Outskirts of Shrewd'm / Living Abroad
No. of Recommendations: 4
Hi gang,
I'm interested in going deep down the rabbit hole again to get my arms around the QQQE/QQEW vs SPY/QQQQ/etc subject.
I appreciate that most of the recent performance favors the market cap weightings, but I understand there are some easy-to-follow backtests that favor the equal weights. I was able to find these two screens from GTR pasted on here a while back but I don't understand what they're doing and if they favor equal vs MC weightings.
https://gtr1.net/2013/?!N1TEhttps://gtr1.net/2013/?!N1TCould someone point me in the right direction of the actual backtest/rolling backtest results to review?
Last question -- where can I reliably backtest/review the P/E ratio (current and historic) of QQQE/QQEW?
Thanks!
No. of Recommendations: 14
No. of Recommendations: 8
GTR1 has many useful features, selected by the Detailed Report boxes.
For example, a portfolio equity curve can be created:
1. go to
https://gtr1.net/2013/?~N1TE:h1::trp%281,1%29ne-99...2. Click "Portfolio Values".
3. Run Backtest
4. Download Report
5. Find "Daily Closing Portfolio Values" in the report.
6. Chart using a spreadsheet program.
Signal values (e.g. [sumE] and [sumMC]) can be downloaded.
Bear/Bull cycle returns can be calculated. For example, ^N1TE Peaks (50%) and Troughs (-33%) for Trading Cycle 0:
Peak/Trough Date Total Return Drawdown Market Days
19741003 -48 -48 453
19870821 765 0 3256
19871204 -40 -40 73
20000327 1576 0 3110
20010404 -59 -59 258
20010521 57 -35 32
20010926 -52 -69 85
20011206 64 -49 50
20021007 -61 -80 209
20071012 246 -31 1263
20081120 -58 -71 280
20250219 1645 0 4085
20250424 -11 -11 45
No. of Recommendations: 2
This is truly wonderful -- it'll take me some time to work through this, I will admit! I believe the second posting approach gives me the ability to create rolling backtests of X trading days and form some pretty interesting conclusions.
Thank you!
Two questions:
1) What does "_BCC0" reference? I've googled the heck out of it and came up empty handed
2) There was prior mention of proxies for EW indexes (such as 30% S&P500 index 70% mid cap index) yielding roughly the same results. Would anyone care to offer their views on this topic?
No. of Recommendations: 11
BCC0 is my shorthand for the timing method: close all positions when BCC=0.
BCC is "Bear Catchers Combined".
BCC is zero when all three "Bear Catchers" are bearish.
"These were dubbed "the Bear Catchers":
The NewHighstoNewLows trend
The 99DayRule (a.k.a. “Dying Bullish Euphoria”)
The SMA Slope Simple Moving Average Slope"
http://mechinvesting.wikidot.com/timing-methods
No. of Recommendations: 1
Between this data, a little Excel and a little ChatGPT, I was able to really dive into rolling 5 year performance of N1T vs N1TE. I didn't consider the BCC0 as I'm probably not smart enough to incorporate this into any strategy.
I observed that about 70% of the time, 5 year rolling performance of N1T was above N1TE. N1TE had significantly lower variance across the data series.
ChatGPT made a pretty chart for me that I replicated in Excel:
https://ibb.co/B5P2RLGZWould really appreciate the ongoing thoughts on this fascinating topic given the concentration of the Mag7 these days.
No. of Recommendations: 2
Here's one more curve that I found interesting:
https://ibb.co/cSnmZMzdHere’s the curve showing how N1TE's win percentage across 5-year rolling returns evolves based on the starting year then ending in 2025 (yes, Mungo would be ticked about my choice of end year... sorry there's probably an even more evolved way to do this):
Flat around 28–30% when starting in the 1970s and 1980s.
Steady rise beginning in the early 1990s.
Peak around 50% when starting near 2003–2004 (after the Dot-Com crash).
Sharp collapse after 2015, reflecting the recent dominance of mega-cap tech.
The red dashed line is the 50% mark — you can clearly see how close N1TE gets around early 2000s, then falls away.
No. of Recommendations: 14
A philosophical note.
Examining the historical relative performance of QQQ and QQQE (or SPY and RSP for that matter) is a lot like doing the following test:
You have two sets of two dice. The cubes in one set are covered with 3s and 4s. The other set is normal, 1 through 6. Same sum on each die.
You roll each set of dice 20 times, and sum the total. That represents the "CAGR" of that set of dice.
One of them will be a lot better than the other in terms of the total returns--it could be either. Does the result tell you whether you're better off with one or the other in future? No, it's just a finite bunch of random numbers that diverged one way, or the other, by chance.
Why does this remind me of QQQE and QQQ?
QQQ is totally dominated by a tiny number of stocks. (so is the S&P 500 these days). Those few stocks might do better than the rest, or might not. If a specific tiny number of stocks did better than average in the last month, does that tell you whether it will do better or worse than a much more diversified set in future? No. If they did better in the last 5 years or 10, does it tell you? Nope. It's just a random number dependent on the fates of a few specific businesses. We have absolutely no idea. (in the last decade the very largest have done very well, but in the prior 40 years the largest ~5 stocks by market cap were laggards by a mile on average. So which of those histories tells us much? Neither, perhaps).
In short, I don't think doing a deep complicated analysis into the relative returns of QQQ and QQQE is time well spent. The returns of QQQ are just the returns of QQQE, plus or minus a bunch of random numbers representing the fates of about five gigacaps. The random numbers in the past are not predictive of the random numbers in the future; the only thing you know for sure is that the returns will be a lot more random, and that your company-specific risk will be many times as high.
Jim
No. of Recommendations: 9
I really like the dice analogy — it’s a helpful reminder that a lot of what we observe is tied up in randomness.
That said -- I'm stuck on a counterpoint (that I am not convinced is right, but find compelling).
I think of Buffett’s old “stock market gin rummy” line — where investors keep trading away their winners to chase something new, only to realize later they were throwing away compounding machines.
I wonder if that's part of the story here too. Maybe it’s not just random dice rolls. Maybe it's that some businesses (the Mag 7 be damned), once they gain a foothold — through brand strength, ecosystems, scale advantages, whatever it is — actually increase their odds of pulling ahead over time. I appreciate your perspective that if I believe this, I am better off just making big bets on those 7 companies specifically... who knows.
So even though the past isn’t a crystal ball for the future, I’m not sure it’s purely random either. Sometimes, winners really do keep winning — and that might be worth studying, not because it guarantees anything, but because it shows how success can build on itself in ways that dice can't capture.
Thank you for the friendly and thoughtful discussion!
No. of Recommendations: 10
I wonder if that's part of the story here too. Maybe it’s not just random dice rolls. Maybe it's that some businesses (the Mag 7 be damned), once they gain a foothold — through brand strength, ecosystems, scale advantages, whatever it is — actually increase their odds of pulling ahead over time.
More likely not, I'd say.
It is true that the US economy has a few insanely profitable giants (and a couple of less profitable giants), but a large part of that recent leadership effect is a one-time-in-history thing and another large part of it is those stocks simply getting more expensive. For example, look at the changes in market multiple of Apple and Microsoft in the last decade.
Consider:
There are extremely compelling reasons to believe that the largest firms are on average at any given time much more likely to be overvalued than a randomly selected firm is.
There is also surprisingly compelling empirical evidence. For example, in the 18 years 1997 to 20014 inclusive (i.e., ending about ten years ago), an equally weighted portfolio of the largest 5 stocks in the S&P underperformed an equally weighted portfolio of the other 495 by a rather startling 5.76%/year. Ponder that number for a moment. Could you underperform the market by that much even if you tried?
My contention is that the evidence for the recent anomaly continuing (the anomaly of the few biggest being the best bets) is pretty weak, and a prudent person of business would not wager on it continuing. While realizing that it might.
The normal thing is for leadership by size to rotate over time.
Jim
