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

Prediction of corporate financial distress : an application of the composite rule induction system

The economic consequence of corporate failure is enormous, especially for the stakeholders of public-held companies. Prior to a corporate failure, the firm’s financial status is frequently in distress. Consequently, finding a method to identify corporate financial distress as early as possible is clearly a matter of considerable interest to investors, creditors, auditors and other stakeholders. This paper uses a composite rule induction system (CRIS; Liang 1992) to derive rules for predicting corporate financial distress in Taiwan. In addition, this paper compares the prediction performance of cris, neural computing and the logit model. The empirical results indicate that both CRIS and neural computing outperform the logit model in predicting financial distress. Although both CRIS and neural computing perform rather well, CRIS has the advantage that the derived rules are easier to understand and interpret.La consecuencia económica de un fracaso corporativo es enorme, especialmente para los actores clave de las compañías públicas. En las fases previas a un fracaso corporativo, es común que el estatus financiero de la firma se encuentre normalmente en apuros. Consecuentemente, encontrar un método para identificar peligros financieros en una corporación tan pronto como sea posible es claramente un asunto con gran interés para los inversores, acreedores, auditores y otros actores clave. Este artículo usa un sistema de reglas inductivas compuestas (CRIS; Liang 1992) para elaborar reglas y patrones que ayuden a predecir problemas económicos en Taiwan. Además, este artículo compara el rendimiento y predicciones de CRIS, la informática neuronal y el modelo logístico. Los resultados empíricos indican que tanto CRIS como la informática neuronal funcionan generalmente bien a la hora de predecir los problemas financieros. Aunque ambos funcionan correctamente, CRIS tiene la ventaja de que sus reglas son más sencillas de entender e interpretar

A Case-Based Reasoning Approach to Bankruptcy Prediction Modeling.

This study examines the usefulness of an artificial intelligence method, case-based reasoning (CBR), in predicting corporate bankruptcy. Based on prior research, CBR is believed to be a viable method of predicting bankruptcy. Hypotheses are developed to test the usefulness of a CBR system and to compare the accuracy of such a system to the model considered to be the benchmark model in bankruptcy prediction, Ohlson\u27s (1980) nine-factor logistic regression (logit) model. Sample data consisting of manufacturing and industrial firms is drawn from the Compustat database in a 20:1 ratio of nonbankrupt to bankrupt firms, consistent with Ohlson\u27s (1980) proportions. Three CBR models representing one, two, and three years before bankruptcy are designed and developed using a CBR development tool, ReMind. Cross-validation is done using a 10% in-period holdout sample as well as a holdout sample of firms from outside the period from which the model is constructed. Three logit models based on Ohlson (1980) representing one, two, and three years before bankruptcy are constructed. The usefulness of the CBR system is determined by examination of type I and type II error rates. Chi-square statistics are used to compare the predictive accuracy of the three CBR models with the three logit models. The results indicate that the CBR method using ReMind is not useful in predicting corporate bankruptcy. It is believed that the small sample of bankrupt firms (relative to the sample size of nonbankrupt firms) contributes to the failure of these CBR models to accurately predict bankruptcy. Compared with two other studies that also use ReMind as development tools, there is evidence that the algorithm in ReMind does not accommodate small sample sizes. The results also indicate that CBR is not more accurate than the Ohlson (1980) logit model. Ohlson\u27s (1980) logit models attain a much higher accuracy rate than the CBR models and appear to be more stable over time than the CBR models

Transfer Learning in Dynamic Business Environments: An Application in Earnings Forecast for Public Firms

In dynamic business environments, the underlying true data pattern changes rapidly. Machine learning models built upon historical data may not be responsive to the changes. A simple solution is to re-train a machine learning model using the re-collected current data. However, current data are often scarce. Therefore, it would be optimal to adapt the machine learning model built on historical data to the current period. In this study, we propose a two-step transfer learning method for enhancing machine learning in dynamic data environments. Our insight is that, by comparing current data and historical data, we gain information on the change of data environments, which guides the training of machine learning using historical and current data sets simultaneously. In this research-in-progress, we evaluate our method and an existing state-of-art algorithm in the earnings prediction tasks. Preliminary results show the effectiveness of transfer learning in dynamic business environments

Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.info:eu-repo/semantics/publishedVersio

Predicting Corporate Bankruptcy in Pakistan A Comparative Study of Multiple Discriminant Analysis (MDA) and Logistic Regression

Purpose:- The aim of the study was to predict corporate bankruptcy in an emerging market like Pakistan by employing two statistical methods which are Discriminant Analysis (MDA) and Logistic Regression (Logit). It was also aimed at identifying the predicting accuracies of these statistical methods. Methodology:- This study had examined 35 bankrupt and 35 non-bankrupt companies which belongs to sector (non-financial) of Pakistan listed at KSE (Karachi Stock Exchange) over the period of seventeen years i.e. 1996 to 2012. Here, we had compared the accuracy and predictive ability of two statistical methods which are Discriminant Analysis (MDA) and Logistic Regression (Logit) and was expecting that Logistic Regression (Logit) accuracy and predictive ability will supercede Multiple Discriminant Analysis (MDA) accuracy and predictive ability. Findings:- The results have proved that Logistic Regression accuracy and predictive ability (80%) is better than the accuracy and predictive ability (78.6%) of Multiple Discriminant Analysis (MDA). It is proved that both the models identified the same amount of predictors for bankruptcy prediction. The variables identified by Logistic Regression are Shareholder’s Equity / Debt (Book Value), EBIT / Current Liabilities, Retained Earnings / Total Assets and variables identified by Multiple Discriminant Analysis (MDA) are EBIT / Current Liabilities, Sales / Total Assets and Sales / Quick Assets which have shown significant contribution towards bankruptcy prediction. Originality/Value:-This paper had revealed the accuracy and predictive ability of two statistical methods which are Discriminant Analysis (MDA) and Logistic Regression (Logit) employed in this study and has shown us the better model i.e. Logistic Regression which needs to be used in future for prediction of corporate bankruptcy in Pakistan. Limitations:- This study had used the small sample size and the focus was at Non-Financial Sector only. So it may be extended to other sectors and to other developing countries of the world. Practical Implications:- This study will be beneficial for managers and investors

A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.hybrid neural networks, corporate failures

Statistical techniques vs. SEES algorithm : an application to a small business environment

The aim of this research is to compare the accuracy of a rule induction classifier system –Quinlan’s SEE5– with linear discriminant analysis and logit. The classification task chosen is the differentiation of the most efficient companies from the least efficient ones on the basis of a set of financial variables. The sample consists of a database containing the annual accounts of the companies located in the Principality of Asturias (Spain), which are mainly small businesses. The main results indicate that SEE5 outperforms logit, but it is not clearly better than discriminant analysis. However, SEE5 models suffer from bigger increases in error rates when tested with validation samples. Another interesting finding is that in SEE5 systems both the number of variables selected and the number of rules inferred grow when sample size increases.El objetivo de esta investigación es comparar la precisión de un sistema de clasificación por reglas inductivas (SEE5, de Quinlan) con discriminación de análisis y logística. La tarea de clasificación elegida es la diferenciación entre las compañías más y menos eficientes en base a una serie de variables financieras. La muestra consiste en una base de datos que contiene las cuentas anuales de las compañías localizadas en el Principado de Asturias (España), que mayormente se trata de negocios pequeños. Los principales resultados indican que SEE5 supera la logística, pero no es claramente mejor que un análisis discriminatorio. Sin embargo, los modelos SEE5 padecen un aumento en los ratios de error cuando se prueban con muestras de validación. Otro hallazgo interesante es que en los sistemas SEE5 tanto el número de variables seleccionadas como el número de reglas inferidas aumentan cuando el tamaño de la muestra es mayor