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

Bank Failure prediction: corporate governance and financial indicators

Most failure prediction studies have relied on using financial ratios as predictors. The most suitable financial predictors for banks are financial ratios following the CAMEL rating system. Also, corporate governance has been proven to be an important aspect of banks, especially after the financial crisis. Given its importance, we test the ability of corporate governance to enhance the prediction of bank failure. While there are only few studies that examine efficiency of corporate governance as a failure predictor, there are scarcely any studies that examine it as predictor of US banks failure. Using discriminant analysis, we predict the failure of banks insured by the Federal Deposit Insurance Corporation during the period from 2010 to 2018 using financial and non-financial predictors. We find that combining CAMEL ratios with corporate governance variables not only enhances the accuracy of prediction but also extends the time horizon of prediction to three years before failure. We also show that the earnings of banks are more significant in predicting bank failure than the capital structure and asset quality. The results further reveal that the CEO compensation, voting rights and institutional ownership are more significant predictors than the board characteristics. These results are robust when using logit regression. This paper provides insight to banks, regulators and shareholders by showing that corporate governance and banks earnings are strong predictors of bank failure

Predicting The Financial Failure Of Retail Companies In The United States

Predicting the financial failure of companies using financial ratios is a topic that has been explored in various ways for many years, and the current economic climate suggests that these models may still be more useful than ever.  Various financial ratios and bankruptcy prediction methods have been used in order to try to find the most accurate prediction model possible.  With historically successful retailers, like Sears, Kmart and JCPenney, struggling in recent years, predicting the future of retailers has become even more important.Therefore, this paper focuses specifically on the application of a failure prediction model to companies from the retail industry.  Logistic regressions are used in this study in order to attempt to predict which companies are likely to fail.  The sample for this study includes publicly traded United States companies from the retail industry, and data is collected from the COMPUSTAT database for the period from 2005-2012.  Based on prior studies, the author hypothesizes that companies are most likely to fail if they are unprofitable, highly leveraged, and having cash flow problems.  As expected, the results demonstrate that smaller retail companies with fewer employees are more likely to fail.  The results also provide strong evidence that firms with lower cash to current liability ratios, lower cash flow margins, and higher debt to equity ratios are more likely to file for bankruptcy

Predicting airline corporate bankruptcies using a modified Altman Z -score model

Since 1979, 150 airlines have filed for bankruptcy. The airline industry was officially deregulated in October 1978, which brought about many changes including the strengthening of hub and spoke operations, fare-cutting, and the entry of new competitors into the industry. However, following deregulation, the airline industry has suffered financially from various problems: the economic recession of the early 1980s; rising jet fuel costs; rising labor costs; maintenance and interest costs; rising insurance costs; and intensified competition. The transition, from a regulated to a deregulated environment, increased the instability of the carriers\u27 operating profits. In 1998, airlines earned record profits, but by 2002, only two of the major carriers turned a profit. Since 1998, six major or national North American airlines filed for bankruptcy; The objective of this study was to analyze bankrupt and non-bankrupt airlines using a traditional bankruptcy prediction model, the Altman Z-score model, in order to evaluate its ability to predict financial distress in the airline industry. The four financial ratios used in the model represented liquidity, cumulative profitability, productivity, and solvency. A second objective of this study was to develop and test a new statistical model that would better differentiate between bankrupt and non-bankrupt airlines; The new model used only three variables, predicted membership to only one of two groups, and used a simple zero as a cut-off to distinguish whether a firm belonged to the bankrupt group or the non-bankrupt group. Furthermore, the new model\u27s predictions were accurate up to four years in advance of a bankruptcy filing. The Z model, on the other hand, used four variables, did not always give a classification to one of two groups, and used two cut-offs. Furthermore, it performed no better than a naive prediction in determining whether an airline firm should be classified as bankrupt or non-bankrupt

Survival analysis of Internet companies: An application of the hazard model

The purpose of this study is to develop a model that predicts failure and estimates the time of survival of dotcoms using a number of financial and non-financial factors. This model can be used as a warning tool for stockholders, creditors, and consumers to protect themselves from such failures. I employ the Cox (1972) Proportional Hazards Model in a cross-sectional and time-varying context using financial data over the 1998–2001 period. Results from a cross-sectional analysis reveal that the coefficient estimates for variables CFTL and NSTA are consistently negative and highly significant. This suggests that higher sales and cash flows lower the potential of failure. The results also show that NITA is negative and significant at the 10% level, suggesting that higher revenues improve the survivability of a firm. Moreover, TLTA and WCTA show no significant effect on failure. On the other hand, the coefficient estimate on TA is positive and highly significant, suggesting that larger firms have higher odds of failure. This could be the result of an unsustainable growth rate among dotcoms. The excessive and rapid need for external sources of funds may raise the concerns of creditors about the financial position of the company and can lead to higher cost of funds and closer monitoring. The results from event-time data show qualitatively similar findings. However, the coefficient estimate for TA becomes negative. On the other hand, the event-time model does not show much significance in the overall effect of the regressors. The time-dependent analysis, however, shows a few differences in results, in that; sales have no significant effect on the potential of failure. In contrast, the coefficient estimate on NITA becomes negative, and highly significant. Results also reveal that stock returns add little to the predictive capability of these models. Moreover, matching companies by size to account for the size effect do not significantly alter the results. Finally, findings from industry-specific models, namely, retail, service and manufacturing, are not conclusive

Learning Machines Supporting Bankruptcy Prediction

In many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification rule called a score or an SVM. Companies with scores above zero belong to one group and the rest to another group. Estimation of the probability of default (PD) values can be calculated from the scores provided by an SVM. The transformation used in this paper is a combination of weighting ranks and of smoothing the results using the PAV algorithm. The conversion is then monotone. This discussion paper is based on the Creditreform database from 1997 to 2002. The indicator variables were converted to financial ratios; it transpired out that eight of the 25 were useful for the training of the SVM. The results showed that those ratios belong to activity, profitability, liquidity and leverage. Finally, we conclude that SVMs are capable of extracting the necessary information from financial balance sheets and then to predict the future solvency or insolvent of a company. Banks in particular will benefit from these results by allowing them to be more aware of their risk when lending money.Support Vector Machine, Bankruptcy, Default Probabilities Prediction, Profitability

U.K. Small Firm Bankruptcy Prediction: A Logit Analysis of Financial Trend-, Industry-, and Macro-Effects

Much work has been done in the last two decades to estimate the determinants of bankruptcy of large firms. Very little work appears to have been attempted in the area of small firm bankruptcy. This paper goes some way to remedy the deficiency by estimating conditional logistic probability regressions for small firm bankruptcy on a recently constructed U.K. accounts database

Designing the model for evaluating business quality in Croatia

The main objective of the paper includes designing a model for evaluating the financial quality of business operations. In that context, for the paper purposes, the financial quality of business operations is defined as an ability to achieve adequate value of individual financial ratios for financial position and performance evaluation. The objective of the model is to obtain comprehensive conclusion about the financial quality of business operation using only value of the function. Data used for designing the model is limited to financial data available from the annual balance sheet and income statement. Those limitations offer the opportunity for all sizes of companies from the non-financial business economy sector to use the designed model for evaluation purposes. Statistical methods used for designing the model are multivariate discriminant analysis and logistic regression. Discriminant analysis resulted in the function which includes five individual financial ratios with the best discriminant power. Respecting the results obtained in the classification matrix with classification accuracy of 95.92% by the original sample, or accuracy of 96.06% for the independent sample, it can be concluded that it is possible to evaluate the financial quality of business operations of companies in Croatia by using the model composed of individual financial ratios. Conducted logistic regression confirms the results obtained using discriminant analysis