I am going to describe a simple strategy that I conjecture will beat most Commodity Trading Advisors out there.
I suspect that some of you are salivating at the prospects. However, before I share it with you I must share that I have not did much in the way of analysis on it yet, and do not provide investment advise. More over, in regards to this strategy, I am only concerned about actual return and not risk adjusted return.
Regarding risk adjusted return measures and risk in general within the trading community, I agree they have value provided all else is kept equal– which is rarely true!
And, that provides our opportunity, our opening to beat many of the hedge funds, CTA’s, etc with a simple strategy.
And, that strategy is simply to buy MSFT stock and to strategically add to it when it falls– you can add at EMA support levels, technical support levels, or any other strategic way, really. Yes, it is an average down strategy.
But first, let me warn you, never average down when day trading! It is unlikely to work because (1) the high leverage makes it too risky and (2) the half-life of the mean reversion is typically longer then a single day.
In fact, there are probably at least a few important rules when running any average down strategy and they probably look something like the following:
It looks like we are good on everything except #4. Any time you average down on a single stock, there is a possibility that it could go to zero and that makes it a highly risky strategy. As such, great caution is warranted against averaging down on unproven stocks/companies that could go to zero.
However, we have to be realistic. MSFT is a money making enterprise and endeavor. Any trading strategy you can build is likewise a money making enterprise and endeavor. On the other hand, MSFT has far greater resources then any trader and a proven long, long term track record.
The fact is that the probability of MSFT going to zero or long-term decline is very low. As well, most traders have low capital. They need to produce bigger gains for tangible returns.
Okay, let us get to the numbers. A 10 year buy and hold strategy in MSFT has produced a CAGR of 22% and a total return of 646%. More over, the max drawdown was only 26%. Over the most recent 3 years, we have a 37% CAGR and a 155% return with only an 18% max DD.
And, these results may be understated as I am not sure that they are dividend adjusted. More over, keep in mind that the returns would be higher, as well, if you added when the stock was down.
At this point, some of you may object that I have not provided a quantified algorithm for how to average down. However, thinking about the strategy in that way is missing the point.
As shared in the past, short term trading is really, provably all about prediction. This type of strategy is more about trying to acquire the maximum number of MSFT shares. Because drawdowns may be severe, it may help to take the mindset that MSFT/USD conversion is not important and instead to measure performance in the number of shares acquired.
Profit expectation is based on long-term trends. And, based on historical analysis, MSFT has been a proven winner. As for future developments, MSFT is actively investing in several transformation and growth areas like AI and cloud computing. As well, those growth areas will likely be protected because of national interests.
As for when to quit this strategy, there is no price based stop loss envisioned. Events that might necessitate adaption would be (1) long term trend changes or (2) eventually concentration of risk may too high for comfort.
If this strategy does not appeal to you, a more quantitative backtested and diversified strategy based around similar properties is probably possible to be created using the MES 1/10th size E-mini S&P 500.
I am already running this basic strategy-idea in one of my accounts. But, I plan to do a more in-depth analysis of this MSFT strategy in the future. Eventually, I may quantify the strategy more formally.
The author is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact the author at firstname.lastname@example.org.
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