Moving Averages

Moving Averages are popular technical analysis tools because they make it easy to spot trends by smoothing out random price fluctuations that are especially prevalent in a volatile market. Moving Averages show a security's average value over a set period of time. They form the basis for many other more complex indicators, like Moving Average Convergence/Divergence, Price Rate-of-Change, Momentum and Stochastics.

There are five main types of Moving Averages: Simple, Exponential, Triangular, Weighted and Variable. The major difference among all five types is how each assigns weight to the most recent, past and middle prices of a data set.

Simple Moving Average

A Simple Moving Average (SMA) smoothes out fluctuations in data caused by prices and volume to better illustrate the direction of a trend. It takes the average of a security's price over a specific number of preceding periods and gives equal weight to all of the prices in the data set.

For example, for a five-day moving average, the first five closing prices of a security are added together and then divided by five to get the average price over the time period. This average represents the first data point for your SMA. At the end of the next day, you calculate a new average using that day's closing price and removing the oldest day - now six trading days ago - from the data set. This way, older days get dropped as new data becomes available, and the Moving Average line moves over time to reflect the progression of the stock's prices.

Though all types of Moving Averages are lagging indicators, SMAs lag prices the most. If you're using a five-day Moving Average, you'll have to wait until the fifth day before you can calculate your first data point. Furthermore, SMAs give equal weight to all prices, whether old or new, which may make misleading statements about current price trends.

Exponential Moving Average

Exponential Moving Averages (EMAs) reduce some of the time lag associated with Simple Moving Averages by applying more weight to the most recent data points. Although the calculation is more complex, EMAs provide a more accurate representation of current price movements.

Triangular Moving Average

Triangular Moving Averages take an average of the Simple Moving Average, which smoothes out fluctuations in the prices even further. By creating a clearer line, general trends may be easier to spot.

Weighted Moving Average

Like Exponential Moving Averages, Weighted Moving Averages give more weight to the current prices. However, Weighted Moving Averages are more specific about how weight is distributed across each number in a given price set. With this type of Moving Average, each progressive day is given one unit of weight more than the day before.

For example, in a five-day Weighted Moving Average, the first day is given a weight of 1, the second day is given a weight of 2, and so on until you reach the fifth day, which is given a weight of 5.

Then, say you're looking at the five following prices: $10, $13, $14, $16 and $18. If you distribute the weight, giving progressively more to each day, you end up with a weight distribution as follows:

Price

 

Weight

 

Weighted

       

10

x

1

=

10

       

13

x

2

=

26

       

14

x

3

=

42

       

16

x

4

=

64

       

18

x

5

=

90

       
 

Total:

15

=

232

/

15

=

15.5

To arrive at the Weighted Moving Average, divide the sum of the weighted values (10 + 26 + 42 + 64 + 90) by the sum of the various weights. For this set of data points, the first Weighted Moving Average point is 15.5.

Variable Moving Average

Variable Moving Averages also apply more weight to the more recent prices, though the amount of weight given fluctuates based on the security's volatility. The more volatile a security, the more heavily the current prices are weighted.

One downside to Moving Averages is that they are lagging indicators that tend to take longer to reveal trend reversals in trending markets, and can produce false signals in trading markets, where prices move sideways. However, because Variable Moving Averages are able to adjust automatically based on current market conditions, they're generally thought to perform better in both situations.