Mostly Voices [0:00]

A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines. If a man does not keep pace with his companions, perhaps it is because he hears a different drummer. A different drummer.

Mostly Mary [0:18]

And now, coming to you from dead center on your dial, welcome to Risk Parity Radio, where we explore alternatives and asset allocations for the do-it-yourself investor. Broadcasting to you now from the comfort of his easy chair, here is your host, Frank Vasquez.

Mostly Uncle Frank [0:36]

Thank you, Mary, and welcome to Risk Parity Radio. If you are new here and wonder what we are talking about, you may wish to go back and listen to some of the foundational episodes for this program. Yeah, baby, yeah!

Mostly Voices [0:51]

And the basic foundational episodes are episodes 1,

Mostly Uncle Frank [0:54]

3, 5, 7, and 9. Some of our listeners, including Karen and Chris, have identified additional episodes that you may consider foundational. And those are episodes 12, 14, 16, 19, 21, 56, 82, and 184. And you probably should check those out too because we have the finest podcast audience available.

Mostly Mary [1:28]

Top drawer, really top drawer.

Mostly Uncle Frank [1:31]

Along with a host named after a hot dog.

Mostly Voices [1:35]

Lighten up, Francis. But now onward to episode 277.

Mostly Uncle Frank [1:41]

Today on Risk Parity Radio, we'll just be taking it a little easy here in our easy chair after that very long episode last time. It's not that I'm lazy, it's that I just don't care. But we'll just go ahead and tackle a couple of emails here. Yes! And so without further ado, here I go once again with the email. And, first off, we have an email from my contact info. Oh, I didn't know you were doing one. Oh, sure.

Mostly Mary [2:21]

And my contact info writes, Thought you might find this of interest. Correlations are unstable, that's no surprise, but interesting methodology on how to deal with instability. The Science of Risk, Seeing Risk, the Fab Rick Show. For the fifth episode of Seeing Risk, the Fab Rick Show, Rick is joined by Jean-Philippe Bouchaud to discuss the disciplined applications for random matrix theory. Scenario generation with agent-based models and lost decades. Jean-Philippe's expertise combines physics and finance, often coined econophysics. Between being chairman of capital fund management, member of the French Academy of Science, and professor of physics at ENS Paris, he is knowledgeable on complex systems. His proficient study of complex systems proves interesting when applied to the financial market.

Mostly Voices [3:16]

You are talking about the nonsensical ravings of a lunatic mind.

Mostly Uncle Frank [3:21]

So I did take a listen to that podcast that you recommended there. It's interesting. It looks like a new podcast by Rick Bookstaber who used to work with Ray Dalio at Bridgewater and now has gone off to do a number of other things. But it really got at some theory and complexity theory in particular and reminded me of some things that I hadn't looked at in quite a while that I'll mention here. First, just some interesting takeaways for those who do not wish to partake of this particular podcast, which does kind of get into the weeds on complexity theory.

Mostly Voices [4:03]

You're that smart. Let me put it this way. Have you ever heard of Plato, Aristotle, Socrates? Yes. Morons.

Mostly Uncle Frank [4:12]

One of those things had to do with time scales and how they apply differently to things like correlations and volatility. And the point they were trying to raise is that short-term correlations and volatility are not really good predictors by themselves of long-term correlations and volatility. which tend to be more stable over very long periods of time. And that sort of went to a key theme of a lot of this podcast was that looking at just a short period, say the past couple of years, is not a good way of constructing portfolios or really doing anything with respect to long-term investing. Because what you were really concerned about are these long, bad periods like the early 2000s and the 1970s. And so you need to be most mindful of those periods and less mindful of current conditions, whether those have something to do with current valuations or anything else. The other thing that they raised that I was unaware of was that they had done some studies about trend following going back a couple hundred years and basically discovered that regardless of the markets, The idea of trend following, what we talk about in terms of managed futures, has been valid and has worked over all kinds of different markets over all kinds of periods of time. And what that tells you is that it's a robust methodology for investing or for making an allocation to. Now they also talked about some advanced techniques in modeling things, basically taking ideas used for modeling physics problems and putting them or moving them over into financial issues. One was random matrix theory, which I still don't understand or fully understand. I understand what it is. I'll link to a little article in the show notes. But I have to tell you, once we got to matrices in my mathematical tutelage, that was one place where I do not have very good intuition as to how they work, and I still don't. A more familiar idea was the idea of agent-based models. And what are agent-based models? Basically, you can set up a system where you essentially program what you call agents as acting in certain ways in a little environment. And these kinds of experiments go back over 20 years now, maybe longer. The most famous one is called the Sugar Experiment, where basically they had sort of three kinds of agents in this little model. One was programmed to cooperate with whomever it was working with, and they were all playing a prisoner's dilemma game over and over again. So one was programmed to always cooperate, one was programmed to never cooperate with the other side. And then the third one was programmed to do what was called Tit for Tat. So if it had an interaction with you or another agent and that agent did not cooperate, then it would mimic whatever the other agent did. And they watched a simulation of thousands of these things and they would tend to create these kind of little cities where all the cooperators were located and then you had the tit for tatters surrounding those things kind of as the bull work and then out in the open range were the non-cooperators. And you can run those kinds of experiments with all kinds of things, including financial models. You can pretend that you have this group of actors or agents trading on a system and adopting certain strategies. and then let it run and see what the outcomes are. And what is interesting about this, and this is also the same way that ants and colonies work, is that out of these kind of simple agent models or behaviors, you get very complex systems and you get emergent properties. So there's no way of looking at what you're programming in there and determining what it's likely to come out as. and whether the system that comes out of it is even stable or not. So it's a very interesting way of modeling something. And it allows you to see something in a dynamic form as opposed to the static form that most theoretical models rely upon. Most theoretical models are obsessed with equilibria or equilibrium, but you don't really have that in the actual markets. You have these people running around trading and doing other things. and that this is the kind of modeling that is often useful for that purpose. I had read a very interesting book about this over 10 years ago now. I pulled it off the shelf to make sure that I remembered it. It's called the Origins of Wealth, and it's by a guy named Eric Beinhocker, who was with McKinsey at the time. It was published in 2007, and it was all about using these kind of agent-based models to model economic systems. Essentially, it's applying biological models to financial systems. Charlie Munger would be proud.

Mostly Voices [9:30]

I am a scientist, not a philosopher.

Mostly Uncle Frank [9:34]

But I always thought that was a very good way to obtain a better understanding of how things actually work that are dynamic, as opposed to working based on static equilibrium or static equilibria. But what they were alluding to in this podcast is that this is a decent explanation for why trend following works because you do have essentially agents whose programming, if you will, is to buy things that are doing well or sell things that are doing poorly, leading to trends in a system. And that makes a lot of sense actually because it does mimic a lot of human behavior. I still haven't come up with any way to actually use those ideas in my own investing in any way, shape or form, other than to recognize that trend following and managed futures are viable strategies for part of a portfolio. The other thing this reminded me of, and maybe because we're talking to somebody who's from the French Academy of Science and a professor of physics in Paris, is the work of Didier Sornet on Dragon Kings. And there was a bunch of stuff written about this around 2010-ish, I would say. And Dragon Kings was an idea or an attempt to take off from the idea of the Black Swan that Nassim Taleb had wrote about and trying to figure out a way to essentially predict Black Swans. and somehow Dragon Kings got worked into that. I'll link to that a little bit in the show notes, but that's one of those things that I saw that I thought was interesting at the time, but never seemed to really go anywhere in terms of being able to really predict something and be a useful trading strategy. What was interesting to me is that also came out of the French scientific establishment, if you will. And if you know anything about French culture or history, you know that there is a tradition of academics taking the lead and coming up with systems that tend to get adopted widely, including things like the metric system and civil law codes. And I thought this seemed like another application of that tradition. Very interesting stuff there. I'll link to the podcast in the show notes, of course. And as always, thank you for your email. And we just have time for one more email today.

Mostly Mary [12:37]

And so last off, we have an email from Stanley and Stanley writes, Frank, I was comparing two small cap ETFs, the IJR, which is a blend tilted to growth and the VBR, a pure small cap value ETF. Back testing them on portfolio visualizer, I get some head scratching results. such as VBR having lower CAGR and larger drawdowns, worst year, higher standard deviation comparing with IJR. To summarize, VBR loses in most of the important points to IJR since 2005, which is enough to go through a couple of economic cycles. Isn't the purpose of small cap value to have a lower drawdown and enabled for higher safe withdrawal rates? For this reason, I think I'll stick with IJR in my portfolio unless you can really convince me that VBR is better than IJR in some important way. Thanks, Stan.

Mostly Uncle Frank [13:37]

Well, Stanley. Au contraire. Now getting saucy with you a little bit.

Mostly Voices [13:44]

Don't be saucy with me, Bernays. Here's the issue that you're running into.

Mostly Uncle Frank [13:47]

The funds you're looking at only have data dating back to 2004. And so you're looking at a very limited data set. And what you're comparing essentially is a small cap blend fund with a small cap value fund. Now it is true that value has underperformed since the great financial crisis in general. But if you look at a much longer data set, you'll get a much different picture. And so what you want to do here, and what you actually want to do in all circumstances, is not look at funds first, but look at asset classes first. And you can do that at Portfolio Visualizer by shifting to the asset class model and then put in, in this case, a portfolio that's 100% small cap blend and one that's 100% small cap value and model those. Now you'll get data going back to 1972 for that. If you were to do the same thing at portfolio charts, you'd get similar results. But what you'll see from that, that over that very long period of time, the 50-year period, small cap value greatly outperforms the small cap blend. And the compounded annual growth rates over that 50-year period are 13.47% for small cap value versus 11.37% compounded annual growth for small cap blend. since 1972. And so this actually touches on the last email and that other podcast that observed that you really want to be looking at the longest data sets you can and for portfolio construction in particular be mindful of the 1970s and the early 2000s. And when you do that, you get these kind of results which show you why you really want to be using small cap value over small cap blend because it's a better choice. The other reason it's a better choice, it's going to be more diversified from your total market or large cap value fund that is likely to be a large percentage of your portfolio if you are using VOO or VTI or VUG or anything similar like that. So hopefully that convinces you I will link to the analysis in the show notes so you can check it out. And you should. But if it doesn't, I just don't know what to do. You can't handle the banter. After all, it's your money. In terms of a small cap value fund, VIoV is a bit better than VBR in terms of being smaller and more value-y. Another good choice would be AVUV, which is this newer Avantis Fund, which also layers in a quality factor over that or a profitability factor if you prefer. But this is a very good example of the problem we face today in analyzing things because we're going to end up with this large recency bias based on anything after the great financial crisis up to 2020. because that was actually a highly unusual period when you compare it with historical norms or other things that have happened. And so the other lesson to be learned is to use the biggest data sets you can find and use multiple calculators because you want to find a basic consensus over a long period of time in these kind of asset allocation decisions. Hopefully that helps straighten things out. Do you think anybody wants a roundhouse kick to the face while I'm wearing these bad boys? And thank you for your email. But now I see our signal is beginning to fade. I'm afraid I'm gonna have to go on hiatus both this weekend and the following weekend, although I'm hoping to have a podcast early next week to talk about the rebalancing that I'm about to do in the next day or so here. of the sample portfolios. Two of our sons closed on houses in the past two weeks, and so we're going to be running around helping them do a few things in that regard.

Mostly Voices [18:07]

You call this a happy family? Why do we have to have all these kids? As the Vasquez Real Estate Empire expands. You realize what this means? It means bankruptcy and scandal and prison. That's what it means. One of us is going to jail. Well, it's not gonna be me.

Mostly Uncle Frank [18:27]

In the meantime, if you have comments or questions for me, please send them to frank@riskparityradio.com that email is frank@riskparityradio.com or you can go to the website www.riskparityradio.com and put your message into the contact form and I'll get it that way eventually. If you haven't had a chance to do it, Please go to your favorite podcast provider and like, subscribe, give me some stars, a review. That would be great. Mmmkay? Thank you once again for tuning in. This is Frank Vasquez with Risk Parity Radio. Signing off.

Mostly Mary [19:27]

The Risk Parity Radio Show is hosted by Frank Vasquez. The content provided is for entertainment and informational purposes only and does not constitute financial, investment, tax, or legal advice. Please consult with your own advisors before taking any actions based on any information you have heard here, making sure to take into account your own personal circumstances.