V.13:10 (413-417): The End Point Moving Average by Patrick E. Lafferty

V.13:10 (413-417): The End Point Moving Average by Patrick E. Lafferty
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INDICATORS The End Point Moving Average by Patrick E. Lafferty

Moving averages have been used for decades to smooth out the noise in the prices of tradables. Here's an entry in the category, one that may provide a more accurate representation of market trends and turn out to be a more sensitive indicator of trend changes.

A century ago, Charles Henry Dow observed that trends and noise were major components of stock market price movements. Recent research into the fractal nature of markets has confirmed those observations by demonstrating that economic and capital market time series are biased random walks. Simply put, the changes of market prices with respect to time can be described as trends with noise.

Until now, two main techniques, the simple moving average (SMA) and the exponential moving average (EMA), have been used to smooth out the noise and reveal underlying trends in the prices of financial instruments. I will discuss these two methods and compare their performance with that of a third approach that I call an end point moving average (EPMA). (Editor's note: MetaStock users can use the time series forecast indicator, and SuperCharts users can use the linear regression indicator.)

Here's a quick review of simple moving averages and exponential moving averages: A simple moving average is the arithmetic mean or average of a series of prices over some fixed period of time. For example, today's value for an 11-day simple moving average is calculated by adding together today's closing price and the daily closing prices for the preceding 10 days and dividing by 11. The conventional position for plotting the SMA is at the end of the period being averaged. Thus, for an 11-day SMA, the SMA value calculated on day 11 would be plotted on day 11.

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