Even staff at P123 recognize, now, that there might be a little overfitting at times. Bootstrapping is recommended as a possible method to alleviate this problem. But there are flawed models to be sure. If you want to wait 10 years to find out which ones are flawed that’s a plan.
This does not even take into account survivorship bias. I know I used to follow other strategies from some of the Designers that just are not there now. Even so, look at what would have happened if you placed equal amounts into all of a (particular) designer’s models that you can see.
Maybe you could have know which models to pick ahead of time.
The ONLY automated strategies that do this (rotate ETFs mostly) come from Georg, using machine learning techniques away from P123. Yes regression (and other techniques used by Georg) is a machine learning technique.
If P123 wants to stand back and let others do this they should. Marketing and new features is above my pay scale.
I am not against screening and then looking at the 10-K. I admire people that can do that. I cannot. Way above my pay scale and abilities: not really what I came to P123 for either. I would like to see someone’s out-of-sample results on this. I am not the only one able to post here. Besides, on the topic of Designer Models, are we even sure what factors to look at on a 10-K? Do we know enough about the model to second-guess unknown factors? How far will we go second-guessing a black box?
Anyway, I stand by my only conclusion: not a good 2 years by any measure. Everyone has their own, unproven, theories to explain this. We are just “short-sighted” is a story I am familiar with. One I deal with every day in the eye clinic;-) Not that applicable here, in my professional opinion.
Interesting idea. Instead of checking the correlation of each portfolio holding against the Spyders, I tried looking at the portfolio as the instrument. Unfortunately P123 doesn’t present a portfolio’s equity curve as a Series so we can’t get there from here. Maybe, as the first baby step, P123 could offer a GetPortSeries function. What that in place, we could start experimenting with making a port that picks the lowest correlated ETF to a stock port and then combine both in a Book.
I am not suggesting to stick by a strategy forever, but still 2 years is too short. I would argue that actually seeing some underperformance relative to a benchmark in the past at times may actually be desirable because it suggests that the strategy has not been overfit. It is not impossible to beat the S&P500 on a long term basis but you have to be willing to underperform it for periods of time. Most investors aren’t which is why indexing is better for them because at least they can match (or slightly trail). The S&P500 is not a magical index which picks the best stocks. It strives to do that but the rules for inclusion are readily available and can be simulated. At its heart it is a large cap, positive EPS, momentum strategy. So if you can design strategies that pick up on better characteristics without overfitting and becoming too complicated you should be able to outperform. You also need to accept that you aren’t going to beat the index every year. In fact a lot of active money managers have gripes about the very fact that because funds are scored year by year it is nearly impossible to engage in strategies which can outperform in the long run but may underperform for periods of time in the short run.
I will say that Marc is right. Don’t overfit your strategies and keep them simple. Complicated overfit strategies are an exercise in correlation hunting. Correlation is nothing more than statistical analysis of the past and without a strong logical fundamental basis means nothing.
That being said why is there so much interest in decorrelating portfolios. Just seems like another exercise in data mining. IMHO diversifying amongst other asset classes (bonds, commodities, cash, real estate, emerging markets) would be a lot more successful in lowering portfolio risk. Yes those other classes may put a drag on your gains when stocks are doing well but will can offer true alternative growth or stability when stock markets systemically fall.
Not sure I am making any of the mistakes you are cautioning against. I do hope people feel free to opine.
The only thing I do not like at all is having to pass new ideas that use substantial data through the P123 committee : already decades behind and getting worse.
You have nothing new that you want to do? Without my approval (and the rest of the forum)?
So you make my point about nothing being accomplished in the forum. If you are happy with the Designer Models you should subscribe ( if you are not already). You are free to do so.
If there is an error in my statistics let me know. You are free to interpret away as to the cause of this. I do not think I speculated so no disagreement on my part.
If you have a serious question about newer methods then email me. I will link to some books on Amazon.
Based on your original question, in all honesty I am not sure there is more I want to do. I can see the trap of more analysis and data leading us to better fit our models. A year ago I might have answered differently but I’ve come to realize that investing is far from an exact science nor can be quantified as well as we would like to think our models suggest. At this point I am satisfied to simplify my models and try to look at ways to diversify away risk.
I’d be curious about newer methods if not to be informed.
I don’t use the designer models. I’ve built my own but quite frankly I haven’t executed them for a long period of time. I am suspicious of designer models because they are available for others to use and are somewhat black box.
I do share your concern that designer models appear to have overfitting bias as well. My own models underperform for periods of time but I try to combine them with other models and asset classes to even that out. I will say that my models are incredibly simple in comparison to some of the free Portfolio123 models. I am very leery of over complicating models…
I’m not sure I would apply any new information but I would be curious to read what you are referencing. As I am a knowledge bug I’m always open to new ideas.
“The Man Who Solved the Market” shows what can be done with a little math. And I do mean a little—Simons’ math IS decades old by today’s standards. Mercer is at the helm now. Simons had a Sharpe ratio of 7.5 at one point. Worth $27 Billion. I contend there is something to be learned.
If you want more modern methods: “Advances in Financial Machine Learning.” By de Prado. Very inexpensive.
If you remain unconvinced I am good with that. You are welcome to your opinion. I mention this only because you asked.
I think the P123 committee will not be considering any of these methods anytime soon anyway.