**The Hull Moving Average (HMA) – Is It Better than EMA?**

In this article, we will test nine different moving averages to see which is the best moving average for trading. So, don’t get confused by various types of moving averages.

It is worth noting that two different strategies and markets are tested.

It is desirable to have at least a general understanding of moving averages. The answer to the question “What are moving averages?” isn’t as hard as it might appear at first glance.

MAs plot the average price of a security over a set number of periods or days, and moving averages are an extremely popular tool used by traders when they need to determine the overall trend. They smooth past price data so traders can more objectively see the recent trend. Moreover, moving averages filter out the noise, which makes it much easier to see what direction a market is heading.

Interestingly, the most common way to use them is to look for moving average crossovers. We have to mention that this technique has been used by many successful trend followers.

When a fast moving average, for example, a 5-day MA, cross over a slow MA (such as a 20-day MA), it indicates a new uptrend is taking place and is a bullish signal for a trend follower, telling trend followers to buy the market.

When the fast moving average crosses over under the slow moving average, it indicates that the uptrend ended, and a new downtrend is in place. Notably, this is a bearish signal for a trend follower, telling trend followers to close their long trade or go short the market.

**What is the biggest problem with moving averages?**

Undoubtedly, the biggest problem with moving averages ( as in the case of all technical indicators) is that they are lagging indicators.

Since moving averages make a calculation based on previous price data, moving averages only ever tell you what happened in the past and not the future. Hence, the longer the look-back (or a number of days/period used in the calculation), the more lagging the will be.

For instance, a 5-day moving average will be a lot more responsive to recent price moves than a 200-day. Nevertheless, because of this, a 5-day moving average will also have considerably more noise, invalidating the effect of the MA in the first place.

As a result, all moving averages are a trade-off between noise and leg. Faster MAs respond to new trends quickly; nevertheless, they show more noise and lead to more whipsaws. On the contrary, slower MA’s are better at smoothing noise, but they can be late to find new trends.

**Various types of MAs**

Due to a trade-off between noise and lag, several traders attempted to improve on the simple moving average calculation.

It is not hard to calculate the simple moving average (SMA). Hopefully, it is relatively easy to calculate, and so the indicator is carried by nearly all trading platforms. Currently, you just have to click a button, and the moving average can be plotted onto your price chart.

Nevertheless, by making the calculation more complex, many developers attempted to develop faster and smoother versions designed to better track trends.

We can start with Hull Moving Average (HMA). There is no lack of moving averages. But the most basic is the HMA, developed by Alan Hull.

Hull developed the HMA in a bid to create a moving average that was fast, responsive and with reduced lag. According to Alan Hull, the Hull Moving Average “almost eliminates lag altogether and manages to improve smoothing at the same time”.

The HMA (Hull Moving Average) is a moving average that is rarely found on popular trading platforms, but it is regarded by some to be a good indicator.

### Exponential moving average and double exponential moving average

Now, let’s discuss the exponential moving average (EMA). The EMA seeks works in the same way as the simple moving average. However, it gives greater weight to more recent price moves.

The exponential moving average is, therefore, able to react faster to new trends but could consequently lead to more whipsaws. It is also very popular as well as available on nearly all trading as well as technical analysis platforms.

Notably, the double exponential moving average (DEMA) is a faster version of the EMA. Even though the calculation is actually based on both a simple MA and a double EMA.

The double exponential moving average was first introduced by Patrick Mulloy. The most important thing is that DEMA is a moving average that reacts quickly to new price moves.

Don’t forget about the TEMA. As in the case of DEMA, Patrick Mulloy developed the triple exponential moving average (TEMA). The TEMA became established from the composite of an EMA, a DEMA and a triple EMA. As such, the TEMA significantly reduces lag and reacts quickly to new price moves.

It can be so quick that the TEMA can also overshoot the market, which means TEMA, in some instances, goes too far and moves beyond the recent price action. This is another downside to using fast moving averages.

**Wilders moving average and weighted moving average**

Welles Wilder developed the Wilders moving average (WILDERS) in the 1970s. In order to calculate the Wilders moving average, we need to change the original exponential moving average (EMA) formula.

Instead of using the original formula J. Welles Wilder used a slightly different calculation. The upshot of this is that the WILDERS is slightly slower than the exponential moving average but faster than the SMA. Interestingly, with this formula, a 27-day WMA is equivalent to a 14-day EMA.

The purpose of the weighted moving average (WMA) is to find trends faster but without whipsaws. You should keep in mind that the WMA is calculated by multiplying each data point by a different ratio and then taking the sum of all those products. This makes WMA faster than the typical EMA.

**Least squares moving average and Guppy multiple moving average**

The least squares moving average is based on linear regression. Basically, the linear regression line projects forward. It indicated what would happen if the regression continued forward.

We should mention that the GUPPY multiple moving average (GMMA) differs from the other moving averages discussed here because GMMA is a combination of several experimental moving averages at once.

For the test, we will be using the following exponential moving average parameters: 3,5,7,10,12, 15, and 30,35,40,45,50,60. As shown in the chart below:

**Which is the best moving average? Let’s find out**

As an example, we can use old data. At this point, the purpose of these tests is to get a rough idea as to which MAs work best

Two different tests will be run, a long-only, moving average crossover comparison on the S&P 500 index and a portfolio test.

Let’s start with the S&P 500 crossover test. For example, we will buy the S&P 500 whenever the faster moving average crosses over the slowing moving average, indicating an upward trend. Next, we will sell our position when the fast moving average crosses back under.

It is worth noting that the starting capital starts at $10,000, and commissions will cost $0.01 per share. Moreover, the position size will be 100% with no leverage. Notably, the ticker used will be $SPX from Norgate Premium Data, as well as the test will be run from January 1st 2000 to January 1st 2015.

Besides, all MAs calculations will use close price, and entries/exits will be made on the next day open.

**S&P 500 crossover results**

The best MA for a 5/20 day crossover happened to the WILDERS. It produced a compounded annualized return of 2.11% with a maximum drawdown of -33% giving a CAR/MDD ratio of 0.06. However, the worst performing average was, in fact, the HMA.

Notably, looking at the 50/200 day crossover, the best MA was the EMA which gave an annualized return of 5.96% with a maximum drawdown of -17%. The worst performing MA was tied between the HMA and the least squares moving average.

### Test number two

Importantly, the second test will be the same as the first one, except we will be running a 10-position long only portfolio system.

Wherever the fast moving average crosses the slow moving average on a stock in the universe, we will buy it as well as add it to the portfolio. However, whenever it crosses back under, we will sell the stock, and it will drop off the portfolio. Significantly, entries/exits will be made on the next day open, and duplicate signals will be ranked by the RSI indicator (strongest stocks preferred first). Moreover, the stock must be priced over $2.

Besides, commissions will be set at $0.01 per share. Our starting equity is split equally between each position (equal weighted portfolio).

**S&P 100 portfolio test results:**

The best MA for a 5/20 day crossover was the exponential moving average (EMA) which gave a compounded annualized return of 3.6% and a maximum drawdown of -34%. This resulted in a CAR/MDD of 0.11.

The worst performing MA was the least squares moving average.

In the case of the 50/200 crossover, the best MA was the DEMA, and the worst performer was the GMMA strategy.

When looking at the range of results, it is obvious that we can come to two conclusions. First of all, longer term moving average crossovers work better than short-term crossovers.

And second, newer as well as more complex moving averages appear to be no better at finding trends than the more transitional moving averages.

To sum up, simple moving averages work just as well as complex ones at finding trends, and the exponential moving average (EMA) is best.