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Study Notes

Moving Averages and Extrapolation

AS, A-Level
AQA, Edexcel, OCR, IB

Last updated 22 Mar 2021

These two methods make extensive use of sales and other data to make predictions about the future.

A moving average takes a data series and "smoothes" the fluctuations in data to show an average. The aim is to take out the extremes of data from period to period. Moving averages are often calculated on a quarterly or weekly basis.

Extrapolation involves the use of trends established by historical data to make predictions about future values. The basic assumption of extrapolation is that the pattern will continue into the future unless evidence suggests otherwise.

To understand these techniques further, look at the following chart that shows quarterly sales (£m) for a large business from Q1 Year'06 to Q4 (Year'10):

The blue line shows the actual quarterly sales figure. As you can see the sales total varies quarter by quarter, although you might guess from looking at the data that the overall trend is for a stead increase in sales.

The red line shows the quarterly moving average. This is calculated by adding the latest four quarters of sales (e.g. Q1 + Q2 + Q3 + Q4) and then dividing by four. This technique smoothes out the quarterly variations and gives a good indication of the overall trend in quarterly sales.

Looking at the chart, how might moving averages and extrapolation help management predict sales from Year'11 onwards?

The moving average helps point out the growth trend (expressed as a percentage growth rate), and it is this which extrapolation would use first to predict the path of future sales. This could be done mathematically using a spreadsheet. Alternatively, an extrapolated trend can simply be drawn on the chart as a rough estimate, as shown below:

How useful is extrapolation? The main benefits and drawbacks are summarised below:

Advantages of using extrapolation

A simple method of forecasting

Not much data required

Quick and cheap

Disadvantages of using extrapolation

Unreliable if there are significant fluctuations in historical data

Assumes past trend will continue into the future – unlikely in many competitive business environments

Ignores qualitative factors (e.g. changes in tastes & fashions)

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