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Financial Forecasting

In basic terms financial forecasting is something corporations do to determine how to allocate resources for the future – typically a year in advance. From the investor’s point of view, forecasting can be a useful tool to determine if certain events such as sales expectations will increase or decrease the value of the shares they hold in each company.

Analysts use forecasting to determine how extrinsic trends such as GDP or unemployment may change in the coming quarter or year. Others, for example, statisticians, use forecasting in a variety of ways to help them determine everything from the impact of customer satisfaction on the hours a business operates or how employee productivity affects the bottom line.

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Qualitative Versus Quantitative

Qualitative forecasting relies on expert opinions and are most helpful in the short term. Examples include market research, polls and Delphi surveys. Quantitative forecasting specifically excludes expert options and instead utilizes statistical data. Quantitative forecasting models include time series methods, discounting, analysis of leading or lagging indicators and econometric modeling.

The Danger Of Forecasting

Forecasting, which is at best a “guess” can be limiting. Companies (and shareholders) that rely on forecasting for most or all their information are prone to view the forecast as the only possible outcome for the company. Even if forecasts are well constructed they can break down due to unforeseen circumstances (black swan events).

This is not to say forecasting should be ignored. It shouldn’t and based on history it won’t be. Forecasting can help companies plan and stay solvent, even in tough times. The main concern is to not use forecasting as the only way to see a company or a stock but rather as one way of viewing a rather complex operation.

How Forecasting Works

From the viewpoint of a company forecasting begins with a problem or data point. For example: What will our sales be next March? Next the forecast identifies the relevant variables and how data will be collected. A time frame or assumption time is chosen to cut down on the amount of time needed and the volume of data required.

A model is selected (qualitative or quantitative) that best fits the data set, variables and assumptions. This is followed by analysis in which data are analyzed and a forecast made. The final crucial step is verification. In this step the forecaster compares the forecast to what actually happened. Based on findings the process can be tweaked and problems identified. If the forecast was spot on, congratulations are in order.


Toward A Better Forecast

The historical tweaking process when it comes to forecasting has resulted in some useful ways to help make forecasts more accurate and therefore more helpful to both companies and shareholders alike. Forecasting that follows these types of suggestions are more likely to be accurate.

Multiple scenarios – for example, one optimistic and one cautious – can help avoid a forecast that leans too heavily on one direction or the other. This can be especially important in uncertain times when the economy could easily go up or down. It’s also worthwhile to begin a forecast model by outlining fixed expenses instead of anticipated revenues. This can be followed by fluctuating expenses and finally discretionary expenses – in case there is a need to cut back.

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