Technical Analysis: Moving Averages

By IU AU

April 6, 2022

Technical Analysis: Moving Averages

It's evident the trends and prices for any freely traded entity can be very volatile, almost haphazard at times. One technique for dealing with this phenomenon is moving averages, which is a statistical technique for smoothing out these fluctuations and presenting us with the true trend. A lot of people think the moving average is a panacea for all our investment and trading troubles.

Like. For me moving averages are just one more tool in the technical arsenal to be used in the weight of the evidence approach. Like old techniques, they have their strengths, but they also have their weaknesses. Let's take a closer look, moving average attempts to tone down the fluctuations of stock prices into a smooth trend.

So that distortions reduced to a minimum. It's important to remember that moving averages just like trend lines should be considered as dynamic levels of support and resistance that dynamic because unlike specific levels, which by definition remained constant moving averages, keep changing their values.

If a specific moving average has not worked well in the past on a particular security, there are a few grounds for suspecting that it would in future and vice versa. The three types of moving average used in technical analysis are simple, weighted and exponential. There are also variable triangular and time series moving averages, the construction and use of these averages, a different, therefore each type will be dealt with in turn, but I'm going to limit the discussion to the three on this.

And start with the most widely used, and I believe the best type of average, which also goes back to my philosophy of keeping things simple. So let's begin with a simple moving average. A simple moving average is by far the most widely used it's constructed by taking a mean average of the data over a given time span.

The resulting number is known as the average or mean average now in order to get them average to move. A new item of data is added. And the first item on the list subtracted the new total is then divided by the number of observations. And the process is repeated. This chart compares the price of the S and P spider ETF, the spy with a 13 week moving.

It shows that the average changes direction well after the peak of trough and the price, and it's therefore late in changing direction, this is because the moving average is plotted on the 13th week. Whereas the average price of 13 weeks of observation, Actually occurs halfway through the time span that is in the seventh week.

That means that if it is to reflect the underlying trend correctly, the latest moving average plot should be centered at is plotted on the seventh week. And that's what we see in the bottom window. The good news is that it turns fairly closely to the turning point in price, but the bad news is that it's necessary to wait 16.

Before it's possible to ascertain whether the average has changed direction. See how the centered moving average plot in six weeks prior to the end of the chart, a time delay though, an irritant is not particularly critical when analyzing other time series such as economic data, but given the relatively rapid movement of prices in the financial model.

And consequent loss of profit potential. The delay of this nature is totally unacceptable technicians. Therefore, uh, found that for the purpose of identifying trend reversals, the best results are achieved by plotting the moving average in a final week. Changes in the trend of then identified not by a reversal in the direction of the moving average, but by the price itself, crossing it's moving average.