Financial forecasting takes the data gained by financial analysis and uses it to make predictions about future cost, profit and growth direction in the corporate environment.
In a previous article, it was shown that budgeting is a major task of management accounting. This kind of foreseeing the future not only includes classical yearly budgeting but can also mean monthly or quarterly forecasts.
Corporate financial forecasting looks at specific business activities (if not the whole business) such as sales, marketing, technology investments and intellectual property assets to make predictions about future outcomes from those activities over given time frames. The overlap between financial forecasting models, that includes statistics, numerical processing methods, physics, the social sciences and computing has made it one of the most commonly studied fields in business graduate programs and major investment firms.
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Prediction making always comes with uncertainties. The development of underlying factors can be unpredictable or hard to estimate. This makes it difficult to provide an accurate forecast. However, several disciplines have converged to provide robust techniques that take business and economic data and return very useful financial forecasting models. In this article, we discuss 5 top financial forecasting models used by businesses.
5 financial forecasting models
When evaluating a new business opportunity for which no private sales data yet exists, top-down modeling enables business financial analysts to make predictions about the specific opportunity based on the size of the new market and forecasts about how much of that new market they will be able to cover. Top-down models are useful when exploring the market share that new product lines will be able to grab as well as predicting the impact that introducing new products into established markets will have. Business finance analysts use the top-down approach to test the viability and strength of new growth potential opportunities.
Furthermore, such models can be used to provide a high-level forecast. For example, a company wants to project its financial outcomes on a monthly base. It is then likely that such a forecast is done top-down, adjusting simply some underlying triggers like growth in %. This process saves time compared to bottom-up planning.
Bottom-up modeling is used in situations where data is plentiful. The primary inputs into this model are the known values associated with each sale and variable volume scenarios. Due to its use of actual data, this approach can produce much more accurate results than top-down financial forecasting models. However, it is also more time consuming and involves more people. This approach is normally used to prepare a sound financial budget based on actual results, inside knowledge and future expectations. Due to its resource insensitivity, the use of it in forecasting is normally limited to one to three planning cycles in a year.
This financial forecasting technique looks at the relationships between two different variables in order to understand the relationship of how fluctuations in one cause changes in the other. Correlation modeling is probably the most widely used predictive model in finance modeling. Its strength lies primarily in its ability to predict the movement in both the same and opposite directions of the business activities it is used to investigate. This approach is used by corporate financial decision-makers to understand their operation’s supply and demand, and price and cost metric curves. It also suggests resource allocation in scenarios where there is a strong pairing of business events but no clear cut cause-effect relationship, such as in changes in sales following new marketing campaigns.
Also referred to as statistical models, quantitative approaches are used to establish relationships between the equations of other disciplines as a means of corporate financial forecasting. Popular methods involve Gaussian distribution analysis that takes the results from a set of financial inputs and attempts to fit it to the standard distribution curve to understand sales and profitability. Even when the Gaussian curve does not function as a strong forecasting tool, the model itself is still useful for examining other factors such as the standard deviation and variance of the financial data under consideration. These figures can help businesses understand how their efforts compare in return to their competitors and the averages in their industries. Two familiar examples of how normal curve distribution is used in the corporate environment predictions are in setting inventory ordering thresholds and sales forecasting.
The methods so far have become steadily more technical in their understanding. Power laws are by far the most challenging, though most promising numerical analysis method used in financial forecasting models. Power laws use mathematical functions that describe the proportional movement between two assets. A familiar example from geometry is that by doubling the length of the sides of a square, its area increases four-fold. Power laws are used in corporate financial forecasting models to describe returns from internal business activities. In the stock market, their value lies in their ability to show the breakdown of specific momentum trends after a trading time lapse of only a few minutes. However, corporate use of this technique is to demonstrate longer term earnings trends and baseline return to profitability levels following the impact of significant external events. The technique helps internal financial analysts understand earnings as they relate to the inputs to which they are paired when building this type of forecast model. The understanding obtained from this approach is used to direct resource allocation, capital purchases, marketing and other types of internal investment business decisions.
Financial forecasting is a discipline comprised of several types of approaches, each of which is valuable depending on the type of financial forecasting being performed and the desired goal of the business financial analyst. This article explored 5 types of powerful financial forecasting models used every day by corporate finance professionals. While no single approach can be used across every type of financial data, the application of several can greatly assist business managers to better understand their sales cycles and to predict the impact that making changes in their resource allocations will have on their earnings.