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BOOK YOUR FREE STRATEGY SESSION NOW >>Let’s get back to our discussion about the selection of the stocks to include in our portfolio from where we left off last week.
Today we’ll show you more applications of the strategy we talked about in the first part of this video, consisting in the creation of a monthly rotational portfolio made up of the “mid-performing” stocks; that is, stocks that were neither the best or the worst performer of the index.
In particular, we’ll see what results you can get by applying this strategy to Russell 1000 and Dow Jones.
Enjoy!
Hey everyone and welcome to this new video dedicated to stocks trading.
Today, we continue talking about the stocks that are neither top nor worst performers, and we'll see together two more examples of our rotational strategy applied to different indexes. We'll apply the same strategy and use the same rules that we've seen in last week's video and take a look at what happens on Russell 1000 and Dow Jones, two indexes that are very different in terms of size, since Russell contains 1000 stocks while Dow Jones only 30.
But let's start with Russell 1000 and test it with a rebalancing frequency of one month. After creating a ranking of the 1000 stocks it contains, we'll choose the 100 stocks in the middle, so from the 450th to the 550th.
Now let's have a look at the results. They are pretty interesting and make me think of what we got last week on S&P500. If you remember, in that case, too, there was a bad drawdown that occurred during the Covid19 pandemic. But apart from that, this rule seems to outperform the benchmark significantly and, all in all, the drawdown is very similar. So I think you can definitely explore this strategy further, perhaps trying a slightly more refined version and buying fewer stocks each month, since 100 is really a lot.
Now let's go and see what happens using a 6-month look-back period. As you can see, the return is slightly lower than in the previous case, and the drawdown is virtually identical to that of the benchmark. Here, too, there was a bad drawdown during the worst period of the Covid19 pandemic, so all in all, the strategy somehow confirms the results we've seen using a one-month time horizon. Perhaps the performance is slightly poorer than the previous case, but the difference is irrelevant.
In light of these results, I think that this idea deserves further analysis, but perhaps not with 100 stocks. In fact, maybe we can try to narrow down the list a little bit because trading 100 stocks every month can be pretty challenging.
Let's go and see the Dow Jones. I took the 5 central stocks because the Dow Jones is made up of only 30 stocks. So I thought that the 5 stocks in the middle of the ranking could represent this kind of strategy quite well. So let's first look at the equity curve using a time horizon of one month. The equity curve is not beautiful, but all in all, it outperforms the benchmark by two and a half percentage points, so a compound return of about 2.5% per year. However, we do see that the drawdown is a bit higher, although the peak dates back to 2008 and 2009, which was a quite dramatic period for stock indexes. However, this approach also seems to be quite interesting.
Finally, let's see what happens on Dow Jones with a 6-month look-back period. Here the strategy seems to be a little more regular, although in the first part of the history it performed slightly worse than in the previous example. However, in this case, the results are very interesting, at least in my opinion, and the drawdown is also lower than that of the benchmark. Clearly, it could be a coincidence, so in order to avoid overfitting, it is advisable to carry out more specific tests and see what happens when the parameters change, even by a little.
See you soon, bye bye!
We'll help you map out a plan to fix the problems in your trading and get you to the next level. Answer a few questions on our application and then choose a time that works for you.
BOOK YOUR FREE STRATEGY SESSION NOW >>Andrea Unger here and I help retail traders to improve their trading, scientifically. I went from being a cog in the machine in a multinational company to the only 4-Time World Trading Champion in a little more than 10 years.
I've been a professional trader since 2001 and in 2008 I became World Champion using just 4 automated trading systems.
In 2015 I founded Unger Academy, where I teach my method of developing effecting trading strategies: a scientific, replicable and universal method, based on numbers and statistics, not hunches, which led me and my students to become Champions again and again.
Now I'm here to help you learn how to develop your own strategies, autonomously. This channel will help you improve your trading, know the markets better, and apply the scientific method to financial markets.
Becoming a trader is harder than you think, but if you have passion, will, and sufficient capital, you'll learn how to code and develop effective strategies, manage risk, and diversify a portfolio of trading systems to greatly improve your chances of becoming successful.
