Does cash forecasting really matter?

At its simplest, cash forecasting is the process of projecting the cash position of the organisation for given points in the future. Its primary purpose is to allow the organisation to predict and manage its liquidity, which in the short-term means ensuring that it has sufficient funds to meet required outflows. Over the medium- to longer term, it is about checking that there is sufficient cash and/or borrowing capacity to allow it to continue operations on an ongoing basis.

This is useful for every business, but the degree of usefulness will depend on its accuracy, the length of advance warning provided for the business to take remedial action, and the business’s specific needs and circumstances. This should frame key parameters around forecast horizon, time buckets, frequency and forecast granularity.

Is there a hierarchy of need?

In practice, businesses forecast cash for a variety of reasons which can be ranked in a ‘hierarchy of need’:

  1. Necessity
  2. Efficiency
  3. Opportunity

This ranking reflects a decreasing criticality to the business, i.e. environmental ‘pull’ to focus on cash forecasting.


Under threat of a potential cash shortfall or covenant breach, a business will look to forecast in order to quantify the scale and likelihood of the challenge. This will, in turn, inform what mitigating actions are required. If existing forecasts are inadequate, management will usually implement whatever processes they think will provide the necessary information to give them enough insight to plan their response. ‘Shock events’ are a common stimulus for increased focus on forecasting.

Another dimension to consider is nature of ownership or investment. Privately-owned businesses – from owner-managed to private equity investments – are more likely to be required by their owners to have a greater focus on cash forecasting, because it’s the underlying cash flows that pay the rewards of ownership. Likewise, heavily-financed businesses are often required by investors to demonstrate their ability to repay borrowings, through regular cash forecasts. On the other hand, within larger businesses – where management are not majority owners – there can be less focus on cash, as incentives tend to be based on earnings.

However, necessity is a one-sided equation. While businesses react promptly to cash deficits, there is often no pressure to act when there is a cash surplus. This reflects the asymmetry between potentially existential penalties for not managing cash downsides, against more marginal benefits of managing upsides.


Accurate visibility of future cash flows should enable businesses to benefit from reduced transaction costs (through aggregation or netting of flows) and improved returns (taking advantage of interest curves and off-setting positions). Additionally, the further the forecast horizon, the more time management has to identify and manage potential cash issues in a cost effective manner (for example working capital levers or negotiated refinancing), rather than reacting at short notice. By definition, it is larger centralised organisations that have the economy of scale to benefit most from potential efficiencies, given the relative investment required in people and infrastructure.

The fundamental truth, however, is that efficiency benefits are entirely dependent on the level of forecast accuracy. This, in turn, depends on both the nature of the business and on the level of effort put into forecasting. In general, more effort provides more accuracy, but some businesses are inherently volatile.

Diagram 1 illustrates a theoretical framework to determine the optimum level of effort vs. benefit, but this can be difficult to quantify in practice. For example, should the weighted average cost of capital (WACC) be used to evaluate net benefits when market interest rates are so low? The current low level of interest rates can also cushion businesses from forecasting inefficiencies.


As a result, most businesses adopt an approach of proportionate effort vs. benefit, to produce forecast data that provides management with sufficient comfort to avoid major challenges under normal conditions – it may not be optimal, but is pragmatic. For example, some companies use statistical forecast approaches, on the grounds that the potential marginal benefit of increased accuracy through a bottom-up forecast would be outweighed by the cost of the process.

We should also recognise the impact of the credit crunch. Many organisations have built up cash reserves, despite the potential opportunity cost and increased counterparty risk with the support in most cases of market analysts, investors and shareholders. Since this provides a buffer against short-term shocks, it may actually reduce the need for more accurate cash forecasting.


There is a theoretical value, at a strategic level, of having a robust cash forecast environment. It gives management the information to make better decisions and respond to new opportunities as soon as they are identified. This is similar to the concept of the ‘value of perfect information’ in capital markets theory, i.e. there is a premium attached to having this level of insight.

The push factor

The hierarchy of need reflects the degree of environmental ‘pull’ experienced by businesses in relation to forecasting, but with the exception of necessity, these seem to be weak forces in practice, despite the logical arguments in favour.

However, perhaps a more decisive influence is the simultaneous lack of a ‘push’ factor – the absence of any real pressure from the markets, for companies to demonstrate strong cash forecast frameworks. This has a significant influence on attitudes to cash forecasting in listed organisations. The analysts’ rationale is simple: over the longer-term earnings are the same as cash flows, just subject to short-term timing differences. Therefore provided the company is not in obvious distress, it is simpler to focus purely on earnings. While there is some focus on free cash flow as a corporate performance measure, it tends to be limited to whether the company met its targets, not an analysis of forecast accuracy or framework.

This is not just accounting semantics because lack of forecast discipline can result in real costs for shareholders e.g. if the company forfeits an interest margin of 0.1% on a liquidity portfolio of £1bn, due to poor forecasting, this equates to £1m lost benefit. That could pay for a better forecasting process many times over, and in most circumstances this would rightly be regarded as poor stewardship of company assets, however there appear to be few sanctions applied in practice.


There is a case for reviewing the existing processes to ensure they provide a ‘best fit’ for the organisation, i.e. that the forecast parameters (horizon, frequency, time-buckets, granularity) and accuracy are aligned to the overall objectives and tolerance levels of the business. Even though interest rates are low, there may also be a case for reviewing whether the process cost/benefit can be further optimised.

Uncertainty may be creating a new definition of necessity. We currently live in a world of increasing financial volatility – in both scale and frequency. Can you take the risk that market rates might yo-yo, or the business environment makes a quantum shift, or even that the CEO pops by and asks how much cash the company will have in six months to fund a spur of the moment acquisition – and you haven’t got the right framework in place to assess the impact and reforecast the cash position with confidence?


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