Criticial Finance Issues: Financial Times Briefing

CR ITIC AL F INAN CIAL ISS UES

90

Before leaving measurement, note that an aspect of it is reviewing the outcome.

The point of serious specific risk is that you cannot measure it. The type that is

really a problem – the rare, unforeseen event – has probably not happened before

and so we have no glimmering of an idea what its probability is.

Financial risk

Indicators of financial risk such as gearing or interest cover or the Z-score (see

below) can be measured but it is a mistake to imagine that, in most cases, this

results in a percentage probability of a particular risk. Rather, however sophisticated

the analysis, the best outcome of measuring is likely to be just an indication of high

or low risk. The fallacies of accurate risk assessment have been written about by

Nassim Taleb, as discussed earlier, and are worth considering in more detail.

The problem with assessing risk

Perhaps the biggest problem is that many approaches to risk, particularly those

that use financial instruments, are built on an assumption that the frequency of

occurrence of events follows a normal distribution (also described as a Gaussian

distribution). This is the familiar bell-shaped graph, of the type shown in Figure

6.1, and is used because there are well-established mathematical techniques for

calculation using this sort of distribution of probabilities.

If the outcome of an investment decision is not checked against the origi-

nal proposal then we have no way of learning and correcting errors for the

future. If it is clear that a business area tends to overstate income from invest-

ments or understate the cost of investment then it is important either to find why

this is and adjust the techniques that are used or else to put through a central

adjustment to their figures to account for the risk of error.

HO W TO MEA SURE THE CRI TICA L FI NANC IAL ISSU ES

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Figure 6.1 The normal distribution curve

The application of this assumption ranges from the calculation of variations

in stockmarket prices to the likelihood of severe weather events and its use

is perfectly understandable. When I studied engineering we often made such

assumptions, because without simplifying complex physical systems they were

impossible to analyse and calculate – so we approximated. The engineer then

tests the results of their calculations in a variety of circumstances and, having

proven that they work in stated conditions, uses the method in future problems of

that type.

The issue with applying this approximation technique in business is that there

is a wealth of evidence to show that the probability distribution of many of these

events is not Gaussian. In particular, we find that extreme events occur far more

often than the Gaussian distribution predicts. Looking at Figure 6.1, it shows that

the bell shape almost touches the ‘base’ within a short range of outcomes. Where

it seems to do so, the probability of occurrence of whatever we are measuring

is very low indeed. Unfortunately, in the worlds of economics and business that

is misleading.

Consider Figure 6.2. This has the same probability distribution as above

except that there is a greater chance of a negative outcome and then, suddenly,

there is quite a high probability of a particularly negative outcome. Suppose this

is a representation of the performance outcomes for an internet business. Most of

the probability is well represented by a normal probability distribution, except that

there is, say, a 15% chance of a catastrophic software collapse that will lead to

a disastrous outcome. In such circumstances it is not particularly helpful to think

only about the risks that are described by the normal probability distribution and

to ignore this fairly low but very present risk.

68.27%

0.1

0–1–2–3–4 1 2 3

0.2

0.3

95.45%

99.73%

0.4