Nov. 15, 2021
There was more to extract from my previous article Use QQQ - Make the Money and Keep IT.
The presented free trading strategy in that article did two simple things, one: it completely outsourced its stock selection process, and two: it rebalanced weekly. That's it.
No trading signals, no technical indicators, no market timing routine, no move to the sidelines in times of market turmoil. Not even a request for your opinion, feelings, state of mind, or market analysis. Quite a simple and productive "whatever happens" strategy, of the suck it up type. This trading strategy is saying that you do not need anything special to win, in fact, you do not need anything at all (except access to money). It is interesting to see how this strategy could also apply to a lot of other strategies having similar trade mechanics. Much can be learned from this pure rebalancing play.
Using QQQ's constituent 100 stocks effectively solved the first problem: the continuous stock selection conundrum. The stocks you select are the foundation to building a long-term portfolio. Outsourcing the selection process transferred its management to its issuer. We had nothing more to do after this simple decision. Any change to QQQ's list of stocks would be taken care of. A real question might have been: would QQQ's constituent stocks really be good choices? Some would say: you do not know what will come next, but, in QQQ's case, we do know, for the most part, what will happen next, and we can be almost certain it will.
Maybe, more importantly, the question should be: how should this singular collection of stocks be traded to optimize my long-term performance? I want to win, I want to win big. I simply hate risks (losing). I'll take the risks if I have to, but I want to know before taking them what I'm in for, even in an automated system. What is my potential reward for taking this kind of risk?
I contend here that some very basic portfolio management decisions can have quite an impact on long-term results. To demonstrate this, we will look more closely at the strategy's portfolio weights and its use of leverage. From the above-mentioned article, we know leverage did increase performance but also increased drawdowns (risks). Increasing the capital base increased performance and did not necessarily add significantly to those drawdowns. We even looked at raising both at the same time: more capital, higher leverage, producing outstanding results, albeit, with added risk as you raised leverage, as should be expected. The added risk did provide much higher profits.
Trading your way to a secure long-term portfolio by rebalancing is different from outright investing for the long term, or day-trading, for that matter. Nonetheless, it can also be quite rewarding.
The Horse Race Analogy
It is like participating in a special 100-horse race. You know how it starts, will see horses jockeying for best position during the race, but do not know how the race is going to finish. Whereas here, we are taking 100 stocks selected out of some 8,000+ for a race that is intended to last over 20 years. Each stock having some unspecified odds of finishing the race from some starting line (p0) at some time (t0). You estimate, from such past races, that about 30%+ might not even cross the finish line. Of the rest, most (~50) will be part of the also-ran (no big money - no big returns - but nonetheless, finishing the race, most positive, some not) while the remaining participants (~20) will grab most of the prize money in their finishing order. However, they first will have to finish the race to be in that group, otherwise, the stock was part of the also-ran, or even worse, part of the dropped out.
At the starting point (t0, p0) (say some twelve years ago), you did not know which 20 stocks you should have selected and placed heavy bets on (but did have some candidates). Whatever your would-be stock selection, it would have a direct impact on the final outcome. Making the biggest bet on the best performer remained an objective, not easily reached. Furthermore, in this race, new stocks will be added along the way to replace those that drop out, and you would not know how they will finish either, if ever.
Reasons for some stocks not finishing the race could be: fraud, bankruptcy, book-cooking, self-serving interest, and/or obsolescence, and more. For the ~70 finishing the race, most (~50) getting the smaller prizes (something close to average market returns or lower, if not still underwater) and will have suffered from being either outclassed by bad business models, having no stamina, no reserves, no innovation, or simply poor and/or bad corporate management. Of the 20 or so to finish upfront (meaning above average), the ultimate winner will get the lion's share of the prize money (heaviest portfolio weight) followed by the other front runners which will take the largest part of the remaining prize money. The rest will be divided into the also-ran who finished the race (survived) according to their respective decreasing weights.
You know from experience that betting big on your favorite stock of the moment is a gamble where you could lose everything, not lose so much, get out near even, make a reasonable profit, and maybe win big. You might have odds of 3 in 10 to lose it all, 1 in 2 to come out close to average, 1 in 5 to make above average, and 0.1 to 1 in 10 to make it big. However, making it big is a big word. We cannot deduce probabilities like this since we do not really know, especially for future outcomes some 20 years hence. We should not confuse statistics with forward probabilities.
In this special QQQ race, stocks that are removed from the list for whatever reason are replaced. On horses, you might lose your entire bet on such an outcome with no recourse, while with stocks, losses could be just a fraction of the initial bet you placed. This might sound silly, but in this game, it is a major point. For example, it would be extremely unlikely, not to say difficult, to have rebalancing QQQ's 100 stocks make you lose it all. It would require all 100 stocks to go bankrupt within a week. This will not stop you from having drawdowns, and I can assure you, you will have them, whether you like it or not. It is an inherent part of the game.
Overall, even with the stock list being modified all the time, you might still have a positive long-term expected return should you spread your bets on all runners in this race (QQQ). By spreading your bets, the most favorable, probable, and expected outcome might come close to 10$\%$ compounded per year: F0 ∙ (1 + 0.10)t, which is close to the secular average market return when viewed from the vantage point of a long-term horizon (20+ years).
So, the decision is relatively simple, place the 100 initial bets on QQQ's constituents, or place a single bet on QQQ itself. It was demonstrated in the above-cited article that you could have had a slightly better CAGR by betting directly on QQQ with a lot less work.
A Twist In The Trading Rules
There is a slight twist to this game of ours. The way the game is built, at any time during this race, we can change any of our ongoing bets, practically at no cost. We do not need to provide excuses, we just do it whenever we want for whatever reason we may have. Period. In there lies our advantage, our ability to make whatever bet we like at any time of our choosing. As long as we can put the cash on the table, our bets (whatever their size) will be accepted. Placing small bets all over is no problem, however, when bets get larger we will have to consider their market impact and how these orders will be filled. But, before getting there, is not a problem. In the beginning, we will certainly not be trading hundreds of thousands of shares at a time.
Instead of waiting for the race to finish for cashing in the winnings (losses) (i.e.: Bet & Hold), we can increase or decrease the position size on any of the stocks as the race progresses to its finish line. A line that we set in the sand ourselves as our goal to reach (our own inherent stopping date to execute total liquidation at termination time T, or do some scheduled withdrawals thereafter).
Using QQQ stocks, we effectively delegated the stock selection process and its management. As if on our behalf, stock weights will be assigned proportionally to market capitalization: wj = Sjpj / Ʃ Sjpj where Sj is the number of shares outstanding for stock j and with pj its current price. The no leverage scenario has for restriction that the sum of weights equals 1.0 or less: (Ʃ wj ≤ 1.0), which is easy to maintain since that is what the expression for wj says, it can maintain up to full market exposure.
Should we want to apply leverage (Lev), the above formula would transform to: (1+Lev) ∙ wj = (1+Lev) ∙ Sjpj / Ʃ Sjpj. We would need to pay the leveraging fees, evidently, but it would raise weights and thereby the number of shares for all the trades and for all the stocks in the portfolio proportional to their market cap weights. Again, putting more emphasis on the front runners, those with the highest value. The sum of the new weights would total: (1+Lev) ∙ Ʃ wj = (1+Lev) ∙ 1.0.
QQQ is doing it all for you: sorting the top 100 stocks by market cap, management of replacements, and weighing by highest value. Stocks that cannot stay on the top 100 list are automatically replaced by newcomers. Newcomers that cannot stand the heat get replaced. Stocks that have been dropped can always come back. It is up to them to make the grade. No matter what, you only play the top 100. Period. And those 100 are the highest valued stocks of the moment.
It takes a lot to get on QQQ's list, it takes, even more, to stay on it.
(QQQ weight data from Invesco, as of Nov. 5, 2021)
How should YOU have determined all your bets? Equal weights, market cap, or some other kind of scalable merit-based weights?
An easy solution was in the same manner as in QQQ. The stocks going up the most get the largest weights and the stocks at the bottom of the list get the smallest weights, as illustrated in the above chart. Stocks would be rewarded based on their respective and relative value, based on their proof of merit to be on the list. This, with little added risk or effort. That was visible in the tables given in the previous article. The same stocks were traded at the same scheduled time, at the same prices. Only their respective bet sizes were different.
Stocks start to be considered when they enter the set of the top 100 (from the right of the above chart) and fight to stay relevant. Their respective weights will move up and down the green line, the best ratcheting their way up to the top. Those unable to maintain their status will simply drop off the chart (again from the right), and return to be irrelevant to your cause.
This also makes a case for other non-equal weights scenarios that behave similarly.
From the above chart, we see the first 22 stocks as overweight, if we consider the average weight to be: Ʃ wj / J = 0.01. For the first two stocks, the overweighing gets to 0.10679 and 0.10575 respectively, which is over 10 times overweight. While the last stock on the list had for weight: 0.00096, a little more than ten times less than equal weight. The highest weight is 172.24 times larger than the lowest in this group. This is indeed putting more emphasis on the highest valued stocks. The lowest weights on the list have very little impact on the overall portfolio return.
Any trading strategy that is able to shift weights in favor of best performers while minimizing the impact of underperformers is in the same category as the above-mentioned strategy. It pays to overweigh overperformers while you underweigh underperformers.
Market cap weights are shifting money that could have been placed on the trailing horses to the front runners and increasing their bet size, in a way, leveraging the best performers by increasing their market exposure. For some, that could be as high as 10 to 1. Throughout the race, weights will be shifted around depending on the evolution and make-up of the race itself.
But no matter how the race is run, at the end of the race, most of the money will be distributed in the finishing order from highest to lowest according to their respective value to all participants.
Shifting weights around will be regulated by the rebalancing procedure and will be done within specific and predetermined parameters such that whatever the outcome of the race, your final and biggest bets will be with the best horses (winners) in their order of finish. Also, smaller bets would have been with the underperformers: those that did not finish the race, did poorly, or failed to beat the average.
Thereby, on average, winning big on our winners and losing small on our losers. All done automatically.
We did not have to do anything to take advantage of this. It was done for us due to the stock selection process and its method of weighing participants. Our bet size changed as we went along, increasing as our portfolio rose and decreasing as relative weights fell. This, with no input or fault of our own. It is being performed with our implicit approval in trying to mimic QQQ's weighing system.
Over time, we will have duplicated QQQ's weighing methodology since our own strategy was closely tracking QQQ which was tracking the NASDAQ 100 index. We became simple closet indexers, like many. However, we also know other stuff, enhanced trading methods, that will help us take better advantage of this.
Adding leverage can also change the game. The following chart shows the cumulative data for equal weights (brown), QQQ's market cap weights (green), and QQQ's 1.3x leveraged weights (yellow).
The green line above is the actual cumulative weight for QQQ as of Nov. 5, 2021 (as in the prior chart). It shows the emphasis put on the front runners. Considering all the chaos in markets, it is a relatively smooth curve. The first 20 stocks with equal weights, evidently, totaled 0.20, as should be expected. For the market cap weighing, the first 20 stocks accounted for 0.69 while the 1.3x leveraged scenario added up to 0.89. And here is the other reason why the leveraged scenario greatly outperformed equal weights.
Pushing the leverage to 1.75x, the first 20 stocks would now total 1.21 in weights. Those 20 stocks are the highest performers of the group. It would push MSFT's weight to 0.18688 compared to the equal weight of 0.01. It would do the same thing with AAPL putting its weight at 0.18506. Sure, the weights will change all the time. Some will even be pushed off the chart. But new ones will take their place. Your job is to profit from it all. And it is not to support those stocks that do not make the grade whatever your ordered stock selection may be.
If you tried the above-mentioned trading script with equal weights it would produce less. Structurally, it is easily understandable. However, it might not have been that evident in the equal weight chart since the sum of weights, in either case, was 1.0: Ʃ wj = 1.0.
Going equal weights reduced weights placed on the best performers while at the same time increasing the weights of the underperforming stocks in the list.
From the previous article using the same market cap weights, we can see the emphasis put on the best performers.
The equal weight distribution is quite different. Bet sizes were limited to 1% of the portfolio.
Any strategy trading with 100+ stocks that uses equal weights will suffer from about the same symptoms. Common sense would dictate to prefer relative performance weighing rather than equal weights. A lot of trading methods could do this. And yet, most of the strategy designs I have seen still use equal weights. Go wonder why a lot of them do underperform? Look at how a merit-based weighing system could improve on your own strategies.
Definitely, all stocks are not equal, why treat them as if they were? Let them prove their worth. If they are part of the top 100, they will float to the top on their own, step by step, and make you profit.
Leveraged Market Cap
The following table demonstrates the outcome of using equal weights with the same strategy in order to compare performances. No other changes were applied.
The same levels of leverage were used as in the previous tests. At all levels, the market cap weights performed better than the equal weight scenario. It generates more money, with a better CAGR, and a lower drawdown. As you increase the leverage, the differences get larger in favor of market cap weights.
The following table gives the differences for each scenario of the above chart:
A single trading decision, taken even before the program could start, is responsible for the differences in performance. And such a decision might represent billions over a 20-year period as illustrated in the "extended to 20 years" column.
Do you follow QQQ's weights or do you put in equal weights? The differences are more than visible over the long term. A simple administrative decision and it reverberates over all trades. Your choice...
A Not Required Strategy
You have here a strategy that outperforms its peers. And, surprisingly, the strategy is not even required since you could replace it with a single position in QQQ itself where none of your trading skills were relevant, requested, asked for, or even considered.
The simulation was just to show you would win the game even if all those stock prices would fluctuate wildly, almost random-like, over the entire investment period. No forecast was required, no guessing either. You just went along for the ride. The strategy doing over 60,000 trades, none of which you could have predicted beforehand. You did not know on which day it would happen or how many shares would be bought or sold. The whole thing behaving as if random-like generated. And yet, you could reach hit rates exceeding 70% as a side effect of the trade mechanics adopted.
I have seen so many trading strategies claiming that they performed better with equal weights and here, a simple demonstration, and it performed worse. The reasons are simple, just as stated above: equal weights reduced the bet size on performers while increasing it for those below average.
You give away the potential profits to gain shares in underperformers (losers). That is not an improvement in a compounding stock trading game.
With this pure rebalancing play, we can see the advantage of using weights scaled by some kind of merit system. Setting the strategy with equal weights is simple, you set all 100 weights to 0.01. Doing so will effectively show how a strategy will handle equal weights.
We should consider that equal weighing might effectively cost you, and based on the last line in the above table, it is an administrative decision that might cost your portfolio a lot! Over 600 times initial capital for not making the added 6.7% CAGR points that were there for the taking. Notice that overall, the differences in CAGR are not that great, only a few points, and yet, over time, they do make quite a difference. The power of peanuts...
It Is Only Rebalancing
With this trading strategy, we cannot use excuses like it was the trading signal that behaved differently, there were no signals used, of any kind. Or, it was the change in trend declaration that was off, optimized in some way, or either too early, too late, or somehow out of sync. Sorry, but no trend definitions were used, except for this vague and general bet on America. Buying QQQ either outright or through its proxy like trading all its constituent stocks was just that: a positive bet on America. We cannot say either that it was due to our stock selection even if it was. The whole process was outsourced, and over which we had absolutely no control.
We cannot talk either about survivorship bias. We knew at all times QQQ's composition and its purpose, right from the start. We opted to blindly follow in its tracks just as anybody else could using the same information. After the initial buy at t0, we most certainly hoped for all the stocks in the group to survive even if with time some would not. It should not have been much of a concern since the portfolio was designed to mimic QQQ's holdings playing the same highest valued stocks.
Regardless of any timing consideration or any trade triggering mechanics, the exact same stocks were trading at the same scheduled time at the same prices in all the tests.
Increasing Capital And Leveraging
The following table makes it clear that equal weights underperformed the strategy's market-cap tracking weights.
Based on the first chart above, using market-cap weighing and leverage over-expose best performers while reducing market exposure of underperformers. You are in it for the money, so why carry much dead wood around or be over-exposed to it?
The above chart is another example of the same trading strategy but this time using both leverage and increasing capital. The differences in performance are even more remarkable. Look at the bottom panel where the differences are estimated over a 20-year period. These four decisions: increasing capital, using equal weights, using market cap weights, and using leverage are all initial administrative decisions that can have a tremendous impact on overall performance as can be seen in the above tables.
All the tests were based on QQQ's raw data. Scheduled to trade at the same time, at the same price, every week. Nothing has been done to improve on the strategy's design. Doing so has the potential to greatly increase overall performance. You have a game plan here, study it, do the same tests, show to yourself what matters for you, and then adapt it to your way of thinking, add some protection and/or enhancements. Take advantage of what it represents, your future.
I consider this trading strategy as the bare minimum one could do. It certainly requires very little effort. You either buy QQQ outright and sit on it or you opt to trade its 100 stocks with its simple rebalancing routine. If so inclined, you could also improve on its trading procedures for even better results. If your trading strategy is doing less than this one, switch. And when you will find a better one, switch again, or operate both. The thing is: you are in charge, therefore, prove to yourself what matters to you.
Pass this one along to your friends, it could help them.
As always, it is all about your choices. May you make the best ones for you.
Created: Nov. 15, 2021, © Guy R. Fleury. All rights reserved.