Chart pattern fluctuation can be the enemy of a trade analyst. If a stock is exhibiting a bunch of movement throughout the day, it could be difficult for even the savviest of analysts to get a bead on an overall trend.
Fortunately, investors can tap into the power of moving averages to help cut through the chaos. It’s a simple method to deploy, and when used properly, it can take the kinks out of even the squiggliest of lines.
What Are Moving Averages?
Moving averages also can also be classified as MA, are indicators that essentially filter out the “noise” that can be built up from random price fluctuations. They strip away the clutter by indicating the average price of a stock over a set period. By plotting the average price in this time frame, investors can quickly pick out a dependable price metric-cut cleanly from the day-to-day variances that may otherwise run interference against a stock’s trend as plotted out over the course of several days or more. In other words, the removal of day-to-day fluctuations provides investors with an easier way to spot true trends, which can ultimately work to increase the probability of gaining profits from the stock.
The Different Type of Moving Averages
While the basic principle of moving average is molded by clutter cutting as a means to spot trends and increase the chances of profitability, there are many different “blades” an investor can use to approach this end game. The three most common types of moving averages that technical analysts will encounter are simple, linear, and exponential. These differences all interpret the ultimate average in the same way, but they do offer variance in the way that the average is derived. These particular variances in calculation manifest in regards to the emphasis placed on price data.
The Simple Moving Average, or SMA, is the most common method that is utilized. The method simply takes the sum of all the previous closing prices within a determined time frame and splits the results by some estimates used in the formula. In other words, it acts as a classic average formula. For example, let’s say you’re honing in on a stock’s performance over a 15-day period. By using the SMA, you’d simply add up the closing prices of that particular time frame, divide that number by 15, and you’ll have the answer you’re looking for. In this case, the longer the time frame observed, the stronger of a performance indicator the resultant number will be.
While this method provides a simplistic way of cutting through red tape, it’s not without its critics. Detractors state the usefulness of the schematic is limited because it weighs all of the numbers culled in the formula equally, which runs counter of the stock philosophy of tracking recent performance in trends. This criticism seems particularly pointed in regards to some of the trade tactics that may be associated with technical analysis, as their plans tend to operate under the guise of “what have you done for me lately.”
The criticism leveled against SMA has led to the creation of other formulas to calculate moving averages, such as Linear Weighted Average. This particular indicator is not as common the other indicators that are commonly used, but it is one that readily addresses the issue of recent performance that some say is lacking in the SMA tactic.
A linear weighted average derives its moving average by looking at the closing prices over a set period, much like you’d see in an SMA. Before it adds up the numbers of the respective prices, this formula multiplies each price by a factor about the position the price happens to be in relating to the end of the data strain. For example, let’s say you’re looking at a price performance spread out over a ten-day period, with today’s date being the period’s last day. You’d take today’s closing price and multiply it by ten. Then, you’d multiply yesterday’s date by nine. Next, you’d multiply the day before yesterday’s date by eight, and so on. These numbers are then added up and then divided by the number of days in the timeframe (in this case, it would be divided by ten), and the “weighted” average figure would be derived.
The third moving average calculation is the Exponential Moving Average or EMA. This method is considered to be a more efficient model of the linear weighted average, just because it tends to be more responsive to new information that may crop up within a stock’s performance within a time frame – info that may not necessarily pick up on a stock’s closing price. A lot of technical analysts feel this extra wrinkle provides an enhanced level of accuracy to the moving average that is produced by the calculation, because it not only skews toward recent performance, but it skews toward the elements that are responsible for driving recent performance.
The Importance of Moving Averages
Moving averages can be a technical analyst’s best friend because it frequently drives to the heart of the matter in their pursuit of stock performance. Specifically, it can help current trends, and trend reversals bubble up to the surface unfettered, making them more efficient to spot. They can also be quite handy when it comes to helping investors spot support and resistance levels, not to mention the reverses that can form around these “lines.” Indeed, it is not uncommon to witness a rapidly declining stock halt its free-fall and reverse its course once it lands on the support of a prime moving average.
All of these elements make moving averages an important tool in the world of technical analysis because the methodology tends to be linked to trade strategies driven by speed and short-term timeliness. Because of this, it is entirely conceivable that the investor that knows how to use moving averages to quickly spot trends as they occur can view the tactic as the fine line that separates a horrible experience and a fantastic day.