In a previous FM article, we explored value measurement techniques, the impact of intellectual capital, and the calculation of a relevant cost of capital. The article concluded that discounted-cash-flow (DCF) valuations have the potential to provide the most justified andaccurate valuations.
This second article considers how processes and capability can be improved to provide a robust forecast that can then be used to provide the projections required for a DCF valuation.
The creation of robust financial forecasts can be boiled down to five critical steps:
1. Analyse historic performance to determine the underlying drivers of financial performance, map the interrelationships, and quantify the current values of drivers.
2. Identify projected future changes in the values of drivers and the external factors or strategic themes of the business that will cause those changes.
3. Build or modify a forecast model from the underlying drivers, including their interrelationships and projected future values, to determine forecast financial outcomes under various agreed scenarios.
4. Analyse variances between actual and forecast performance on an ongoing basis for both the underlying drivers and the financial outcomes.
5. Review the strategic themes, as necessary, and modify the forecast model.
It is imperative that finance develops the expertise to create accurate-as-possible forecasts for the short term but also into the long term. The forecasts also need to provide appropriate insight that informs actions to protect and increase business value.
There are too many "known unknowns" and "unknown unknowns" in business to expect perfectly accurate forecasts. However, forecasts should reflect the "known knowns" and be well justified, insightful, and actionable. There is scope within most businesses to significantly improve forecasting capability.
Factors to consider when preparing such a forecast include strategic alignment, managing predictability, setting the appropriate level of detail, ensuring integration of data, and forecasting through to the long term.
Businesses that can produce better, more informative forecasts can gain a significant competitive advantage, reduce business risk, and hence improve value.
Improving understanding of the value-creation levers available to the business is needed. The underlying drivers generally relate to intellectual capital and will ultimately lead to finance outcomes that determine future financial capital.
To measure the development of intellectual capital and the consequent financial outcomes, interactions between types of intellectual capital need to be mapped and appropriate metrics identified. An effective approach is that proposed by Kaplan and Norton, in which interactions are described through a business's strategic themes. These themes provide justification for projected changes in the future values of performance ratios based on investments in intellectual capital (see the example in the chart "Example Strategic Theme," below).
Example Strategic Theme
The offset in time between investment in capability and the realisation of financial returns adds complexity to the recognition of numerical relationships. The processes of identifying and tracking the strategic themes are critical in developing an improved understanding of how intellectual capital creates value. By incorporating all strategic themes within one model, the synergistic benefits that can come from improvements in multiple drivers are more easily identified and kept realistic.
The value of some metrics that measure the relationships will effectively be determined by business rules such as price lists, standard process timings, and staff costs by pay band. Others will be determined by the likely behaviours of different customers or customer groups, such as the propensity to renew or likelihood to pay within credit terms.
Disruptive factors, such as terrorism, extreme weather events, or even client empowerment, can apply unpredictable pressures on the business, which will need to be managed. The challenge for finance is to identify and master the predictable aspects of business, freeing up time and resources to focus on managing the unpredictable.
Customer behaviours drive the actions required from the business. Therefore, finance needs to develop predictive analysis skills to determine how customers have behaved and may behave in the future under various scenarios.
In my experience, drilling down to analyse business dynamics at greater levels of granularity provides insight to develop relationships that prove relatively stable over time. This results from the reduction of volatility resultant from changes in mix of business types.
By way of example, an insurance company may analyse the propensity of customers to renew their policies (see the chart "Example Insurance Renewal Propensity Rates," below). It could just look at the overall propensity to renew (A). However, it could drill down further to analyse by the customer's age at policy inception (B), renewal year (C), or both (D). The increase in variability of propensity rates as you drill down highlights the amount of variance introduced by using top-level assumptions.
Example Insurance Renewal Propensity Rates
As models mature, it may become relevant to consider adopting algorithmic approaches. However, the potential increase in complexity and cost needs to be justified by a corresponding increase in forecast accuracy and the derived benefits.
The workings and implications of any algorithm need to be fully understood to prevent "black box" situations — where "what is happening" is understood but not "why" it is happening. This is likely to impede analysis on how to change business dynamics to deliver value gains or avoid value loss.
Increasing the complexity of the prediction methodology can reduce the accuracy of forecasts, although it may improve accuracy for a few exceptional cases.
It is possible to see how significant benefits could be gained from automating predictive analysis, but this must be complemented by oversight from finance experts.
Once built, a forecast should then be flexed to run various scenarios that test the impact of assumptions. These risk-weighted scenarios should capture the impact of the disruptive factors relevant to the business to produce a forecast based on a range of possible outcomes rather than one specific outcome.
Level of detail
The level of detail used in the forecast should be sufficient to facilitate improving business predictability, whilst building confidence. However, if too much detail is used, there is a risk it becomes onerous to maintain, provides an unjustified illusion of credibility, and creates barriers hindering management engagement.
As understanding of the more predictable elements of the business improves, it is possible to increase automation and maintain greater levels of detail. By verifying the forecast against actual performance, credibility is built in the quality of the forecast.
Integration of forecasts
Short-term forecasts, which tend to be derived at a relatively detailed level and supported by stated underlying assumptions, can achieve high levels of accuracy. Variance analysis provides timely and high-quality feedback, improving understanding of performance and the accuracy of future forecasts.
Long-term forecasts tend to be produced as a totally separate exercise, at a higher level, and with limited documented justification for the assumptions made. It is rare that a business reviews historic long-term forecasts against subsequent actual performance, as there is little to be gained when the original forecast is unfit for purpose.
Creating one cohesive story to project future business performance results in integration within the forecasts across:
- Temporal periods: For example, operational plans, short-term forecasts, budgets, investment plans, long-term forecasts, risk management, and strategic planning.
- Data types: For example, nonfinancial, profitability, cash flow, and on- and off-balance-sheet measures sourced from across the business.
By developing one dynamic forecast, it is easier to ensure there is realistic progression through time and enable the business focus to be shifted to long-term value creation.
Creating a clear line of sight between current and long-term performance helps to improve the understanding and communication of strategy so that employees can effectively deliver on it. A recent AICPA & CIMA report, Reimagining Performance Management, indicated that 95% of employees do not understand their organisation's strategy.
The forecast should be prepared to cover all the periods to the point after which using an industry-based growth-in-perpetuity rate is judged to be appropriate; for most organisations the common range is five to ten years. After that period, a terminal or continuing value calculation should be completed to reflect the ongoing value-creation potential of the business (see the sidebar, "Terminal or Continuing Value," at the end of this article).
The terminal value may be based on the normalised earnings, WACC (weighted average cost of capital), and an industry growth rate to derive a terminal value of the business by utilising McKinsey and Co's "value driver formula" referred to in the first article.
Having developed the forecast, the net present values of future financial transactions and balances should be calculated by discounting at the company's WACC and hence create a DCF valuation.
This approach creates a justified valuation with target values on underlying drivers that need to be met to deliver the specified value.
Learning feedback loops
The development and ongoing utilisation of the integrated forecast model provides valuable learning in two areas. Firstly, variance analysis provides insight into performance issues and adjustments to forecasting methodology, ensuring the operational health of the business. Secondly, the resulting insights can be fed into organisational strategy.
This approach facilitates finance partnering with the business to manage both its operational and strategic health and value in an aligned manner.
This two-level review was described by Argyris and Schön as double-loop learning (see the chart "Variation on Argyris and Schön's Double-Loop Learning Model," below). Through the integration of the management of strategic health, this highlights that intellectual capital is a critical resource to be invested in, not a cost to be reduced.
Variation on Argyris and Schön's Double-Loop Learning Model
In the rapidly evolving digital world, sustainable business advantage can be achieved and maintained through the monitoring of real-time data to inform strategic change. Double-loop learning helps ensure this becomes an ongoing, continuous process.
Improved business value is achievable through the development and effective use of informative forecasting processes. Organisations that have started developing in-house expertise will already be benefiting from the insights gained, invariably gaining advantage at their competitors' cost.
Terminal or continuing value
The terminal value or continuing value is the present value of the free cash flows after the point where any explicit forecast ends and can represent a significant proportion of a company’s overall value. This is not unreasonable, as it can reflect improved cash flows from higherthan- normal return on investments in the near term. An explicit forecast should be prepared for the period until the company is expected to reach an equilibrium state, which is likely to be in line with average industry performance.
This article is the second in a two-part series looking at the role of the management accountant in understanding, measuring, and managing value.
Paul Ashworth, FCMA, CGMA, is a Jersey, British Isles-based practising management accountant providing strategic insight and enabling business intelligence systems in financial and business services, and public-sector organisations. To comment on this article or to suggest an idea for another article, contact Oliver Rowe at Oliver.Rowe@aicpa-cima.com.