The incidence of business
failure in the US is increasing. Statistics show that more than 300
companies go out of business every week. The high rate of bankruptcy is
attributed to the combined effect of fiercer competition in the marketplace
and heavier debt burdens carried by companies. Matters grow even worse when
these two factors are accompanied by an economic downturn. A company's
chances of survival can be predicted with the use of financial-statement
analysis. One of the most commonly used statistical ratio models for
predicting business collapse is Altman's Z score. This model has proven to
be a reliable tool for bankruptcy forecasting in a wide variety of contexts
and markets. However, it should be noted that the Z score does not apply to
every situation. It can only be used for forecasting if the company being
analyzed can be compared to the database.
Financial-statement
analysis looks at a firm's past performance to predict its future condition.
Some users of ratio information have very specific concerns:
* Lenders are interested
in the firm's ability to meet the payments over the life of the loan.
* Auditors are interested
in judging whether financially troubled companies are likely to continue as
a going concern.
* Managements are
interested in knowing the problems they are about to face and, where
appropriate, taking corrective action.
Methods for Statistical
Approaches to Ratios
Statistical ratio models
are usually created by academics. They often are developed with the
following pattern:
* Identify a sample of
failing firms. These would meet some predetermined criterion of failure such
as bankruptcy, loan defaults, etc. A sample of around 30 is probably needed
for results to have statistical validity.
* Find a group of
comparable firms. These would be similar with respect to size, industry,
etc. The only difference is these businesses are in a healthy state.
* Analyze differences
between healthy and failing businesses. Computer analysis should reveal
which ratios are consistently and significantly different between the two
groups.
* Derive a scoring system
containing the significant ratios. This usually takes the form of a score
such that score = ratio #1 * weight attached to ratio #1 +ratio #2 * weight
attached to ratio #2 ... etc.
The formula would tell us
whether any given firm has a profile that more closely corresponds to other
successful or failing businesses.
* Evaluate new firms. This
involves scoring their financial ratio profile against our database.
Eventually you can track the performance of the model's assessment with what
actually happened, e.g., did the firm go bankrupt in the real world?
Altman's Z Score
Altman's model is probably
the classic of this genre. The original data sample consisted of 66 firms,
half of which had filed for bankruptcy under Chapter 7. All businesses in
the database were manufacturers, and small firms with assets of less than $1
million were eliminated. The original Z score was as follows:
Z = 1.2X.sub.1 +
1.4X.sub.2 + 3.3X.sub.3 + 0.6X.sub.4 + 1.0X.sub.5 Where X.sub.1 = Working
Capital/Total Assets. This measures liquid assets in relation to the firm's
size. Altman, interestingly, mentions that the most widely used current and
acid ratios were not as good predictors as this measure.
Where X.sub.2 = Retained
Earnings/Total Assets. This is a measure of cumulative profitability that
reflects the firm's age as well as earning power. Many studies have shown
failure rates to be closely related to the age of the business.
Where X.sub.3 = Earnings
Before Income Taxes/Total Assets. This is a measure of operating efficiency
separated from any leverage effects. It recognizes operating earnings as a
key to long-run viability.
Where X.sub.4 = Market
Value of Equity/Book Value of Debt. This ratio adds a market dimension.
Academic studies of stock markets suggest that security price changes may
foreshadow upcoming problems.
Where X.sub.5 =
Sales/Total Assets. This is a standard turnover measure. Unfortunately, it
varies greatly from one industry to another.
Altman found the following
significantly different ratio profiles for the two groups:
BankruptNonbankrupt
X.sub.1-6.1%41.4%
X.sub.2-62.6%35.5%
X.sub.3-31.8%15.4%
X.sub.440.1%247.7%
X.sub.51.5%1.9%
The resulting Z values are
as follows:
X.sub.1 X.sub.2 X.sub.3
X.sub.4 X.sub.5 Z
Zbr= -.07 -.87 -1.04 +0.24
+1.49 = -0.25
Znbr= +.49 +.49 +.50 +1.48
+1.89 = +4.88
To assess any firm's
likelihood of bankruptcy, we would compare their Z score with the
predetermined cutoffs shown below.
Bankruptlessthan1.81
Zoneofignorance1.81-2.99
Nonbankruptgreaterthan2.99
The Z score has proven
successful in the real world. It correctly predicted 72% of bankruptcies two
years prior to the event. Z score profiles for failing businesses often
indicate a consistent downward trend as they approach bankruptcy.
Some Cautions
Altman's Z score is the
tried and tested formula for bankruptcy prediction. It has been demonstrated
to be quite reliable in a variety of contexts and countries. It is not
designed to be used in every situation. Before using a Z score to make
predictions, one must ensure the firm being examined is comparable to the
database. The two major issues are discussed below.
Privately Held Firms. If a
firm's stock is not publicly traded, the X4 term (Market Value of
Equity/Book Value of Debt) cannot be calculated. To correct for this
problem, the Z score can be reestimated using book
values of equity. This provides the following score:
The predetermined cutoffs
for the Z.sub.1 score are as follows:
Bankruptlessthan1.23
Zoneofignorance1.23-2.90
Nonbankruptgreaterthan2.90
Nonmanufacturing Firms.
The X.sub.5 (Sales/Total Assets) ratio is believed to vary significantly by
industry. It is likely to be higher for merchandising and service firms than
for manufacturers, since the former are typically less capital intensive.
Consequently, non manufacturers would have significantly higher asset
turnover and Z scores. The model is thus likely to under predict certain
sorts of bankruptcy. To correct for this potential defect, Altman recommends
the following correction that eliminates the X.sub.5 ratio:
The predetermined cutoffs
for the Z score are as follows:
Bankruptlessthan1.1
Zoneofignorance1.1-2.6
Nonbankruptgreaterthan2.6
Small Firms.
Altman's original data sample consisted of large firms with assets in excess
of $1 million. The most recent model had businesses with assets averaging
approximately $100 million. If it is believed that smaller firms have
significantly different ratios from larger entities, then the use of Z
scores may not be appropriate.
An Example of
Misclassifications
The following shows how
using an inappropriate Z score might cause an improper classification to
occur. JIMMY, INC., is a service dealership for heavy equipment. The
conventional Z score of 1.73 indicates a firm in the zone of ignorance. This
is largely because of the asset turnover (X5) ratio. When the modified Z
score of -.96 is employed, this distortion is removed and the firm clearly
falls into the bankrupt classification.
A decade ago, the use of Z
Scores was virtually unheard of among practicing accountants. Today they are
used by auditors, management consultants, and courts of law, and as part of
many database systems used for loan evaluation. Those who advocate the use
of these approaches argue as follows:
* They are more precise
and lead to clearer conclusions than a mass of contradictory ratios. They
measure the extent of our uncertainty.
* They are uniform and
leave less room for the quirks and inaccuracies of judgment that some
individuals possess.
* Their reliability can be
evaluated statistically. They are based on past experience rather than
merely on someone's unverified opinion.
* They are faster and less
costly to work with than traditional tools.
* They can weed out the
two extremes of the spectrum in an economical fashion. This allows the
analyst to focus on the gray area where experience and judgment are needed
to compensate for what the computer misses.
Based on experience with
financial models, users must be fully aware of the pitfalls involved. Some
of these are as follows:
* Many scoring systems can
behave strangely; when ratios take on abnormal values they often produce
erroneous results. It is dangerous to assume that sophisticated tools can be
used by the untrained. They can be blinded by their apparent accuracy and
sophistication. Models move us one stage further from the raw accounting
data. Only experienced users realize how imprecise "exact" information
sometimes is.
* Models often do not give
a clear result. Whenever there is doubt, we must look to the intangibles and
address the qualitative issues.
* Most users lack an
adequate database to construct their own models. As a result, they must
purchase a custom-built one (expensive) or rely on models like those
described here that may not meet their specifications exactly.
For better or worse, the
era of computer assisted statement analysis is with us. In the future it is
likely to spread more widely. Whether Z scores and the rest can out-perform
traditional approaches is a question we can only answer in the real world.
In my opinion they are a valuable, cost-effective weapon to be added to the
arsenal. Provided they are used to complement our existing knowledge and we
are not fooled by their apparent exactness, they can only improve the
quality of our work.
(original article by:
Gregory J. Eidleman, CPA, assistant professor of accounting at Penn State
University, Hazleton Campus. published in
CPA journal-1995)
NOW you can check your company's status using the following spread sheet :
Just change /fill yellow shaded areas.
Financial Statement Analysis - Liquidity Ratios
In analyzing Financial Statements for the purpose of granting credit
Ratios can be broadly classified into three categories.
Liquidity Ratios
Efficiency Ratios
Profitability Ratios
Liquidity Ratios:
Liquidity Ratios are ratios that come off the the Balance Sheet and hence
measure the liquidity of the company as on a particular day i.e the day that
the Balance Sheet was prepared. These ratios are important in measuring the
ability of a company to meet both its short term and long term obligations.
FIRST LIQUIDITY RATIO
Current Ratio: This ratio is obtained by dividing the 'Total
Current Assets' of a company by its 'Total Current Liabilities'. The ratio
is regarded as a test of liquidity for a company. It expresses the
'working capital' relationship of current assets available to meet the
company's current obligations.
The formula:
Current Ratio = Total Current Assets/ Total Current Liabilities
SECOND LIQUIDITY RATIO
Quick Ratio: This ratio is obtained by dividing the 'Total Quick
Assets' of a company by its 'Total Current Liabilities'. Sometimes a
company could be carrying heavy inventory as part of its current assets,
which might be obsolete or slow moving. Thus eliminating inventory from
current assets and then doing the liquidity test is measured by this
ratio. The ratio is regarded as an acid test of liquidity for a company.
It expresses the true 'working capital' relationship of its cash, accounts
receivables, prepaids and notes receivables available to meet the
company's current obligations.
The formula:
Quick Ratio = Total Quick Assets/ Total Current Liabilities
Quick Assets = Total Current Assets (minus) Inventory
THIRD LIQUIDITY RATIO
Debt to Equity Ratio: This ratio is obtained by dividing the
'Total Liability or Debt ' of a company by its 'Owners Equity a.k.a Net
Worth'. The ratio measures how the company is leveraging its debt against
the capital employed by its owners. If the liabilities exceed the net
worth then in that case the creditors have more stake than the
shareowners.
The formula:
Debt to Equity Ratio = Total Liabilities / Owners Equity or Net Worth.
Efficiency Ratios:
FIRST EFFICIENCY RATIO
DSO (Days Sales Outstanding): The
Days Sales Outstanding ratio shows both the average time it takes to turn
the receivables into cash and the age, in terms of days, of a company's
accounts receivable. The ratio is regarded as a test of Efficiency for a
company. The effectiveness with which it converts its receivables into
cash. This ratio is of particular importance to credit and collection
associates.
Best Possible DSO yields insight into delinquencies
since it uses only the current portion of receivables. As a measurement,
the closer the regular DSO is to the Best Possible DSO, the closer the
receivables are to the optimal level.
Best Possible DSO requires three pieces of information for calculation:
Current Receivables
Total credit sales for the period analyzed
The Number of days in the period analyzed
Formula:
Best Possible DSO = Current Receivables/Total Credit Sales X Number of
Days
The formula:
Regular DSO = (Total Accounts Receivables/Total Credit Sales) x
Number of Days in the period that is being analyzed
SECOND EFFICIENCY RATIO
Inventory Turnover ratio: This
ratio is obtained by dividing the 'Total Sales' of a company by its 'Total
Inventory'. The ratio is regarded as a test of Efficiency and indicates
the rapidity with which the company is able to move its merchandise.
The formula:
Inventory Turnover Ratio = Net Sales / Inventory
It could also be calculated as:
Inventory Turnover Ratio = Cost of Goods Sold /
Inventory
THIRD EFFICIENCY RATIO
Accounts Payable to Sales (%): This
ratio is obtained by dividing the 'Accounts Payables' of a company by its
'Annual Net Sales'. This ratio gives you an indication as to how much of
their suppliers money does this company use in order to fund its Sales.
Higher the ratio means that the company is using its suppliers as a source
of cheap financing. The working capital of such companies could be funded
by their suppliers..
The formula:
Accounts Payables to Sales Ratio = [Accounts Payables / Net Sales ] x 100
Profitability Ratios:
FIRST PROFITABILITY RATIO
Return on Sales or Profit Margin (%):
The Profit Margin of a company determines its ability to withstand
competition and adverse conditions like rising costs, falling prices or
declining sales in the future. The ratio measures the percentage of
profits earned per dollar of sales and thus is a measure of efficiency of
the company.
The formula:
Return on Sales or Profit Margin = (Net Profit / Net Sales) x 100
SECOND PROFITABILITY RATIO
Return on Assets: The Return on
Assets of a company determines its ability to utitize the Assets
employed in the company efficiently and effectively to earn a good
return. The ratio measures the percentage of profits earned per dollar
of Asset and thus is a measure of efficiency of the company in
generating profits on its Assets.
The formula:
Return on Assets = (Net Profit / Total Assets) x 100
THIRD PROFITABILITY RATIO
Return on Equity or Net Worth:
The Return on Equity of a company measures the ability of the management
of the company to generate adequate returns for the capital invested by
the owners of a company. Generally a return of 10% would be desirable to
provide dividents to owners and have funds for future growth of the
company
The formula:
Return on Equity or Net Worth = (Net Profit / Net Worth or Owners
Equity) x 100
Net Worth or Owners Equity = Total Assets (minus) Total Liability
