Ratio Analysis and Cash Flow in Bankruptcy Prediction
Date of Submission
1991
Document Type
Thesis
Degree Name
Master of Science in Accounting
Department
Accounting
Advisor
Michael Rolleri
LCSH
Bankruptcy--United States--Econometric models, Ratio analysis, Cash flow, Corporations--Finance--Econometric models
Call No. at the Univ. of New Haven Library
AS 36 .N29 Acc. 1991 no.2
Abstract
The failure of a business firm is an event which can produce substantial losses to creditors and stockholders. Therefore, a model which predicts potential business failure as early as possible would serve to reduce such losses by providing an early warning to these interested parties.
This paper has investigated bankruptcy and different models that proved to have the ability to predict a firm's bankruptcy at least three years ahead. All the models predicting bankruptcy successfully used financial ratios as predictor of failure.
Altman used discriminate analysis to group firms on the basis of a combination of five ratios. His results were 95 percent effective in selecting future bankruptcies in the year prior to bankruptcy. His model proved to be accurate in predicting business failure at least three years before the actual event.
The model based on Beaver was as good a predictor as the other models. His model using cash flow to total debt ratio was able to predict a firm's failure five years ahead.
The Evidence indicates that the non-liquid assets measures predict failure better than the liquid assets measure, even in the years immediate before failure.
Even though these models were successfully able to predict business failure early enough, their results were not 100 percent accurate. This results in two types of possible errors. Both types of error have their own cost. The cost of misclassifying a failed firm is much greater than that associated with misclassifying a non-failed firm.
Recommended Citation
Ismail, Mohamed A., "Ratio Analysis and Cash Flow in Bankruptcy Prediction" (1991). Master's Theses. 87.
https://digitalcommons.newhaven.edu/masterstheses/87