Moving Averages Technical Analysis

In many stocks technical analysis applications averages are used to smooth short term price swings, to get a better indication of the price trend. Let’s have a look at different moving averages and how some of the lag, typical to an average, can be compensated.

Averages are trend-following indicators. A moving average of daily prices is the average price of a share over a chosen period, displayed day by day. For calculating the average, you have to choose a time period. The choice of a time period is always a reflection upon, more or less lag in relation to price compared to a greater or smaller smoothing of the price data. There are a lot of different averages used. I will limit this overview to the common ones.

First let’s talk about the simple moving average that is calculated by adding all prices within the chosen time period, divided by that time period. That way, each data value has the same weight in the average result. The simple average has the best smoothing, but generally also the biggest lag after price reversals.

An exponential moving average gives exponentially more weight, based on a selected percentage, to the more recent prices in a range based on this formula:
EMA= (price * EMA %) + (previous EMA * (1 – EMA %))

Most investors do not feel comfortable with an expression related to percentage in the exponential moving average; rather, they feel better using a time period.

If you want know the percentage in which to work using a period, this formula gives you the conversion:
EMA Percentage(%) = 2 / (Time period +1)

Compared to the simple moving average, the exponential moving average will therefore follow closer the price evolution. This will result in less smoothing compared to the simple moving average.

A weighted moving average puts more weight on recent data and less weight on older data. A weighted moving average is calculated by multiplying each datum with a factor from day “1” till day “n” for the oldest to the most recent data; the result is divided by the total of all multiplying factors. In a 20-day weighted moving average, there is 20 times more weight for the price today in proportion to the price 20 days ago. Likewise, the price of yesterday gets 19 times more weight, and so on. The weighted average follows the price movement the closest and moves in general smoother than the exponential average. Determining which of these averages to use depends on your objective. If you want a trend indicator with better smoothing and only little reaction for short time movements, the simple average is best. If you want a smoothing where you can still see and react to the short period swings, then either the exponential or weighted moving average is the better choice.

The 20-, 50-, and 200-days simple moving averages were mostly used in the past before the advent of personal computers. A simple average was used because the calculation was simple; longer periods were used because the movements in those days took time to take off and to complete. This tradition is still alive today in the sense that investors still watch these averages. That is the reason why prices generally experience support and resistance at the level of these averages.

The 50-day moving average gives direction to the medium-time period. The 200-day moving average is important for a look at the long-term trend. Around the 50- and the 200-day averages, you will almost always notice some form of support or resistance. It is therefore a good idea displaying the 50- and 200-day moving averages on your price chart. The 20-day moving average is most useful as an inclination indication for short term trend lines.

If you are a trend following medium term trend trader, you probably keep an eye on one or the other average. Of course you like a smooth average to stay in the trade as long as possible. Smooth means a longer time period. The disadvantage will be too much lag at the main turning points. So you could make use of a technique to limit as much as possible the lagging nature of the average. The principles for limiting the lag of an average were introduced by Dr. Joe Sharp in Stocks & Commodities magazine, January 2000. Using a 50-days zero-lagging simple moving average for example will clearly show much less lag compared to the 50-days standard simple moving average.

Another interesting average that can be used to smooth larger chunks of data without the disadvantage of a larger lag is the TEMA average or Triple Exponential Moving Average. This average was introduced by Patrick Mulloy in Technical Analysis of Stocks & Commodities magazine, February 1994. Averages of 100 days and more will only show little lag, while the smoothing will be quite good. TEMA is not simply a triple exponential moving average, as you probably would assume from the name. The intention of TEMA is to limit the typical lag of an average.

An ‘n’ day exponential average (EMA) has a smoothing factor alpha of:

Alpha = 2 / (n + 1) and a delay of:

Delay = (n - 1) / 2. The larger the average period n, the better the smoothing, but, unfortunately, the larger the delay. TEMA uses a technique of John Wilder Tukey to compensate the delay. The data is sent several times through the same filter and combined afterward:

TEMA = (3*EMA – 3*EMA(EMA)) + EMA(EMA(EMA))

The application of the TEMA average makes most sense if you want to smooth larger data periods, whereas the delay must remain as small as possible.

Of course you can start making all kinds of combinations with the different averaging techniques, combining simple, exponential or weighted moving averages with the TEMA and zero-lagging average techniques. That way you can create your own average that fits best your way of trading.