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Forecasting finance: The benefits of attempting the impossible. By Rob Lewis

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18th Sep 2007
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Crystal ballDesperate to divine their destinies, the ancient Romans studied the passage of birds and the entrails of beasts. The pressure to predict the future is no less intense in the age of modern capital structuring. Today’s financial seers do their soothsaying from their deskstops – whether they’re any more reliable, however, is a matter of some debate, says Rob Lewis.

When it comes to adjusting existing financial arrangements or making new investment decisions, a little prescience can be a precious thing. The problem is, it’s hard enough getting a trustworthy snapshot of things as they are, let alone some film footage from tomorrow. In truth, most models exist to massage reality rather than foretell it, and to give people numerical authority for what intuition or experience are already telling them.

One such traditional technique is the capital asset pricing model (CAPM), beloved of analysts and accountants alike. It has been routinely used by investment banks to justify their profits, as it assumes the volatility of a company’s share price increases its cost of equity. Well, in the words of George Gershwin, that ain’t necessarily so. Smooth down the points over a different period and (as in the case of investment banks) you’ll see a steady and not-so-slow increase in stock value.

If you’re still in doubt as to how susceptible financial forecasting is to spin, look at the buy recommendations historically made by city equity analysts. Before the dot.com bubble burst, buy recommendations outnumbered sells by 50-1 – this dubious market view doubtless had more to do with banking revenues than anything else. And yet it would be wrong to be entirely cynical about financial forecasting, which is after all taught at many British universities and business schools. If you want to envisage the future, instead of invent it, there are three key points to bear in mind.

Any type of forecasting in earnest should come with the following disclaimers: firstly, it’s impossible, or as the forecasters would have it; “there will always be an element of uncertainty until the forecast horizon has come to pass.” The error margin on forecasts, when computed (which may not be easy), is often bigger than the percentage change it’s pointing to. Secondly, there will always be blind spots. The moments when forecasts are most needed, because the future isn’t going to look much like the past, are usually the moments of ‘structural break’ which cause a previously reliable forecasting equation or technique to break down suddenly. As Donald Rumsfeld poetically put it, there are known unknowns and unknown unknowns, which is to say if someone perfects fusion power in their shed, for example, a lot of energy forecasting is going to be completely shot. Finally, there is a lot of distortion feedback (as Merrill Lynch et al are well aware). The very act of predicting the future changes it if people act on your predictions, however innocent your intentions. So what sort of onus should you be placing on your oracle?

Forecasting your forecasting

The foggy world of financial forecasting boasts a surfeit of tools: net present value, the internal rate of return technique, the accounting rate of return, payback period, profitability indexing, and ranged statistical probability to name but a few. Given the inherent shortcomings of them all, they are usually accompanied by a secondary tier of prediction, commonly known as sensitivity analysis, which effectively serves to tell you just how wrong you were in the first place.

Sensitivity analysis usually falls into two fields. The first of these is the practice of creating simulations of alternative scenarios. Potentially, these scenarios are of course limitless. In practice, this exercise confines itself to a series of obvious concerns, such as what would happen if capital costs ramp up or if a key customer goes under.

Consultants that specialise in this often have software that can generate hundreds of outcomes based on a series of inputted parameters. Once you have established your central model, it’s relatively simple to generate forecasts for any other conceivable eventuality in this way. As you might imagine, it can be quite a sobering process.

The next aspect of forecasting diligence is to analyse those scenarios, or at least the best and worst case variations, to establish what decisions you’d need to take. This analysis occurs outside the model and, arguably, lies nearer to planning than forecasting.

Designed to deal with sudden and unexpected shifts, this school of management thought actually has its roots in the military strategy of the early cold war. It made the transition to the corporate world in the 60s and it’s commonly held that Shell’s scenario analysis was what enabled it to come out of the 1973 oil crisis with an improved market share.

Accountancy vs economics

It’s probably no surprise that forecasting and sensitivity analysis is largely the preserve of economists, given that generally, they have a larger remit to be wrong. Nevertheless, there are areas of expertise where the two disciplines overlap, and further examination can offer rich rewards.

Post-Enron, there has been increased interest in the concept of economic value added (or net operating profit after taxes). This is an economic estimation of true profit that actually makes corrective adjustments to the standard accounting definition. Ordinarily, these would include deducting the opportunity cost of capital, amortizing goodwill and capitalising brand advertising, for example. Shareholders receive a positive value added when the return from employed capital is greater than the cost of that capital – a welcome alternative to the CAPM, perhaps.

A final subject worthy of mention here is the days working capital calculation (DWC). As money gets more expensive, knowing the timing of your cash operating cycle can be crucially important. DWC shows how many days your working capital has to wait before investment returns begins to come in (for European companies the 2006 average was 52.4). After all, it goes without saying that if you are forecasting for the purposes of financial structuring, you need to know how much finance is required. It may even be possible to release some of what you already have, and that’s almost certainly going to be cheaper than raising it outside.

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