1. COURSE TITLE: SEMINOR IN FINANCE COURSE CODE: MPH 622
Synopsis of articles: First: Discussion of Financial Ratios as Predictors of Failure (John Neter), Second: Discussion
of Financial Ratios as Predictors of Failure (Preston K. Mears) and Professor Beaver’s Reply to
Professor Neter
Submitted to: Prof. Dr. Radhe Shyam Pradhan, Masters of Philosophy in Management, TU
Submitted by: Sudarshan Kadariya, Roll No 04/‟010, M. Phil II Semester
First discussion: The strengths of William Beaver‟s work that identified by John Neter includes; substantial care on
methodology which attempt to avoid bias in the selection of the samples for failed and non-failed firms. The progression
on analysis consisting comparison of means, analysis of predictive power using simple discriminant analysis,
approaching to the likelihood ratios and Bayesian inference, also realization of limitations of the data and analysis as well
as states the possible existence of major pitfalls. Use of the calibrating samples which justifies the use of half of the
samples for developing the criteria and the other half is used to test the predictive ability of the criterion, it also helps to
select the indicators which are the best. Indication of the problems of possible sampling errors while using non-
calibrating sample as base of analysis i.e. in the dichotomous test results, we cannot conclude that cash-flow ratio serves
as a better indicator compare to net-income ratio. But in fact, while we look at the calibrated sample results it turns out
that they perform equally well. The quality of interpretation that is Beaver emphasis on assessing the predictive power of
ratios by comparing it with the simple naïve model so that one can see how much better one can do with ratios than
without them. Another most important strength of Professor Beaver‟s article is the implications of the accounting data
and preference of the study area has chosen.
Major weaknesses found in methodology, analysis and on implications are; the study is essentially a retrospective study,
under which the potential biases (sample selection, matching of populations, measurements) are generally high. The
factors used for matching the samples are asset size and the industry but it is not widely accepted criteria so that the study
bears the unsolved questions; is that the best way to match effectively? Should the age of the business be an important
matching criterion? Next, it is important to know how matching were done for assessing the strength of the conclusions
but it is not clear. Neter has cited the problems on the dichotomous test of the study regarding two major points – First,
the comparison of the predictive power of the ratios were made with those from the naïve model but the question is how
“naïve” the naïve model should be. Secondly, the importance of the context in which these comparisons are made. Also,
found problem on proportion use for the analysis and suggest the more realistic 1:99 i.e. 99 non-failed firms for every
failed firm to make the predictions of failure rather than 50:50 of Beaver‟s. Professor Neter made the comments on the
ground that Bayes‟ decision rule made by Professor Beaver and provided new decision rule as; if ,
predict failure. Where F = fail, NF = Non-fail, LR = Likelihood ratio, = loss if failure predicted, and no failure
occurs and = loss if non-failure predicted, and failure occurs. There is problem on determining the loss ratio as
well as the likelihood ratio which results the different ratios in varying contexts so that the interpretation of likelihood
ratio made my Beaver is not so easy to compare with 1 and note whether it differ from. Another weakness is found that
the study was based on relatively small samples which do not provide enough information about the tails of the
distributions the one of the most difficult problems in statistics.
Neter‟s discussion raised the Issues on analysis comprises; The dangers of the selectivity exist in any study of choosing
the best indicator on the basis of a given set of data, which should caution in drawing conclusions about the best ratio.
How the matching was done? Was it done according to asset size as of the first year before failure, as of some other year
or how? How to determine the loss ratio? And how effective is the use of multivariate analysis?
Professor Beaver‟s pioneering study bears the comments of Neter as; predicting failure in case of non-failure would be
much less serious than predicting non-failure in case of failure. In some contexts the financial ratios could turn out to be
very useful whereas in other contexts the usefulness (predictability) is fairly low. Thus, any general assessment of a ratio
as an excellent (or poor) predictor may be misleading. This also means that one cannot talk about the general usefulness
of accounting data on the basis of a particular predictive ability, because a ratio may be excellent for one purpose and
poor for another. Finally, the study might have been improved if it had not used a sample of non-failed firms of the same
size as that for failed firms.
Second discussion: The theme of the story presents by Professor Mears about two MBA graduates and a poorly
educated but skillful Johnny is: specifically, ground skill is more important than the theoretical knowledge. On the other
way, the cause of success in Johnny‟s version is, “I buy for one dollar and I sell for two dollars. I must make 1 per cent.”

2. The replication of the idea with Beaver‟s work is the worth of his work is to explore the predictive ability of accounting
data or financial ratios. Thus, make your 1 per cent and you are all right.
Major conclusions in favor of the earlier work are identified as; overwhelmingly support the definition of “failure” as
“the inability of a firm to pay its financial obligations as they mature” and that “operationally”, a firm is said to have
failed when any of the following events have occurred: bankruptcy, bond default, overdrawn bank account, non-payment
of preferred stock dividend. Highlight Beaver‟s work as stated primarily focus on „the underlying predictive ability of the
financial statements themselves‟. Describes the practice of financial ratios by different professionals, because their utility
lies in the fact that they can expose the areas where further information is needed which help to save some companies
from failure because their problems were detected in time through the use of the ratios. Use the trend analysis to identify
the trend effect as early as five years before the fact, the trend towards failure begins to appear and becomes increasingly
apparent each succeeding year is a most significant observation of the study. Since, Beaver raised the issue of whether
the nature of the industry has an effect on the data then answer the effect of type of industry is not great so that he may be
safe in this assumption.
The only conclusion against the Beaver‟s work is that, the criterion set for identifying the business failure also be the
liabilities of the firm as used in the earlier works. The finding can be better understood, if the criteria include this variable
as well.
In summary, Professor Mears identified the following statements: i) Prediction of failure or success is implicit in most
business decisions, and the prediction is consciously or subconsciously made hundreds of thousands a time daily. ii)
Financial data are useful and are used regularly in many of these decisions, both inside and outside of business. iii) Thus
far it has not been demonstrated that ratios of themselves will predict failure absolutely. iv) The most useful way of using
the financial ratios are to learn from them what the basic questions are those should be asked – for example, when a ratio
is “poor” – then find out why it is poor and be guided by the answer. v) Bankers, accountants, credit men, business
advisers, and others daily help businesses as a consequence of their understanding of the basic problems affecting the
business which has “poor” ratios and on the basis of their professional advice as to how to overcome the problems. vi)
Further study along the lines of Professor Beaver has undertaken would certainly seen to be justified.
Professor Beaver’s Reply: Though several questions were raised by Professor Neter in his remarks but Beaver has
responded two broad questions i) how does the context of analysis changes when the probability of failure is different
from 0.50? And ii) When the costs of misclassification are asymmetrical?
Beaver confessed that Neter has correctly point out the case of probability of failure is 0.01 instead of 0.50, when the
same cutoff points are used, the percentage error for the cash flow ratio would be 5% as compare to 1% error obtained by
always predicting non-failure (pure strategy). With this discussion two conclusions are emerge as; i) The optimal cutoff
point will shift when the probability of failure changes, ii) An analysis of total percentage error can be misleading and
limited in its insights. The analysis of the conditional probability of errors also suffers from the same limitations. Neter
has correctly points out that the probability error conditional upon the prediction made will vary if the probability of
failure is altered. The type I and II errors will also change when the cutoff point is changed.
On the other hand, the loss when a failed firm is misclassified is greater than the loss when a non-failed firm is
misclassified. The significance of the difference in errors is apparent when the costs of misclassification are considered.
Using the likelihood ratio, the decision criterion of minimization of expected loss is: Predict failure if, prior-odds ratio x
likelihood ratio > 1/loss ratio.
Does the impact of the ratio analysis (likelihood ratio) leads to a change in the behavior of the decision makers? The
solution depends upon the two factors i.e. the loss ratio and the likelihood ratio. As suggested, the loss ratio is not easily
quantified but will be considered from the point of view of a bank‟s lending decision. The calculations of the tentative
opportunity cost of bank lending help to conclude that the cost of misclassifying a failed firm are much greater than those
associated with misclassifying a non-failed firm. Although the assessments of the loss ratio and the maximum value of
the likelihood ratios are not precise, it is possible that the likelihood ratios implied by the financial ratios do exceed the
critical value, at least for a bank‟s lending decision. The analysis tentatively suggests that ratios are useful predictors, in
the sense, they change the decision behavior. Similarly another finding is the critical likelihood ratio of the
multidimensional decision is lower than that implied by a dichotomous decision model.
Finally, the question of the primary interest of the study is; how useful ratios are? Rather, are ratios useful? The answer
depends upon several sub-questions; what is the range of the value of the likelihood ratio both above and below 1.00?
Does the likelihood ratio vary in some systematic manner? What is the form of the systematic movement? Are all
financial ratios are equally useful? Does the likelihood ratio exceed 1.00 to the same extent that it falls below 1.00? Since
the suggested answers are rarely exact and also compounded by the multiple factors. Thus, the clear demarcation of the
variables and their impact analysis is desirable prior to conclude the predictive ability of the accounting data.