When you review all of the technical indicators available to traders in today's analytical software, moving averages are still one of the most popular and widely-used indicators to help identify market trends. Moving averages form the basis of many single-market, trend-following trading strategies, ranging from the popular 4-9-18-day moving average "crossover" approach to the widely followed 50-day and 200-day simple moving averages used to highlight the underlying trend direction of broad market indexes and individual stocks.

Lagging Behind
However, traditional moving averages have one very serious deficiency: They are a '"lagging" technical indicator. By virtue of their mathematical construction (averaging prices over a number of prior periods), they have to rely on past prices that have already occurred so tend to lag behind the current market price.

"Making trades based upon the analysis of moving averages typically results in getting into and out of the market late when you compare the points at which the market's price actually makes a top or bottom and when it changes trend direction," points out Lou Mendelsohn, developer of VantagePoint Intermarket Analysis software. "Depending on the market's price movement and the type and size of moving average used, this lag effect can be substantial, causing the difference between trading success and failure in today's highly volatile, global financial markets."

Notice, for example, how the moving average lags behind the market at major turning points on Figure 1, a chart of daily prices of the U.S. Dollar Index futures contract with its actual 10-day simple moving average.

Figure 1 - Daily prices of the 30 Year U.S. Treasury Bonds with its actual 10-day simple moving average.  Source: VantagePoint Intermarket Analysis Software (www.TraderTech.com)

This lag is the Achilles' heel of moving averages. Technical analysts have spent years on research in a futile effort to eliminate this lag while still retaining the beneficial "smoothing" effects of moving averages. As a result, numerous types of moving averages have been devised, each with its own mathematical construction and effectiveness at discerning the underlying market trend and ability to minimize the lag effect.

Complexity Doesn't Help
Moving averages are often incorporated into more complex technical indicators, such as moving average crossover strategies, to improve their effectiveness. One such approach involves two simple moving averages of different lengths, such as a 5-day and a 10-day average. When the short moving average value is greater than the long moving average value, the underlying trend is assumed to be up. When the short moving average value is less than the long moving average value, the trend is assumed to be down.

The chart of the New York light crude oil futures contract provides an example of how the shorter 5-day moving average is more responsive to current price action than the longer 10-day simple moving average (see Figure 2), but both still lag behind the market at major turning points.

Figure 2 - Chart of daily prices of the U.S. Dollar Index with its actual 5-day and 10-day simple moving averages. Source: VantagePoint Intermarket Analysis Software (www.TraderTech.com)

An inherent assumption behind moving averages is that once a trend is underway, it tends to persist. Therefore, until the long moving average is penetrated by the short moving average in the direction opposite from the prevailing trend, an uptrend is assumed to remain intact.

Traditional moving average crossover strategies are effective at identifying the current market direction in strongly trending markets. In non-trending, sideways markets, however, and even in trending markets when very short moving averages may be overly sensitive to abrupt price fluctuations, these approaches are subject to whipsaws. This results in erroneous trading signals at market tops and bottoms. So, while traders can make money in trending markets using moving averages, it is the choppy markets, increasingly more common today, that can cause substantial trading losses.