Moving average preference depends on objectives, analytical style, and time horizon.

This lag factor needs to be kept in mind when choosing the right moving average for your chart. The 50-day SMA fits somewhere between the 10- and 100-day moving averages when it comes to the lag factor. Even with the January-February decline, the 100-day SMA held the course and did not turn down. The chart above shows the S&P 500 ETF with a 10-day EMA closely following prices and a 100-day SMA grinding higher. It takes a larger and longer price movement for a 100-day moving average to change course. Longer moving averages are like ocean tankers - lethargic and slow to change. In contrast, a 100-day moving average contains lots of past data that slows it down. Short moving averages are like speedboats - nimble and quick to change. In addition, the type of moving average affects the lag: EMAs, with the more recent data weighted more heavily, will lag less than an SMA, which gives equal weight to data further in the past.Ī 10-day exponential moving average will hug prices quite closely and turn shortly after prices turn. The longer the moving average, the more the lag. Since moving averages are based on data in the past, this indicator tends to lag behind price data. Learn More: DEMA | Hull Moving Average | KAMA | TEMA The Lag Factor Other specialty types of moving averages are also available in our charting tools, including DEMA, Hull Moving Average, KAMA, and TEMA. Simple moving averages (SMAs) are a true average of prices over the specified timeframe, while exponential moving averages (EMAs) give more weight to more recent prices. The two most popular types of moving averages are the Simple Moving Average (SMA) and the Exponential Moving Average (EMA), which will be discussed in this article.

They also form the building blocks for many other technical indicators and overlays, such as Bollinger Bands, MACD and the McClellan Oscillator.Ĭlick here for a live version of the chart. These moving averages can be used to identify the direction of the trend or define potential support and resistance levels. Despite this, moving averages help smooth price action and filter out the noise.

They do not predict price direction, but rather define the current direction, though they lag due to being based on past prices. By averaging prior data, moving averages smooth the price data to form a trend following indicator. We say it is “moving” because each data point is calculated using data from the previous X number of time periods. A moving average shows an average of data points (usually price data) for a certain number of time periods.

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