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yuvaltaylor

1.) Do you use nodes in your ranking (i.e all your value factors are a node, all your quality factors are a node, etc. Similar to the P123 combination model)? I think you mean composite nodes. The answer is no. 2.) I understand that a lot of your factors are custom and not the standard P/E, etc., but how can those be SO much more effective than the standard ones? I would at least think they would be in the same ballpark, with custom factors having a slight edge. For example, using the P123 Combination Model (which seems reasonable, solid and well thought out) on the the microcap universe, produces less than stellar risk adjusted returns. I don't know the answer to this, but I would hazard a guess: that the standard factors have simply been arbitraged away. 3.) With so many factors, if your model falters, how will you be able to track down why it's faltering? I have always followed the idea that less factors are better, so you can clearly see which ones may be contributing to under performance. I.E, the idea that things should be as simple as possible and no simpler. Thanks Through constant backtesting, and including the last few months/years in the backtests. There are a large number of factors that I no longer use because I found they didn't contribute to my backtest results. Regarding the idea that fewer factors are better, please check out the following article: https://backland.typepad.com/investigations/2...ngortradingerrors.html Yuval Taylor Product Manager, Portfolio123 invest(igations) Any opinions or recommendations in this message are not opinions or recommendations of Portfolio123 Securities LLC. 


charles123

Appreciate the feedback and the link. I actually read that article, but will refresh on it. So basically, your ranking is one large list of factors with a very small % weighting in each. I wonder how eliminating the conditional nodes would change things in my ranking systems. Thanks again. 


yuvaltaylor

Appreciate the feedback and the link. I actually read that article, but will refresh on it. So basically, your ranking is one large list of factors with a very small % weighting in each. I wonder how eliminating the conditional nodes would change things in my ranking systems. Thanks again. Just to be clear: I use conditional nodes quite often, but seldom use composite nodes. Yuval Taylor Product Manager, Portfolio123 invest(igations) Any opinions or recommendations in this message are not opinions or recommendations of Portfolio123 Securities LLC. 


rtelford

Just to be clear: I use conditional nodes quite often, but seldom use composite nodes. Yuval, I'm curious, do you find the ranking different in not using composite nodes, or is it more for ease of management of factors in not using composites? Theoretically, if weights are equivalent using composite or noncomposite nodes, the overall stock rank should be the same. i.e. with no composites, factor weight is 5%. Or using a composite, the composite family rank is 25%, factor weight is 20% within the composite, the overall factor still gets a weight of 5% overall. If there's another nuance to it, I'd be curious to know. Thanks! Ryan 


yuvaltaylor

Just to be clear: I use conditional nodes quite often, but seldom use composite nodes. Yuval, I'm curious, do you find the ranking different in not using composite nodes, or is it more for ease of management of factors in not using composites? Theoretically, if weights are equivalent using composite or noncomposite nodes, the overall stock rank should be the same. i.e. with no composites, factor weight is 5%. Or using a composite, the composite family rank is 25%, factor weight is 20% within the composite, the overall factor still gets a weight of 5% overall. If there's another nuance to it, I'd be curious to know. Thanks! Ryan Let's say you have four factors and ten stocks, and let's say the ranks are as follow: A 30 60 70 80 B 60 80 10 0 C 0 10 20 90 D 50 70 0 40 E 40 50 90 70 F 90 30 80 30 G 70 0 60 10 H 20 90 40 60 I 80 20 30 50 J 10 40 50 20 With no composite nodes, E ranks at the top, followed by A, then F. But if the first two factors are composite and the second two are composite, then you have the following ranks: A 40 80 B 90 0 C 0 70 D 80 10 E 40 90 F 80 70 G 20 30 H 60 50 I 50 40 J 10 30 Now F ranks on top and E is second, with A taking third place. Now take another look at the first table of ranks. F does very poorly on factors 2 and 4. So it's appropriate that F gets third place. With composite nodes, that distinction vanishes, and F gets first place. Similarly, all four of E's scores are pretty highat least average. So it's appropriate E gets first place. But with composite nodes, he comes in second. If we were to shuffle around the factors so that factors 1 & 3 were combined and 2 & 4 were combined, we might come up with yet different overall ranks. Ditto if we combine 1 & 4 and 2 & 3. The only really fair way to judge all the candidates is to keep all the ranks separate. Yuval Taylor Product Manager, Portfolio123 invest(igations) Any opinions or recommendations in this message are not opinions or recommendations of Portfolio123 Securities LLC. 

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yuvaltaylor
at Jan 8, 2021 10:56:39 PM

abwillingham

The first two pages of the ranking system tutorial discuss this odd blending of weights. https://www.portfolio123.com/doc/side_help_item.jsp?id=29 But its not clear why it does this instead of using a simple weighted average. Is there some advantage to calculating it this way? Tony 


Jrinne

Thank you Yuval. That is very counterintuitive and you have given the best explanation I have seen of this. Perhaps this is a slightly different topic. But not really given the real effect composite nodes have on a ranking system (illustrated here). One can do factor analysis on P123 factors. It will end up putting similar (or correlated) factors together into nodes: Sentiment factors into one node, Value in another etc. Giving weights to the factors and the nodes and removing some factors that are not contributing much. Is what factor analysis recommends (or principle component analysis which is similar) better than separating out the factors? Theoretically, it could resolve some issues with multicollinearity. As a bonus, it removes factors that are not contributing and therefore might be causing problems with overfitting. Multicollinearity is where some of the factors are highly correlated which I am sure happens when people use a lot of factors here at P123. Does putting factors into composite nodes actually help with multicollinearity here at P123? And if so, does that end up being important or even useful? I don’t know of course. But there are theoretical reason for usingor not usingcomposite nodes. Or deciding that it may not make much difference to your system. Jim From time to time you will encounter Luddites, who are beyond redemption. de Prado, Marcos López on the topic of machine learning for financial applications 

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last edit by
Jrinne
at Jan 9, 2021 5:48:10 AM

mike0001

I am trying to wrap my head around this, I understand a composite node is calculated different than a conditional node. Just to be clear for example is using a conditional node with 4 factors calculated the same as using a stock formula or stock factor with 4 separate factors? MikeC 


Jrinne

I am not trying to hijack this thread but I definitely sympathize and agree. And it is an unbelievably important topic, here and elsewhere. I wish Yuval or Marc could give us another truly great explanation and make this intuitive for all of us. Honestly, if they could I think we would all be making a lot of money without a sweat. We would get it. Get how all the factors are interacting and see the patterns without needing P123 or anything. Of course, if we were that good we would be recruited to work on some quantumcomputer project somewhere and be predicting everything flawlessly. As far as making this intuitive, the mathematicians will not be of much help. They look at this as an ndimensional space. Remember those matrices from high school algebra? They were supposed to help us with this. It is the best they have for making this intuitive or at least tractable. Einstein had just a brief, intuitive glimpse at fourdimensions and changed the world. In a sense, they are clearly right. Their’s is a way to look at it. And Marco can—and does—put those matrices into a computer with a little matrix multiplication, I would guess. Maybe he uses a lot of loops at the cost of some computer time. Either way, one of the great things that P123 does for us. P123 is solving this for us and we often never notice. Marco is taking care of a lot of difficult math and making us feel like we are the smart ones. He does it seamlessly and that well. No doubt the mathematicians are right when they say: "No one can imagine more than 3 dimensions in their heads." And even they get into HUGE PROBLEMS when they try (Einstein being a rare exception). For example, they spent decades worrying about local minima (a potential concern for the boosting Steve Auger uses) that GENERALLY DO NOT EXIST IN MORE THAN 3 DIMENSIONS. Instead, there are "saddle points." Decades wasted by the mathematicians because their intuitive understand of this is no better than ours. Anyway the mathematicians will say—after a little matrix multiplicationthat Yuval got it exactly right. From time to time you will encounter Luddites, who are beyond redemption. de Prado, Marcos López on the topic of machine learning for financial applications 

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last edit by
Jrinne
at Jan 9, 2021 10:36:16 AM

