Moving averages are one of the most popular and easy to use tools available to the technical analyst. They smooth a data series and make it easier to spot trends in the short run, something that is especially helpful in volatile and noisy markets, whilst also forming the building blocks for many other technical indicators and overlays. One thing that ought to be understood about Moving Averages is that they are reactionary indicators and don’t predict the future prices of the market
The two most popular types of moving averages used are the Simple Moving Average (SMA) and the Exponential Moving Average (EMA).
Simple Moving Average (SMA)
A simple moving average is formed by computing the mean price of a security over a specified number of periods. While it is possible to create moving averages from prices such as the Open, High, and Low data points; most moving averages are created using the Closing price. If we were to have an α-day SMA for closing prices, this would be calculated by adding the closing prices for the last α-days and dividing the total by α.
This calculation is then repeated for each price bar on the chart. The averages are then joined to form a smooth curving line: this is the SMA. To take this a step further, if the next closing price in the average is higher than the last, then this new period would be added and the oldest day in the α-days would be dropped. As new days are added, the old days will be subtracted proportionally; the moving average will thus continue to move over time.
Exponential Moving Average (EMA)
In order to reduce the lag issue caused by less movement in the indicator in the SMA, technical analysts often use exponential moving averages (also known as the exponentially weighted moving average). The EMA overcome the issue of lag by applying more weight to recent prices relative to older prices. The weighting applied to the most recent price depends on the specified period of the moving average.
The shorter the EMA’s period, the more weight that will be applied to the most recent price. For example, a 10-period exponential moving average weighs the most recent price at 18.18% while a 20-period EMA weighs the most recent price at 9.52%. The important thing to remember is that the exponential moving average puts more weight on recent prices. As such, it will react quicker to recent short term volatility and price changes than an SMA. Here are some useful formulas that will help traders understand the EMA and how it is calculated
Current EMA = ((Current Price – Previous EMA) x Multiplier) + Previous EMA
In addition to this, the EMAs can be specified in two ways – as a percent-base or as a period-base. A percent-based EMA has a percentage as its single parameter whilst a period-based EMA has a parameter that represents the duration of the EMA.
For a percentage-based EMA, the “Multiplier” is equal to the EMA’s specified percentage. However, for a period-based EMA, the “Multiplier” is equal to 2 / (1 + N) where N is the specified number of periods. The result being a percentage will weigh recent days higher
Which is better?
Which moving average you use will simply depend on your trading and investing style as well as personal preferences. The SMAs’ key disadvantage is its lag, whilst on the other hand, the EMA may be prone to quicker breaks; some traders prefer to use EMAs for shorter time periods to capture changes in sharp prices much faster. However, some investors may simply prefer SMAs over long time periods to identify long-term trend changes.
To summarize this, much will depend on the individual security in question by looking at important factors like how it has reacted in the past; this will affect the Moving Average Type and length of time to be used. A 50-day SMA might work great for identifying support levels in the NASDAQ, but a 100-day EMA may work better for the Dow Transports. Generally, the market is Bullish when the Short Term moving average crosses over the Long Term and vice versa to a Bearish market.
“less sensitivity leads to fewer and more reliable signals”
The initial thought for some is that greater sensitivity and quicker signals are bound to be beneficial; especially for traders. But, this is not always true and brings up a great dilemma for the technical analyst: the tradeoff between sensitivity and reliability. The more sensitive an indicator is, the more signals that will be given. These signals may prove timely, but with increased sensitivity comes an increase in false signals to buy or sell. The less sensitive an indicator is the fewer signals that will be given. However, less sensitivity leads to fewer and more reliable signals as well as late ones making them of little use to traders now.
For moving averages, the same dilemma applies. Shorter moving averages will be more sensitive and generate more signals. The EMA, which is generally more sensitive than the SMA, will also be likely to generate more signals. However, there will also be an increase in the number of false signals and whipsaws. Longer moving averages will move slower and generate fewer signals. These signals will likely prove more reliable, but they also may come late. Each investor or trader should experiment with different moving average lengths and types to examine the trade-off between sensitivity and signal reliability.
Here at Tradeview, we understand that applying your acquired knowledge of technical analysis to the live markets is no easy matter. In order to get to grips and succeed, please find more information here. Furthermore, we encourage you the reader, to open a demo account and to familiarize yourself with the markets and all the tools offered at Tradeview before stepping out into the global market.
Account Manager India