A Review of Bankruptcy Prediction Studies: 1930-Present

One of the most well-known bankruptcy prediction models was developed by Altman [1968] using multivariate discriminant analysis. Since Altman\u27s model, a multitude of bankruptcy prediction models have flooded the literature. The primary goal of this paper is to summarize and analyze existing research on bankruptcy prediction studies in order to facilitate more productive future research in this area. This paper traces the literature on bankruptcy prediction from the 1930\u27s, when studies focused on the use of simple ratio analysis to predict future bankruptcy, to present. The authors discuss how bankruptcy prediction studies have evolved, highlighting the different methods, number and variety of factors, and specific uses of models. Analysis of 165 bankruptcy prediction studies published from 1965 to present reveals trends in model development. For example, discriminant analysis was the primary method used to develop models in the 1960\u27s and 1970\u27s. Investigation of model type by decade shows that the primary method began to shift to logit analysis and neural networks in the 1980\u27s and 1990\u27s. The number of factors utilized in models is also analyzed by decade, showing that the average has varied over time but remains around 10 overall. Analysis of accuracy of the models suggests that multivariate discriminant analysis and neural networks are the most promising methods for bankruptcy prediction models. The findings also suggest that higher model accuracy is not guaranteed with a greater number of factors. Some models with two factors are just as capable of accurate prediction as models with 21 factors

A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods

The purpose of this paper is to present two different approaches of financial distress pre-warning models appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) market from 2002 through 2008. We present a binary logistic regression with paned data analysis. With the pooled binary logistic regression we build a model including more variables in the regression than with random effects, while the in-sample and out-sample forecasting performance is higher in random effects estimation than in pooled regression. On the other hand we estimate an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell (Gbell) functions and we find that ANFIS outperforms significant Logit regressions in both in-sample and out-of-sample periods, indicating that ANFIS is a more appropriate tool for financial risk managers and for the economic policy makers in central banks and national statistical services

A Review and Bibliography of Early Warning Models

This note is intended to share some observations regarding a non-exhaustive collection of the early warning literature from 1971 to 2011. Evolution of the interest in early warning models, methodological spectrum of studies and coverage of economic variables are briefly discussed in addition to providing a bibliography.Early warning systems, bibliometric analysis

Testing of OrgPlan Conversion Planning software (OF0331)

OrgPlan is a computer package designed to support farmers and consultants in planning a conversion to organic farming. It consists of two main elements: the basic planning module and a database with data for organic, in-conversion and conventional data. It was developed with DEFRA funding (OF 0159) by a partnership between the University of Wales, Aberystwyth, the University of Hertfordshire, Elm Farm Research Centre and SAC. The objective of this work was to obtain feedback before its general release on the suitability of OrgPlan in supporting the process of planning a conversion to organic farming. Given the risks of the organic conversion process and the sensitive nature of the financial reports that OrgPlan can generate, further testing with consultants experienced in organic conversion planning was carried out. The work was broken down in four objectives. Independent of this, OrgPlan has been used by the contractor in the context of research work, in particular the Modelling of Strategies of Organic Milk Production (OF 0146). Objective 1: Update of standard data The contractor updated the OrgPlan database with data from the 2002/03 Organic Farm Management Handbook and other sources. Objective 2: Workshops and Field testing of the software Three workshops with a total 22 consultants were held during which they were given a basic introduction to the use of OrgPlan and had a first opportunity to use the software on their own computer or appropriate workstations. OrgPlan can effectively support several aspects of a first broad brush planning of an organic conversion (rotation planning, cropping and livestock enterprises, feasibility of a proposed organic scenario in terms of financial output, nutrient and forage budgets) and can assist with more detailed financial planning of investments, leading to Profit and Loss and Cash-Flow forecasts. OrgPlan could have a wider application in whole farm planning, but this would require extending the database to cover a wider range of enterprises common on conventional farms. Key strengths identified by the consultants (not in order of importance) • Financial planning • Availability of basic enterprise data set • Rotation planning and nutrient budgets • Combination of financial and nutrient data in one package • Create different scenarios giving instant access for reassessment of options • Possibility to ‘tweak' a scenario • Library, navigation around the collection is excellent • Help topics clear and straightforward • Broad brush planning, particularly for farms planning new enterprises Key weaknesses (not in order of importance) • Limited range of enterprises in the database, particularly for horticultural crops • Problems with set-up, use of database and understanding all functions • Need for regular updates of the dataset • P and K Fertilisers routinely included in organic enterprises • Data entry in some sections is long-winded Objective 3: Essential corrections to the software and update of advisory section • A list of problems and suggestions was compiled. All essential changes will be implemented before a release of the software. Other suggestions, which entail more complicated programming work, are included in a as ideas for future development of OrgPlan. Objective 4: Final report This is the final report submitted to DEFRA. The contractor will also submit to DEFRA a concept outlining the steps to be taken for the release of the software, which is planned for autumn 2003

Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks

Fiscal contingency planning for banking crises

There is constant demand for an estimate of the likely fiscal costs of future banking crises, but little precision can be expected in such an estimate. The author shows how information that is typically available to authorities could be used to get a general sense of the order of magnitude of the direct fiscal liability. What is required for such an estimate? 1) Information about the size and composition of the bank's balance sheets. 2) Expert assessments of the accuracy of the accounting data and of specific short-term risks to which the components are known to be subject. The author's method distinguishes between losses that have already crystallized and the changing risks for the immediate future. By including contingency planning for banking collapse in their fiscal calculations, authorities may risk destabilizing expectations or worsening the moral hazard in the system. But the risks of contingency planning generally outweigh the risks of sending confused signals. Insisting on ignorance is a poor way to protect against announcement errors that trigger panic.Insurance&Risk Mitigation,Banks&Banking Reform,Financial Intermediation,Payment Systems&Infrastructure,Financial Crisis Management&Restructuring,Banks&Banking Reform,Financial Intermediation,Financial Crisis Management&Restructuring,Insurance&Risk Mitigation,National Governance

City of Milwaukee's Fiscal Condition: Between a Rock and a Hard Place

This report presents an analysis of the fiscal condition of the City of Milwaukee government, applying a professional financial evaluation system of the International City/County Management Association (ICMA). The city conducted this type of analysis internally during the 1990s, but it has done nothing similar this decade. In March 2009, the Forum released an evaluation of the finances of Milwaukee County also using the ICMA methodology. Milwaukee's city government currently is experiencing serious financial difficulties. The recession hit Milwaukee hard, as it has the region and state, and the negative impact on Milwaukee's businesses and property values has had financial repercussions on city coffers. In addition, the massive decline in stock prices has devalued pension investments. While ranked the second most secure public pension fund in the nation prior to the economic downturn, Milwaukee's pension fund now has an unfunded liability of more than $700 million