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Guide
FP&A Leaders' Playbook:
Forecasting in 2024
Your comprehensive guide to forecasting in FP&A. Contains everything you need to know about quantitative approaches, traditional vs rolling forecasts, and more.
Table of Contents
01. Traditional vs Rolling Forecasts
The main purpose of financial forecasting is to predict future financial outcomes using a variety of inputs, including historical enterprise data, market trends, economic assumptions, and customer expectations to provide an estimate of what will happen in the future. To be effective, forecasts must support adaptive decision-making to consider changes in market conditions, global economic shifts, and unforeseen events.
Forecasting in the past generally happened at the same time as budgeting, and was set to cover the same time period. The issue with this, clearly, is that the forecast very quickly goes out of date and doesn’t allow organizations to pivot in the face of change.
As a result, standard practice is to now adopt a rolling forecast model - which entails completing a forecast for a fixed time period in the future, and then updating it on an ongoing basis (most commonly monthly or quarterly). That way, you always have a view into the future that reflects the business conditions of today.
Since forecasts are meant to be as accurate a prediction of the future, the past actuals can result in the changing of the future forecast. For instance if revenue is trending lower in the first half, then the second half may need to be adjusted downward as well if the trend is presumed to be a good indication of the future.
The forecast is a living, breathing document that is meant to be changed based on market conditions. The comparison to actuals is a good barometer. A good example of a rolling forecast would be a 3+9: three months of actual followed by 9 months of forecasted data (all within the same board).
The frequency of updates and the amount of time a forecast stretches into the future will depend on three factors unique to the organization in question: namely, how fast market conditions are likely to change for your organization, the rate of growth you’re experiencing, and the internal resources you have available.
02. How forecasts are built
Financial forecasts typically focus on predicting short-term revenues, costs, and returns on investment. The approaches to building a financial forecast involve quantitative analytical approaches such as financial modeling and statistics, as well as qualitative based upon observations and judgments that have been built up in the finance department.
Financial forecasts are often done on a standard set of components, which can include:
Expense/cost of goods sold forecasting
Margin/Profit and loss forecasting
Revenue forecasting
Cash flow forecasting
Balance sheet forecasting
Capital investment/expenditure forecasting
03. Methodologies used for forecasting
Many organizations’ initial approaches to financial forecasting include using pro forma statements modeled after income statements, balance sheets, and cash flow statements. Pro forma forecasting of these statements can provide a picture of the organization’s financial health and future performance, as impacted by changes in economic and market conditions. Most organizations run multiple scenarios based on different market and economic assumptions to provide alternative views of future performance.
Quantitative methods are used extensively in producing financial forecasts. They form the basis of scenario planning, where models are prepared that model the impact of market, economic, and internal factors. While they are used extensively in forecasting, it must be understood that additional factors that influence performance can't be quantified and many organizations adjust their forecasts to use qualitative approaches, relying on expert knowledge and experience to predict performance rather than historical numerical data.
The Harvard Business School has identified seven quantitative approaches that are used in financial forecasting that must be considered:
- Percent of Sales: Calculate future forecasts using a percentage of sales approach. This would include modeling forecasts using the cost of goods sold, which is typically based on a percentage of sales revenue.
- Straight Line: Forecast using assumptions about historical growth rates that will remain constant. A good place to start with a forecast, however, does not take into consideration market, economic and supply chain issues.
- Moving Average: Forecast using a weighted average of prior periods, such as building a forecast by averaging the previous quarter performance. This is an underlying approach in rolling forecasts.
- Simple Linear Regression: Forecast metrics based upon the relationship between dependent and independent variables.
- Multiple Linear Regression: Forecast metrics based upon two or more variables impacting a company’s performance. The ability to account for several variables that affect performance should lead to a more accurate forecast.
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