A Meta‐learning Framework for Bankruptcy Prediction

The implication of corporate bankruptcy prediction is important to financial institutions when making lending decisions. In related studies, many bankruptcy prediction models have been developed based on some machine‐learning techniques. This paper presents a meta‐learning framework, which is composed of two‐level classifiers for bankruptcy prediction. The first‐level multiple classifiers perform the data reduction task by filtering out unrepresentative training data. Then, the outputs of the first‐level classifiers are utilized to create the second‐level single (meta) classifier. The experiments are based on five related datasets and the results show that the proposed meta‐learning framework provides higher prediction accuracy rates and lower type I/II errors when compared with the stacked generalization classifier and other three widely developed baselines, such as neural networks, decision trees, and logistic regression. Copyright © 2011 John Wiley & Sons, Ltd.     

Business failure prediction using decision trees

Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high‐profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non‐parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd.     

Rough sets bankruptcy prediction models versus auditor signalling rates

Both international and US auditing standards require auditors to evaluate the risk of bankruptcy when planning an audit and to modify their audit report if the bankruptcy risk remains high at the conclusion of the audit. Bankruptcy prediction is a problematic issue for auditors as the development of a cause–effect relationship between attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. Recent research indicates that auditors only signal bankruptcy in about 50% of the cases where companies subsequently declare bankruptcy. Rough sets theory is a new approach for dealing with the problem of apparent indiscernibility between objects in a set that has had a reported bankruptcy prediction accuracy ranging from 76% to 88% in two recent studies. These accuracy levels appear to be superior to auditor signalling rates, however, the two prior rough sets studies made no direct comparisons to auditor signalling rates and either employed small sample sizes or non‐current data. This study advances research in this area by comparing rough set prediction capability with actual auditor signalling rates for a large sample of United States companies from the 1991 to 1997 time period. Prior bankruptcy prediction research was carefully reviewed to identify 11 possible predictive factors which had both significant theoretical support and were present in multiple studies. These factors were expressed as variables and data for 11 variables was then obtained for 146 bankrupt United States public companies during the years 1991–1997. This sample was then matched in terms of size and industry to 145 non‐bankrupt companies from the same time period. The overall sample of 291 companies was divided into development and validation subsamples. Rough sets theory was then used to develop two different bankruptcy prediction models, each containing four variables from the 11 possible predictive variables. The rough sets theory based models achieved 61% and 68% classification accuracy on the validation sample using a progressive classification procedure involving three classification strategies. By comparison, auditors directly signalled going concern problems via opinion modifications for only 54% of the bankrupt companies. However, the auditor signalling rate for bankrupt companies increased to 66% when other opinion modifications related to going concern issues were included. In contrast with prior rough sets theory research which suggested that rough sets theory offered significant bankruptcy predictive improvements for auditors, the rough sets models developed in this research did not provide any significant comparative advantage with regard to prediction accuracy over the actual auditors' methodologies. The current research results should be fairly robust since this rough sets theory based research employed (1) a comparison of the rough sets model results to actual auditor decisions for the same companies, (2) recent data, (3) a relatively large sample size, (4) real world bankruptcy/non‐bankruptcy frequencies to develop the variable classifications, and (5) a wide range of industries and company sizes. Copyright © 2003 John Wiley & Sons, Ltd.     

《破产法》实施中的政府行为分析及对策

20世纪80年代中期制定的现行<破产法>在实施过程中带有较为浓厚的行政色彩,破产行为成了地道的政府行为.为使<破产法>真正成为市场经济条件下实现资源重新配置的手段,笔者认为应尽快修改完善<破产法>,并尽快出台与之相配套的法律法规,降低破产实施成本,健全企业的政府指导制度.     

Predicting bankruptcy using recursive partitioning and a realistically proportioned data set

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. Copyright © 2000 John Wiley & Sons, Ltd.     

带干扰的广义双Poisson风险模型的破产概率

将广义Poisson风险模型推广到带干扰的广义双Poisson风险模型,并利用鞅的方法得出了破产概率所满足的Lundberg不等式及其一般公式。     

有破产成本的风险债务估值未定权益分析

破产成本是企业破产时发生的费用，从而降低了企业资产价值。破产时，债权人只能得到扣除破产成本后的企业资产价值，因而对企业的债务价值有影响。运用未定权益分析方法，给出风险债务估值的基本思路及风险债务价值满足的微分方程，得到永久性债务估值模型，可作为长期债务估值的近似。运用未定权益定价和随机计算，导出了一个包含破产成本的风险债务估值公式，该公式能说明投资策略、股利策略对风险债务价值的影响。最后将得到的结果与默顿、布莱克等人的工作进行了比较。     

双复合泊松风险保险公司最优投资策略

本文克服了保险资金在投资时间上的时滞限制以及保费以常数速率到达的不足,考虑保费和索赔同时都是随机到达的情况下受破产控制的保险公司最优投资策略问题,利用随机Lagrange方法获得了保险公司最优投资策略满足的解析式解.     

破产：国有危困企业的最佳选择

在我国,有关企业资产重组问题一开始就与企业破产问题联系在一起,并且是为了掩盖企业破产问题,取代破产处理而提出来的,全在市场经济条件下,资产重组并非可能取代破产,破产将会是国有危困企业的最佳选择。     

我国商业银行市场退出监管法律问题研究

商业银行是金融业的核心,在国民经济中占有重要地位。它和其他企业一样,会在激烈的市场竞争中遭遇优胜劣汰。但同时它又不同于一般的企业,其是一个高风险行业,具有金融脆弱性。银行系统的脆弱性又导致其经营具有很强的负外部性。国家有必要通过进一步完善商业银行破产法律制度、建立存款保险制度等手段来扩大银行退市体系中的正外部效应,减少或消除负外部效应,以降低金融风险,促进国民经济的健康发展。