A 22 year research study provides the answer..
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In this video we look at a research study which attempts to answer the question; Does trend following work?
The study was completed by a company called RDB Computing, they offer a high-level back testing service used by hedge funds, charging over six figures in return. Needless to say, the study should be rather extensive, let’s take a look.
The definition of the term Trend Following is in essence a trend following trading style, attempting to capture gains through the analysis of an asset’s momentum in a particular direction, up or down. The study in this video focused on a long only approach, covering over 24,000 stocks over a 22-year period from 1983. The stocks chosen came from the major US markets.
In our previous study, we looked at percentage stop losses to determine which percentage offered the best results when compared to a buy and hold approach. Many viewers commented below that video requesting a similar study, but based on an Average True Range stop loss approach, and that is exactly how this trend following study was performed.
The exit rule in the study used the average true range trailing stop, commonly known as the ATR, which is a technical indicator found on most charting or brokerage platforms. It’s a measure of volatility for an individual security, or for this study, a stock’s price movement. Let’s look at an example of its application from within the study.
The red line on this Nortel Networks chart represents the 10-day ATR, and this trailed price after each trading day. We can see that as price increased, so did the ATR, responding to the average price movements during its journey. The price eventually started to trend down, and the trade was therefore closed at the open, the day after the ATR was breached. The same exit process was followed throughout the study on every stock.
The entry rule was simple, buy a stock as soon as it makes an all-time high.
Another example taken from the study here, provides numerous entry points, including the initial entry at an all time high, followed by other entry points thereafter, each making all-time highs.
Some measures were considered regarding the integrity of the data within the study, for example, the database of more than 24,000 stocks considered the survivorship bias, meaning that all stocks that were delisted from the exchanges at any point during the 22 years, were included. In fact, more than half of the database comprised of delisted stocks. Dividends and splits were included, as was a minimum liquidity filter, penny stocks were excluded, a minimum $15 price was used, as was a minimum daily traded volume of $500,000. A trading cost for each completed trade amounted to 0.5% to account for commissions and slippage.
This chart represents the number of stocks which would have passed the filters for any given year during the study.
The results we are about to see are summarised in the study abstract, by stating:
“The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy, an essential building block of an effective investing or trading system”.
Let’s break down the results, and of course if you do like this type of content, please let me know below by hitting the like button.
First, we have the expectancy results.
There were over 18,000 trades placed over the 22-year period, and the expectancy distribution we see here shows the number of trades for each net return. For example, of the 18,000 trades, 175 of them returned a net 100% profit, or 4 of the trades, returned a negative 90% net return.
The clear observation to be made is the proportionate skew to the right of the chart. For example, this area shows all the trades which returned 50% or more, equating to 17% of all the trades, and to the left, this area shows all the trades which had a loss of 50% or more, equating to less than 3% of all trades.
At first glance a near 50% win rate may not seem that impressive, but it is in fact rather good for a trend following system. Remember, although the strategy only had a winning trade half of the time, when it had a winning trade, it provided an average 51% profit, and when a trade lost the other 50% of the time, it only lost 20%. This provided a win to loss ratio of 2.56 to 1, which is a solid foundation to work from and offers a positive mathematical expectancy, which is a bare minimum for any system. In my last video we looked at the importance of positive expectancy, using the example of a casino, which you might find helpful.