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(d) Leading indicators
Balanced scorecards - the use of forward-looking ratios
The idea of taking a more balanced look at business must be a good idea. One very good idea coming out of the balanced scorecard was the identification that there is often too much reviewing of historical data and ratios and then responding - looking at lagging indicators. Of course, a key factor for a successful business is that there are accurate and timely reports and ratios - and that these are acted upon decisively.
FIGURE 10.4 Trend line from Figure 10.3
But if we have leading indicators (ie where we shall be), we can respond ahead in time - and either prevent disaster or grab opportunities.
A leading indicator is a figure or ratio that tells us where we shall be. Ideally (and I would say essentially), it has to be from a third-party 'expert' - an independent source.
Some examples are shown in Table 10.1.
TABLE 10.1 Leading and lagging indicators
However, it all depends on how one comprehends 'indicator'. For example:
- something that provides an indication, especially of trends;
- a thing that indicates the state or level of something.
These are dictionary definitions and thus a to e might meet the definition of indicators.
To be a genuine forecasting tool, leading indicators have to be more than suppositions or extrapolations. I would knock out c, d and e as being simple predictions, and b is only genuine if the conversion factor is certain. Interestingly, e, which is the weakest leading indicator example, is given as an example in a classic text on balanced scorecards.
Genuine, third-party leading indicators are a good basis for a genuine forecast. The problem is to find these indicators.
The penultimate method of forecasting briefly considered is modelling. Why briefly? Modelling could fill several Chapter 5 by itself, and anyway readers are probably much better modellers than I will ever be. The one thing that the majority of today's accountants excel at is - Excel and spreadsheets.
Modelling often uses extrapolation, as explained above, and thus models cannot tell you what the future holds. They can indicate outcomes - know what to avoid (or maybe choose the outcome you like!). Models will be more reliable where the data are regularly updated.
Sophisticated models with many inputs can be very powerful but also dangerous. They have all the uncertainties of amounts and timings, and can also include the use of probabilities. Modelling with probabilities makes sense where past patterns are likely to be repeated and thus where probabilities are, well, probable!
It is interesting that the use of probabilities is entering into financial reporting. Here are two examples:
IFRSs and the use of probabilities from para 21 of the IFRS Exposure Draft on accounting for leases:
The expected outcome is the present value of the probability-weighted average of the cash flows for a reasonable number of outcomes.
Look at the words probability/weighted/average/reasonable - how accurate will the numbers be? And from IFRS 5:
highly probable - significantly more likely than probable.
I have used these just to make the point that the use of probabilities and modelling in any form will only be helpful where bases are clearly defined.
(f) Guaranteeing the figures
Lastly, and unfortunately not so likely for most of us, forecasts can become fact if we can guarantee them.
Many large organizations and government departments have few problems with their forecasts - they demand or impose them.
But we should seek certainty for our forecasts when at all possible. Methods include:
- securing by contract;
- asking for guarantees;
- asking for commitments.
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