propagation Neural Network Model with the Synthetic Minority Over-Sampling Technique for Construction Company Bankruptcy Prediction

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    Improving model accuracy is one of the most frequently addressed issues in bankruptcy prediction. Several previous studies employed artificial neural networks (ANNs) to improve the accuracy at which construction company bankruptcy can be predicted. However, most of these studies use the sample-matching technique and all of the available company quarters or company years in the dataset, resulting in sample selection biases and between-class imbalances. This study integrates a back propagation neural network (BPNN) with the synthetic minority over-sampling technique (SMOTE) and the use of all of the available company-year samples during the sample period to improve the accuracy at which bankruptcy in construction companies can be predicted. In addition to eliminating sample selection biases during the sample matching and between-class imbalance, these methods also achieve the high accuracy rates. Furthermore, the approach used in this study shows optimal over-sampling times, neurons of the hidden layer, and learning rate, all of which are major parameters in the BPNN and SMOTE-BPNN models. The traditional BPNN model is provided as a benchmark for evaluating the predictive abilities of the SMOTE-BPNN model. The empirical results of this paper show that the SMOTE-BPNN model outperforms the traditional BPNN

    Financial Risks: Cases Of Non-Financial Enterprises

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    Corporate Bankruptcy Prediction

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    Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy

    A Balanced Theory of Sourcing, Collaboration and Networks

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    In a synthesis of recent advances, this article gives a fresh, balanced theory of inter-organizational relations. It integrates competence and governance perspectives. It considers the choice between mergers/acquisitions and alliances. It offers a toolbox of instruments to govern relational risk, and the contingencies for their selection. Relationships can last too long. Therefore, the article also looks at how to end relationships. Beyond dyads of collaborating firms, it includes effects of network structure and position.corporate governance;inter-organizational relations;organizational behavior;inter-firm alliances;collaboration
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