About Moving Averages

A moving average is applied to a share price to remove the more rapid changes in the price, revealing its underlying trend. In other words, it reveals whether the price is rising or falling over the long term.

The moving average for a particular day is calculated by adding together the prices for n previous days, and then dividing by n, where n is the period of the moving average. The larger n is, the smoother the result, but the greater the delay (the moving average lags the share price by n days).

Apart from showing the general trend of a price, moving averages are useful indicators at their crossing points. When a shorter term average (for example, 28 days) crosses a longer term average (for example, 90 days), this can be seen as an indication to buy or sell depending on whether the shorter term average is rising or falling, respectively. You can use the same technique using just one moving average, and treating the original share price as the shorter-term average.

There are two main types of moving average: simple and exponential. You can switch between them in AutoShare simply by clicking the appropriate option on the Averages tab. Simple moving averages apply equal weighting to all prices in the specified period, whereas exponential moving averages apply progressively greater weight to more recent prices. Exponential averages have less lag and therefore react more quickly to recent price changes, but their very responsiveness may have the downside of many ‘false signals’, where recently crossed averages cross back before a profit can be made; this is known as a whipsaw. Which option you use is really a matter of personal preference, but you may consider exponential moving averages to be more useful with shorter time periods and frequent trading, and simple moving averages more suited to longer time periods.

In a recent book, The Encyclopedia of Technical Market Indicators, the authors performed a test on more than one hundred indicators for analysing the stock market, including Moving Averages, Bollinger Bands, MACD, and RSI. They found that using the crossover technique with two particular periods of exponential moving averages would have outperformed all other indicators when applied to the daily closing prices of the DJIA (the Dow Jones Industrial Average index, which comprises 30 of the largest U.S. companies) over the period 1900 to 2001. According to their back-test, it would have produced profits of 78 million percent better than buy-and-hold, turning $100 into $16 billion (assuming zero commission and tax, and full reinvestment of profits).