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Chapter 3 • Financial statements and their interpretation

3.7 Using accounting ratios to predict financial failure

One objective of ratio analysis is to try to make a judgement about a particular busi-

ness’s ability to survive and to prosper. Analysts have shown much interest in ratios

that may be able to indicate businesses that are in danger of getting into financial

difficulties. The reason for this is that several groups, who have relationships with the

business, stand to suffer significantly should it collapse. Lenders (including trade

payables) may find that they will not receive the money they are owed, employees will

probably lose their jobs, suppliers will probably lose a customer, and shareholders will

probably lose some or all of their investment. If these parties were able to identify ‘at

risk’ businesses, they could take steps to try to put things back on a sounder footing,

or they could take damage limitation actions such as getting their money repaid

quickly, changing their jobs, finding new customers or selling their shares.

Originally, interest focused on identifying individual ratios that might represent

good indicators of likely financial collapse. Researchers, therefore, sought to be able to

make statements such as: if the value for a particular ratio (such as the acid test ratio)

fell below a particular threshold figure, the business was then significantly at risk.

They attempted to do this by identifying particular ratios that might be good discrim-

inators between potential failures and survivors.

The researchers then found a group of businesses that had actually collapsed. They

matched this with a second group of non-failed businesses, one of which was as like one

of the collapsed group as possible in size, industry and so forth. This provided them with

two groups, as far as possible identical, except that all the members of one group had

collapsed and none of the second group had. Using past data on all the businesses,

attempts were made to examine whether the particular ratios selected were significantly

different between the two groups during the period (say, five years) leading up to the

date of the collapse of the failed businesses. Where there were significant differences for

a specific ratio, it was possible to say that a figure of above a particular level implied that

the business was safe, whereas a figure below this benchmark implied that it was at risk.

Although researchers achieved some success at identifying ratios that were

reasonably good discriminators, thoughts turned to the possibility that combining

several quite good discriminator ratios might produce a Z-score(so called) that would

be a very good discriminator. The most notable UK researcher in this field, Taffler,

derived the following model:

Z=C 0 +C 1 +C 2

+C 3 +C 4

where C 0 to C 4 are constants.

Since the Z-score model has commercial value, Taffler is reluctant to reveal the

values of these constants.

According to Taffler, a positive Z-score means that the business is sound, at least in

the medium term. A negative Z-score implies a relatively high risk of failure. As might

be expected, the higher or lower the Z-score, the more powerful is the indication of the

potential survival or failure of the business concerned.

The recommended reading at the end of this chapter provides some discussion on

the benefits and problems of using Z-scores.

Liquid current assets

Daily cash operating expenses

Current liabilities

Total assets

Current assets

Total liabilities

Profit before tax

Current liabilities

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