9,780 research outputs found

    Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress

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    Recently, applying the novel data mining techniques for evaluating enterprise financial distress has received much research alternation. Support Vector Machine (SVM) and back propagation neural (BPN) network has been applied successfully in many areas with excellent generalization results, such as rule extraction, classification and evaluation. In this paper, a model based on SVM with Gaussian RBF kernel is proposed here for enterprise financial distress evaluation. BPN network is considered one of the simplest and are most general methods used for supervised training of multilayered neural network. The comparative results show that through the difference between the performance measures is marginal; SVM gives higher precision and lower error rates.Comment: 13 pages, 1 figur

    Hybrid model using logit and nonparametric methods for predicting micro-entity failure

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    Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

    Using computer simulation in operating room management: impacts of information quality on process performance

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    High quality information has a significant impact on improving operation performance and patient satisfaction, as well as resolving patient disputes. Based on the analysis of the perioperative process, information quality is considered as an important contributory factor in improving patient throughput. In this paper, we propose a conceptual framework to use computer simulations in modeling information flow of hospital process for operating room management (ORM). Additionally, we conduct simulation studies in different levels of the information quality for ORM. The results of our studies provide evidence that information quality can drive process performance in several phases of the ORM

    Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables

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    The use of non-parametric methodologies, the introduction of non-financial variables, and the development of models geared towards the homogeneous characteristics of corporate sub-populations have recently experienced a surge of interest in the bankruptcy literature. However, no research on default prediction has yet focused on micro-entities (MEs), despite such firms’ importance in the global economy. This paper builds the first bankruptcy model especially designed for MEs by using a wide set of accounts from 1999 to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs outperform the traditional logistic regression (LR) models. In addition, we also report that, thanks to the introduction of non-financial predictors related to age, the delay in filing accounts, legal action by creditors to recover unpaid debts, and the ownership features of the company, the improvement with respect to the use of solely financial information is 3.6%, which is even higher than the improvement that involves the use of the best ANN (2.6%)

    An empirical study on credit evaluation of SMEs based on detailed loan data

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    Small and micro-sized Enterprises (SMEs) are an important part of Chinese economic system.The establishment of credit evaluating model of SMEs can effectively help financial intermediaries to reveal credit risk of enterprises and reduce the cost of enterprises information acquisition. Besides it can also serve as a guide to investors which also helps companies with good credit. This thesis conducts an empirical study based on loan data from a Chinese bank of loans granted to SMEs. The study aims to develop a data-driven model that can accurately predict if a given loan has an acceptable risk from the bank’s perspective, or not. Furthermore, we test different methods to deal with the problem of unbalanced class and uncredible sample. Lastly, the importance of variables is analyzed. Remaining Unpaid Principal, Floating Interest Rate, Time Until Maturity Date, Real Interest Rate, Amount of Loan all have significant effects on the final result of the prediction.The main contribution of this study is to build a credit evaluation model of small and micro enterprises, which not only helps commercial banks accurately identify the credit risk of small and micro enterprises, but also helps to overcome creditdifficulties of small and micro enterprises.As pequenas e microempresas constituem uma parte importante do sistema económico chinês. A definição de um modelo de avaliação de crédito para estas empresas pode ajudar os intermediários financeiros a revelarem o risco de crédito das empresas e a reduzirem o custo de aquisição de informação das empresas. Além disso, pode igualmente servir como guia para os investidores, auxiliando também empresas com bom crédito. Na presente tese apresenta-se um estudo empírico baseado em dados de um banco chinês relativos a empréstimos concedidos a pequenas e microempresas. O estudo visa desenvolver um modelo empírico que possa prever com precisão se um determinado empréstimo tem um risco aceitável do ponto de vista do banco, ou não. Além disso, são efetuados testes com diferentes métodos que permitem lidar com os problemas de classes de dados não balanceadas e de amostras que não refletem o problema real a modelar. Finalmente, é analisada a importância relativa das variáveis. O montante da dívida por pagar, a taxa de juro variável, o prazo até a data de vencimento, a taxa de juro real, o montante do empréstimo, todas têm efeitos significativos no resultado final da previsão. O principal contributo deste estudo é, assim, a construção de um modelo de avaliação de crédito que permite apoiar os bancos comerciais a identificarem com precisão o risco de crédito das pequenas e micro empresas e ajudar também estas empresas a superarem as suas dificuldades de crédito

    Corporate Credit Rating: A Survey

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    Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.Comment: 11 page
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