We need a risk model

Roadmap
30

I manage risk by creating a suite of models which are fundamentally dissimilar. Not just in holdings but in underlying strategy. Correlations will come and go. But if I am deriving returns from a different source, my risk is diversified even if the trailing price returns look similar for a while.

I don’t mean for this to be a debate by any means. By all means use risk models and many people find it useful. I am just of a different school of thought. If 50% of P123 users want a risk model and will pay for it, then by all means it should be made. I am not one to stand in the way even if I am not a user.

31

Kurtis,

Thank you. I might not use a P123 risk model either. So I will step back a little too.

But I have complete respect for what Robert and Rich are saying mathematically.

If, as Marco and Yuval have said, Riccardo is working on a risk model I cannot argue with soundness of their math. What they say should be considered if P123 decides to do some risk models, I think.

But yeah. I use something different and I do not really want to talk about it. So I get your side too. I am not tied to one model risk model if P123 does want to move forward on this.

The math should be right and someone with a finance degree should recognize it I think. Maybe just me on the last.

Jim

32

Yes. Even though it sounds more and more like it’s something only institutions can use to deploy large capital. Let us know if you are an institution and want this built in P123.

PortfolioVisualizer (PV) is a great guide to zero in what tools we need and clear out the noise. Maybe we can buy/license the algos (or enlist some of the students that use P123).

From what I can tell, we need the following PV tools

Risk Factor Allocation
Rolling Optimization

Several things are still fuzzy. The way forward I think would be a separate thread(s) to discuss implementation details, dissecting PV tools, and come up with MVP features.

This thread can remain for debates, theory, etc.

33

Marco, I have started a new thread with one addition recommendation from PV, FWIW-Jim

1 Like
34

“…Even though it sounds more and more like it’s something only institutions can deploy using large capital.”

I can’t figure how large amounts of capital is the driving characteristic requiring a risk model. My thinking is your remark can apply to anything on P123, simulations, screens, ranking, etc. so why are you delineating factor risk management as an institutional need when factor alpha research and deployment isn’t?

I’m trying to understand the large degree of hesitation here. My suspicion now is that no one wants to see evidence that their excess returns are well known risk factors. Please prove me wrong.

How do I create a working group or whatever is needed to keep this thread for things you described? I’ll try to show you an MVP risk model and work collaboratively. And yes, I believe you are correct with what P123 can lift from PV. FWIW, I think P123 can rather cheaply do way more than PV can by virtual of it’s current tech stack.

35

Our P123 models for example are easy to follow by anyone. Some have very low turnover, some not, but not excessive. They are made up of no more than 30 stocks, so even w/o fractional shares they can be followed with small amounts. And no shorting, so ok for retirement accounts

Doesn’t risk management by loading risk factors involves exposures to hundreds of stocks, long and short. Or how else do you do it?

On this thread I’m coming to the realization that perhaps we just create the infrastructure (API, components) and just do minimal tweaks the the UI

As far as where to start doing practical specs I’ll start a thread called Risk Model MVP and put a link to a shared google folder where the latest agreed upon spec will reside. It will be open for edits, comments, etc.

Sound ok?

36

Yes, it sounds OK. Thank you!

“Doesn’t risk management by loading risk factors involves exposures to hundreds of stocks, long and short. Or how else do you do it?”

It doesn’t. I will show you a very detailed walk through on how it’s done via an excel file and explanatory pages.

I think I may have not explained how risk factors work, and jumped ahead saying we need one, thinking users knew the fundamentals. They are betas. This was Hemmerling’s criticism; he believes what a risk model says is beta, should not be of concern. My initial request on this thread was that even a user like Hemmerling could very well want to create a ranking system that studies the alpha movements of stocks, only. What is beta and alpha can be left to a user.

Re changes to the user interface, I don’t think you need to do anything other than very minimal UX work as my original proposal doesn’t require it.

I suspect it will all become more clear once you see how these risk factors are calculated and used.

Thank you again.

37

What I have so far is in this folder. Ask us to become an editor here or via email. I have already added contributors to these threads

I identified key enhancements, and added some specs for Risk Parity.

Let me know if any terminology I use is incorrect.

Thank You

https://drive.google.com/drive/folders/1pvipMqYyoLoEDDVdSPjqVhaMDeq5tya9

1 Like
38

Korr123,

I do think this project should go ahead even if I won’t use it. By concern though is not about betas. It is more that I don’t believe trailing correlations between my portfolio return and some other market wide aggregate shows risk. Or explains returns. There are AI tools which can mimic portfolio returns using totally unrelated assets with different risk profiles. You could possibly blend bonds together to get a highly correlated return to some microcap index. But neither one carries the risk of the other.

If I was to develop a risk assessment model it would look at disaster scenarios along with the probability of the scenarios actually happening. Things like sky-rocketing inflation, recession or stagflation. Market crashes. Nuclear war. As many of these real-world risks as possible. The amount of risk management would need to be weighted by the likelihood of such an event. And the likelihood of events is always changing. As well as the severity. The risk that one of my portfolios might trade similar to some factor index isn’t really a big enough scare for me to change my underlying strategy. If I knew that the factor based index was likely to blow up with other factor indexes doing really well….then maybe.

The factor based risk model falls short of analyzing true risk for me. It might tell me if my portfolio return had some similarity to some aggregate value portfolio. But what is the risk? Is the risk simply that my portfolio might trade somewhat similar going forward to value stocks? Without actually knowing the factor characteristics of the individual stocks, I cannot say. Correlations come and go. As well, is it bad to have a model tilted heavily towards value? I don’t know the future. Maybe that is the best possible thing I could do. I am of the school that I should create the best possible value model, the best possible momentum model, the best sentiment model and so forth. And mix them. Each model will be super correlated to a certain factor and that’s by design.

I think that if I was a buy and hold investor with the goal of super diversification then maybe I could see the case. But in the matter of what I do, I am more worried that my model will fail because there is a flaw in my logic or that I am looking at correlations and not causations. My models will usually fail not because the stock is trading more like value or growth. It is because the strategy was designed poorly and the supposed alpha was data-mined in the first place making it fail in the real world.

But I reiterate that I do believe this tool should be made. It is just not something which I can find a use for. Using the Effective Number of Minimum-Torsion Bets might be closer to something I would use. Maybe. Risk Budgeting and Diversification Based on Optimized Uncorrelated Factors by Attilio Meucci, Alberto Santangelo, Romain Deguest :: SSRN

39

We’re not talking about “trailing correlations” but about trailing alpha and beta, which are related to but not equivalent to correlations. As someone who pays a great deal of attention to market beta, I feel a need to come to its defense. Beta quantifies “market risk”–the portion of risk that’s related to the market. That’s an essential concept, in my opinion, when managing a portfolio. How much of the risk that your portfolio takes on is market risk? To put it another way, how likely is your portfolio to crash when the market crashes? In my experience, market beta is one of the most persistent of all portfolio measures. It’s very hard to find a portfolio with a high market beta that will later have a low market beta, or vice versa. Moreover, it’s easy to design a portfolio with high or low market beta by using share turnover as a major factor. Another great advantage of market beta is that it has an inverse correlation with market alpha, and market alpha is a more reliable tool for measuring a portfolio’s success than almost any other tool out there.

Whether combining market beta with other betas will be valuable or worthless is an open question for me, and one I’m looking forward to exploring. The easiest solution, in my opinion, might be to introduce multiple linear regression, which is a logical next step from LinRegXY. One could then create various aggregate series and explore regressions to them.

Maybe we should use a different term than “risk” for a lot of this. I am never going to be comfortable with calling a low-volatility or a value beta a “risk factor.” And I agree with you that having a ranking system with size and value factors in it makes a linear regression to a long-short portfolio based on size or value seem somewhat redundant. But in general, I feel pretty upbeat about the possibilities of linear regression of returns.

40

Thanks Yuval and Rich,

I just want to add that Rich’s comments (principle component analysis or PCA above) tie in well to what Yuval is saying, I believe.

Historically at least (things could change going forward for sure), PC2 or what I called “risk off” above should have close to zero “market beta.” An interesting thing as Yuval correctly points out I believe.

I will probably be looking at PCA a little closer this weekend: trying to learn a little more about how this fits into portfolio construction.

I am not sure that weighting PC1 and PC2 according to the “variance they explain” is not a good (and very simple) idea for keeping your returns high while reducing your exposure to “market beta.”

A market-neutral portfolio could be constructed and that would be just an extension of this wouldn’t it? None of this is new or unused or even not-previously-discussed In the forum.

A lot of people In the forum hedge and use short positions. A lot of professionals hedge or use short positions using the concept of beta, Black-Scholes formulas for options etc. Not a single new or radical thing here!

I think we just keep ALMOST rediscovering what is done in finance all the time here in the forum. And at the end of the day decide to be “unredeemable luddites” as de Prado has called us (maybe a joke by him, I can hope).

For sure using PCA to weight your portfolio is a little more well-reasoned that just adding bonds in a 60/40 or 85/15 ratio. At least with the ETFs included in the above example, PCA takes into account the weights of XLP, XLU, GLD etc in creating the “risk-off” portion of the portfolio.

To reiterate PC2 is completely uncorrelated or “orthogonal” if you want to pretend (like me) that you really understand this. Actually, if unlike my wife and daughter you can visualize Cartesian coordinates-my daughter is great at statistics but some people simply cannot visualize it—“orthogonal” is not so complex. Sadly, I have trouble visualizing more than 100 dimensions :worried: Actually meant to say we all struggle with this as part of our human nature.

The PCA analysis above does not overweight bonds too much (less than 60/40 for sure) although one could reasonably ask whether bonds belong in a portfolio at all.

I would argue that the question of whether bonds belong in a portfolio goes away if you leverage the bonds up to the same expected returns (expected value) as equities. Then the question just becomes how much and whether you want to try to time that.

Anyway, nice (both of you)!

BTW, I get that I am still not up to speed with Korr123 on this and my points are probably “orthogonal” to what he is trying to express. In that regard this may have nothing to do with what he is trying to accomplish and is not a direct comment on any of that. That having been said, Rich’s and Yuval’s points can be useful, I think.

Jim

41

Yuvall,

I am going to stay out of this conversation from here on. I just wanted to defend my own position that I was against Betas. That’s not the case. And my issue with this is a fundamental one based on correlations.

Suppose I trade turnips from my garden. Then we compare this to the price of Apple. Suppose they appear to have high trailing correlation to each other. We then calculate the Beta with the method of dividing the StdDev of my turnip prices by Apple’s StdDev. And multiply this by the correlation. The question is - what is the real link between my turnips and the price of Apple? Zero. The trailing prices may have high correlation which is a component of Beta. But in reality, there is no casual link between them. My high turnip to Apple Beta factor is not useful. If Apple releases a new Iron Man suit and goes up 10x, my turnips are not going to go up 20x regardless of the trailing correlation and comparison of standard deviation of returns. Now if I knew I was selling Apple all my turnips and they were extracting turnip juice to power the Iron Man suit…then suddenly my turnip to apple beta is meaningful. But you have to make a causal connection in order to say this and not just looking at correlation and volatility.

My issue with this thesis is that you can have a portfolio of stocks that have no actual ‘value risk’ but it may look like you have high beta exposure to the value factor. And if I do not have value stocks in my portfolio, how can someone say I have high exposure to value risk just based on trailing price returns?

This is why I was advocating an approach that involves looking at actual value characteristics to determine exposure to the value factor. Perhaps this could somehow be modified to include market beta (which I use). Thus, I would feel confident in saying that I have a strong value tilt and I also high market beta. But I wouldn’t try to make some connection of a beta to the value premium using portfolio returns to value premium returns. Seems like a lot of assumptions are made in the middle. Too much is read into correlation and standard deviation without ever looking at the actual factors.

42

Kurtis,

You are right. This should all be done by looking at the financial data.

But if the price of eggs and apples and cereal and….everything else is going up at the supermarket I am not going to short port bellies (is that still a thing).

Admittedly, better if I check on the supply of pork bellies, price of feed etc before making a decision. Not the type of thing I do every morning after seeing if the data is up to date at P123, however.

Sometimes I am not ever sure what the name of the stock is or its sector—assuming I can remember the tickers for all of the stocks I have bought. Just for me personally, I am going to say I use some correlations no matter how you look at it.

EVERYTHING we do with ranks is correlation based. Value metrics “correlate with the price-action” for reasons we can only speculate about with behavioral economics.

But you have a point.

Jim

43

Jrinne,

Ok. I lied. I am going to reply again. I agree with you. And the illustration you gave is good. However, you named the products and pork bellies is related to food. That’s the fundamental and causal link. But suppose you were blindfolded and you had no idea which products or services were under discussion. You didn’t know if it was food or pet rocks or tickles. You would have a harder time making the link.

And correlations are fine as long as they are paired with something. And it is also the reason why a hyper-fitted ranking system with no logic is not worth trading. All correlation and no underlying reason.

At best, I think the risk meter could be a simple tool to say, ‘hey maybe you have too many value stocks here and also high beta stocks. Take a look’. And then I take a look and maybe it is true and maybe not. If so, I can remove a few value stocks based on characteristics. If that is my goal. Unless I want it to be a value portfolio. But I probably already know that I have a value tilt and have high market beta just by looking at the average factor rank for beta and value.

The risk model could be a tool to simply suggest further inspection if you are concerned about unintended factor tilts. But I would imagine the more active your strategy is, the less meaningful this will be. If I am trading one-week price reversions, I wouldn’t put too much stock into an aggregate price to book benchmark.

44

Kurtis -

How could you possibly have any meaningful correlation between the price of turnips and the price of AAPL? Correlation doesn’t just happen randomly. If there’s no actual relationship, there’s going to be no correlation between the returns.

For example, the returns of mining stocks are going to be more correlated with those of chemical stocks than with those of financial stocks. A portfolio based on price to sales will be more correlated with a porftolio based on EV to EBITDA than with a portfolio based on earnings growth or price momentum. Correlation studies provide evidence that factors are related to each other. OK, my examples are obvious and trivial. But I have found some rather surprising relationships by studying correlations. Keep an open mind!

45

Kurtis. My analogy cannot be taken too far. But my car cost me a lot more than even the sticker price when I bought it.

Turned out the price I paid for my car was related to pork bellies in some weird way. But surely something is not correlated to pork bellies……hmmm, seems like I pay a lot for everything now but……uh, something.

Better if you can figure it out based on principles but that is truly hard too. Remember "transient "inflation? All COVID still?

I read 100 reasons for things in the news every day. Some happen to be correct and a few of those only by chance.

Correlation has its limits and I do not really disagree with you I think. I am just saying I use it every trading day in the morning. That’s all: Not to be totally discounted.

46

Yuvall,

I think this is being taken out of context. Correlations are fine. But say nothing of causation. Small people drive small cars and big people big cars. But I won’t grow just because I buy a bigger car.

Correlations are great. But now we are using correlations and volatility to imply that the two are linked and one is directly tied to the risk of the other. Without looking any further under the hood. We need to go one step further.

I am making the case for using Betas, correlations, covariances or whatever paired with something that attempts to make a logical connection between the two. I am not saying throw it out. I am saying we could probably make it better if we find a way to establish a higher likelihood of their being an actual relationship.

This reminds me of pairs trading. It makes a great narrative in that you find two stocks which are co-integrated and they move around some central mean with an equilibrium. Then you look at the stocks and they are so totally unrelated. And the co-integration falls apart with no profit. But if you can find 2 stocks which are fundamentally similar, in the same industry group where returns are being made from the same drivers, then there is a higher chance that the variance in returns might come back to some mean. You marry the two concepts.

I have a very open mind. But there are reasons why this type of risk model has had so many changes over the past 10 - 12 years. I think with the brains here we can actually design something new and better. And that has some forward risk mitigation. But if we want to just make a copy of an older risk model…as I said before…that’s fine. I just want to see more effort going into a causation. So far, that hasn’t been addressed. Adding in factor characteristics into the equation takes us one step closer to that.

edit:
To be honest, I never meant for this to get so out of hand. I am truly sorry for that. I am not some Stone Age villain trying to prevent technological progress. I do want us to build a better risk model. And for real this time, I will leave this thread. You have nothing to gain from my input. It seems like I am just creating a debate which I didn’t mean to. Sorry if I come across combative. It is hard to properly express the emotion behind the words in posts. I meant no harm.

47

Kurtis,

Maybe I should apologize. I do use correlations and sincerely believe it is pretty much all I do with regard to investing at P123—even using P123s ranking system.

I do not understand how P123’s method is not like a regression or correlation of future returns to factors or factor ranks. I have no ability to see it otherwise. Given that I simply cannot understand how something being a correlation automatically disqualifies it as a method.

It is a sincere view. Using my car and pork bellies as examples was not meant to be personal in any way.

Jim

48

I think it is crazy to calculate risk of a factor based on factor statistics.

That is trying to determine the risk of a car loaded with factors not seeing the weather (tornado, snowstorm or sunny weather?) or the street (racetrack or bad road). And there is no car that will drive you through a tornado without risk. Correlations are fine until all correlations = 1 (in a crash like 2008 no factor in the world would have saved you from at least a 40% drawdown long only).

Risk is not driven by factors, risk is driven by the macro regime which favors factors or dislikes factors and the valuation of the factor to itself historically (2000 value was cheap, 2007 it was expensive etc.).

Important in that regard is that you have a balanced ranking system that diversifies over a lot of factors, so your timing of what macro likes does not have to be perfect.

Also you need an alpha source that can not be easily arbitraged away (therfore micro caps and
small cap where the big institutions can not trade due to liquidity reasons).

Take your best strategies, put them in a book, one long only and one with a short ETF (which correlates to the factors you use in your book) and compare the capital curves and decide in which macro regime you choose which book you use (long only or hedged).

I you design your book with a 50% short, and you see no alpha start over (because then you
have a beta book!).

Book with a 50% Short

image
image1920×1080 349 KB

Also, it is very important to know which factors come back strong and fast after a 50% drawdown if
your timing was wrong (e.g., you did not choose the hedged book in a severe macro regime).

High beta can take a decade to come back and make new highs, small cap value momentum takes max 1-2 years (good systems below 1 Year!) to make new highs.

Andreas

49

TL;DR: Here is where we are in the forum with medicine as an analogy (and where I was when I finished medical school). I.e.,before statins (cholesterol lowering agents became mainstream): “Hey, did you read that article that said cholesterol levels are correlated with heart disease?” There are still serious questions about this but progress can be found in most texts on the subject.

BTW, they may still be wrong about many things but they were definitely wrong about cholesterol then. Correlation not being causation was discussed a lot with cholesterol as well as with smoking causing cancer. It is always just a lot of handwaving and gesticulating early on. But my main point is you cannot become a doctor without text-level understanding of many subjects, I think. And rightly so.

@judgetrade You said: “Important in that regard is that you have a balanced ranking system that diversifies over a lot of factors, so your timing of what macro likes does not have to be perfect.”

I have a simple question: Isn’t this exactly what Korr123 is trying to accomplish on the portfolio level? I understand that Korr123 may also be wanting to balance his shorts but I think that would only emphasize that the purpose is to balance and “diversify over a lot of different factors.”

BTW, I really do not care what P123 does with this. I think I will end up staying with Portfolio Visualizer. If nothing else the discussions in the forum are going to slow adoption of many useful techniques in the near future.

But I do not really fully understand what Korr123 is doing and want to learn. I would like to understand how this is used and how effective it is at accomplishing its intended use.

Has Ricccardo studied this in any detail in school so we do not have to reinvent the wheel on this STARTING WITH THE USEFULNESS OF CORRELATIONS? Some of this has been fully discussed with much of this in the 5th or 6th edition of a text, I would guess.

I am pretty sure that if correlations are discussed in a text about Finance at all, the general usefulness of correlations is established (or discounted) in an earlier chapter. Real sure actually.

But getting back to Andreas’ quote, isn’t that exactly what Korr123 is trying to measure and accomplish at the portfolio level? For long only portfolios, wouldn’t it balance diversification over a lot of factors which you said is desirable? Or isn’t that the goal, at least, with the question of how effective it is at doing that a separate question that could be answered empirically?

Note, I have no interest at all in how one would use this to create short positions but others might have a legitimate interest in that.

The only other comment I would have is mainly about my own comments (but not exclusively my comments). Pork bellies, car prices or how good cars are–when loaded with factors–at avoiding tornadoes will only go so far. I think I will be looking at some text books and papers on the subject at some point. There is certainly a lot about beta in any serious text. I think what Korr123 is interested in implementing is talked about in the texts in detail also. I have read about it, I believe, but it is a more complex subject and my understanding was limited on the first read-through. This is not in my priority list now (did I say I don’t really care what P123 does with this but I would like to learn about the technique as long as people are posting something with some useful information) but I don’t mind learning about it in the forum now.

Marco presented a paper in this regard earlier: @Marco: Thank you for advancing the discussion a little better than I have been able to so far. Real research is much appreciated.

While this is a complex topic that I do not fully understand, I still have the simple question about whether this is trying to accomplish exactly what judgetrade says is important. Or not.

To be sure, the above should reiterate that I do not know enough to make a recommendation about where this should be on anyone’s priority list. I am only saying this discussion might not be the final word for me. I will probably ignore the discussions about correlation—although you should check out the great insights about pork bellies above :wink:

Jim