Moving averages in technical analysis are just an expansion of what we have all studied about averages in school.
Moving averages are trend indicators and are often used because of their simplicity and efficacy. First, let us go through a summary of how averages are computed:
For instance, 5 people are sitting in a hushed and serene quick service restaurant enjoying a nice decaffeinated bottled beverage. The night is so beautiful and moonlit that each one of them ends up gulping many bottles.
The total count would be as follows:
When a 6th person enters the restaurant, she finds 30 empty bottles on the table. She quickly gets an idea of “approximately” how many bottles each person had by dividing [the total bottles] by [total number of people].
For this case, it would be as follows:
Total number of bottles/Total number of people = 30/5
= 6 bottles per head
The average here states the approximate number of bottles each person had consumed. A few of them had consumed above and below the average. For instance, person E gulped 8 bottles of beverage, while person D drank only 4 bottles of beverage. Thus, the average is simply an estimation, and one cannot anticipate it to be precise.
Expanding the concept to stocks, here are the closing prices of Company A for the last 5 trading sessions. The last 5-day average close would be computed as follows:
Thus, the average closing price of Company A over the last 5 trading sessions is 343.95.
What is a Moving Average?
A moving average is a technical indicator for market participants to discern the direction of a trend. First, it adds up the data points of financial security over a particular time period. Then, it distributes the total as per the number of data points to reach an average. It is called a “moving” average as it is repeatedly reevaluated based on the latest price data.
Market participants use moving averages to assess support and resistance by analyzing the movements of an asset’s price. A moving average indicates the earlier price action or movement of a security. The market participants then use the data to deduce the possible direction of the asset price. It is known as a lagging indicator. It follows the price action of the underlying asset to generate a signal or reveal the direction of a given trend.
Consider a situation where you want to compute the average closing price of Company B for the latest 5 days. The data is as follows:
Therefore, the average closing price of Company B over the last 5 trading sessions is 242.5.
Moving ahead, on the next day, i.e. 21st February (19th and 20th were Saturday and Sunday respectively), there is a new data point. This means the “new” latest 5 days presently would be 15th, 16th, 17th, 18th, and 21st. The data point that pertains to the 21st would be removed as the motive is to compute the latest 5-day average.
Thus, the average closing price of Company B over the last 5 trading sessions is 244.66
The latest data (21st February) has been included as calculated above. In contrast, the oldest data (14th February) has been removed to compute the 5-day average. On 22nd, the 22nd data would be included, while the 15th data would be excluded, on 23rd, the 23rd data point would be included. In contrast, 16th data would be excluded, which would continue further.
As the latest data point is being included, whereas the oldest is being removed, to calculate the latest 5-day average, it is given the name the “moving” average.
In the above case, the moving average computation is based on the closing prices. At times, moving averages are also computed using other parameters such as high, low and open. But, the closing prices are used primarily by the traders and investors as it indicates the price at which the market eventually settles down.
In practice, a moving average is commonly called a “Simple Moving Average” (SMA). However, since it is being calculated as per the latest 5 days of data, it is referred to as 5-Day SMA.
The averages for the 5 days (any number of days) are then connected to construct a smooth curving line called the moving average line, and it keeps on moving as time passes.
A 5 day SMA is overlaid over Reliance Industries Ltd’s candlestick graph in the chart below:
Types of Moving Averages
Simple moving average
As exemplified above, the simple moving average (SMA) is a simple technical indicator attained by totaling the latest data points in a particular set and distributing the total as per the number of time periods.
Traders use the SMA indicator to create signals on when to enter or exit a market. An SMA is backward-looking because it depends on the past price data for a particular period. The same can be computed for different types of prices, i.e., high, low, open, and close.
In financial markets, participants employ the SMA indicator to infer ‘buy and sell’ signals for securities. In addition, the SMA helps recognize support and resistance prices to get signals on where to enter or exit a trade.
When creating the SMA, traders must first compute this average by summing up prices over a given period and distributing the total as per the total number of periods. The data is then plotted on a graph.
Exponential moving average
Another kind of moving average is the exponential moving average (EMA), which gives more importance to the most recent price points to make it more sensitive to recent data points. An exponential moving average is more sensitive to recent price changes, as described in relation to the simple moving average, which assigns equal significance to all price changes in the given period.
Take a look at the data points used in this example.
Every data point is given equal importance. So, for example, the data point on 15th February is assumed to be as important as the data point on 21st February. But, this may not always be the fact when it comes to markets.
Always remember the fundamental assumption of technical analysis – markets discount everything. This suggests the latest price, which is on 21st February, deducts all the known and unknown data. This also suggests that the price on the 21st is more critical than the price on the 18th. Thus, the data point on 21st February earns the highest weightage, 18th February receives the next highest weightage, 17th February gets the 3rd highest, and this continues the same way further. Thereby, the latest data point reaps the maximum attention, while the oldest data point gets the least attention.
The average computed on this scaled set of numbers provides us with the Exponential Moving Average (EMA). Most technical analysis software allows the market participants to drag and drop the EMA on prices.
Here is a chart of Reliance, with a 50-day SMA (blue) and a 50-day EMA (yellow) plotted on its closing prices. Both SMA and EMA are for a 50-day period. However, the EMA is more sensitive to the prices and stays around the price.
EMA tends to react more quickly to the current market price. This is because EMA gives more significance to the most recent data points. This enables the trader to make quick trading decisions. Thus, most traders choose to use the EMA over the SMA for this motive.
Comparison: Exponential Moving Average and Simple Moving Average
The major difference between a simple moving average and exponential moving averages is their sensitivity to price changes. The exponential moving average expresses more sensitivity to recent price point changes. This makes the EMA more sensitive to the latest price changes.
The formula for computing exponential moving averages is more complex. However, a majority of charting tools make it manageable for traders to follow exponential moving averages. Contrary to that, a simple moving average assigns equal importance to all observations in the data set. Moreover, it is easy to compute as it is calculated by taking the arithmetic average of prices during the time period in concern.
Application of Moving Averages
The moving average can be used to recognize buying and selling opportunities with their own value. For example, when the stock price goes above (is more than) its average price, it implies the traders are ready to buy the stock at a price that is more than its average price. This suggests that the traders are delighted about the stock price going higher. Thus, one should seek buying opportunities.
Similarly, when the stock price goes below (is less than) its average price, it implies that the traders are ready to sell the stock at a price that is lesser than its average price. Again, this suggests that the traders are not delighted about the stock price movement. Thus, one should seek selling opportunities.
Moving average crossover system
A moving average crossover system assists the trader to take a lower number of trades in a sideways market.
The trader integrates two moving averages rather than the conventional single moving average in a moving average crossover system. This is generally referred to as “smoothing”.
A typical instance of this would be incorporating a 50-day EMA with a 100-day EMA. The shorter moving average (50 days here) is also called the faster-moving average. The longer moving average (100-day) is also called the slower moving average.
The shorter moving average considers fewer data points to compute the average. Therefore, it tends to stay near the current market price and reacts more quickly. On the other hand, a longer moving average considers more data points to compute the average. Thus, it tends to remain far from the current market price. Therefore, the reactions are slower.
Nifty’s chart exhibits how the two moving averages stack up when loaded on a chart:
As seen in the chart, the black 50 day EMA line is near the current market price (as it reacts faster) described in relation to the pink 100 days EMA (as it reacts slower).
Entry and exit rules for the moving average crossover system
Rule 1
Buy (fresh long) when the short-term moving averages become more than the long-term moving averages. Remain in the trade as long as this factor is satisfied.
Rule 2
Exit the long position (square off) when the short-term moving average becomes less than the longer-term moving average.
Applying the moving average crossover system to Company F’s chart, the Company’s chart with a single 50-day moving average is reproduced below.
When the markets were going sideways, moving averages indicated at least 3 trading signals. But, the 4th trade was the winner, which led to 67% profit.
The chart below exhibits the application of a moving average crossover system with 50 and 100 days EMA.
The black line plots the 50-day moving average, while the pink line plots the 100-day moving average. As per the cross-overrule, the signal to go long starts when the 50-day moving average (short term MA) crosses over the 100-day moving average (long term MA).
Again, the crossover point has been featured with an arrow. Also, notice how the crossover system holds the trader far from the 3 unprofitable trades. This is one of the biggest benefits of a crossover system.
A trader can use any combination to build a moving average crossover system. However, some of the prominent combinations for a swing trader would be as follows:
9-days EMA with 21-days EMA: This is used for short-term trades ( up to a few trading sessions).
25-days EMA with 50-days EMA: This is used to recognize medium-term trade (up to a few weeks)
50-days EMA with 100-days EMA: This is used to spot trades that last up to a few months.
100-days EMA with 200-days EMA: This is used to identify long-term trades (investment opportunities). Some of them can even continue for over a year or more.
Remember, the wider the time frame, the fewer the trading signals.
Here is an instance of a 25 x 50 EMA crossover. Three trading signals are eligible under the crossover rule.
It is evident that the crossover system for moving averages can also be employed for intraday trading. For example, one could use the 15 x 30 minutes crossover to recognize intraday opportunities. Also, a more aggressive trader could employ a 5 x 10-minute crossover.
Always remember, moving averages are like a trend-following system. Till the time there is a trend, the moving averages work excellently. It has no concern about which time frame you use or which crossover combination you use.
Key Takeaways
- A standard average computation is a quick approximation of a sequence of numbers.
- Moving averages involve an average calculation involving the latest data where the oldest data is eliminated.
- The simple moving average (SMA) lends equal importance to all data points in the sequence.
- An exponential moving average (EMA) measures the data according to its newness.
- The latest data earns the maximum importance, whereas the oldest data receives the least importance.
- For all realistic goals, an EMA should be used instead of an SMA. The EMA lends more importance to the most recent data points.
- The expectation is bullish when the current market price is higher than the EMA.
- The expectation becomes bearish when the current market price becomes lesser than the EMA.
- In a non-trending market, moving averages may lead to whipsaws, resulting in frequent losses in this manner. To avoid this, an EMA crossover system is employed.
- In a usual crossover system, the price chart is overlapped with two EMAs. The shorter EMA is quicker to react, whereas the longer EMA is slower to react.
- The expectation becomes bullish when the quicker EMA passes over and moves above the slower EMA. Therefore, one should seek to buy the stock.
- The trade continues up to a point where the quicker EMA begins to go below the slower EMA. The wider the time frame one selects for a crossover system, the fewer the trading signals.
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