Jonathan Briggs (03:03):
And so folks like myself can certainly help with some of the applied math that goes into building such things. The funny story is, I don’t think it was a success early days, but it was a bit before its time because I think had some success going forward. But the person who hired me was looking specifically for someone with a Ph.D. with an applied math background. And that was my first entry point into actual finance where I learned about stocks and bonds, which I had no exposure to up until that point. Literally, I’d never invested a penny in my life.

Jonathan Briggs (03:30):
And there was one person, particularly at Schwab. A guy by the name of Jim Peterson, who, he’s still there. He’s actually the CIO of Charles Schwab Investment Advisory, a great human being. He took me under his wing and started to teach me about The Journal of Finance and quantitative investing. So it included things like Fama and French and momentum and things like that. And so that was my first introduction. I spent about four years at Schwab, as I said, helping to build this robo-advisory, got exposed to their Schwab equity ratings, something you may have seen, that it was pretty clear to me that it was time to move on.

Jonathan Briggs (04:00):
And I would say the giant in the industry at that time, was out of San Francisco. It was a place called Barclays Global Investors. They had pioneered the quantitative investing process, literally written the book on it. But there’s a book by Grinold and Kahn who were Barclay’s Global Investors. We can talk about it at some point perhaps. And primarily they built an incredible business off of the quantitative equity world, which was they had basically gathered a ton of assets and would continue to gather assets over the next six years while I was there. And really won accolades for their performance over that period of time, using a quantitative approach.

Jonathan Briggs (04:32):
So to me, I think it was formative. That was like going and getting my next degree, so to speak, in quantitative asset management. And I worked with some of the brightest minds, at the time, in that space, Richard Grinold, Ron Kahn, Mark Britten-Jones, [Moree Wakeard 00:04:47], Ken Kroner. These were are all names that became pinnacle in their career in quantitative investing until BlackRock acquired them in 2010.

Jonathan Briggs (04:54):
And my understanding was BlackRock really was interested in the iShares business, which BGI had built along the side of its active investing strategies. And it just turned out, I think, to not be a place for me. Just, it was a different culture, different utility function for the organization at that point. And so I wasn’t the only one. There was a natural diaspora, I think, from BGI out of San Francisco at that point.

Jonathan Briggs (05:18):
So a recruiter called me out of the middle of nowhere, an interesting human being. He basically called me up and he said, “Hey, I know what you made last year. I know your wife’s name, your daughter’s name. I know everything about you. There’s this place called CPPIB that’s recruiting a lot of talented folks.” And I hung up on him, literally. I thought this guy was a sock head. How could you possibly know this?

Trey Lockerbie (05:36):
A very odd approach.

Jonathan Briggs (05:38):
A very odd approach, but what was hilarious is that he said, “Okay. Trust me. Go walk across the aisle to the head of the global macro team, and go ask him about me. Ask him, ‘Do you know, Bob [Shainheit 00:05:48]?'” 00:00:05:48]. And I was like… I went and talked to him. It was Ken Kroner at the time. He said, “Oh yeah, he’s a great guy. He helped BGI build from the ground up.” So he ended up turning out to be legit, thank God.

Jonathan Briggs (05:58):
And yeah, so the CPPIB was essential, which is the Canadian Pension Plan Investment Board, was recruiting a bunch of talented folks across the world and bringing them on board in Canada to help build their investments internally. And then such began the journey. I started on the global macro team, which was the style of investment that essentially is betting economy versus economy through the various instruments.

Jonathan Briggs (06:18):
Bridgewater Associates is, I think, the pinnacle of global macro investing. Most folks have probably heard of them. So I spent a few years working on the macro team. There were some changes going on, on the equity side, and at some point, they reached out and asked me and said, “Hey, why don’t you come and be the head of the quant equity research team?” That was the beginning of my equity experience as a researcher, in terms of actually doing, what I would call forecasting of expected returns. That was a journey that was about eight years long.

Trey Lockerbie (06:46):
Talk to us about that pension experience, because as we were discussing beforehand, they did a good job forecasting this gap, this demographic gap with the baby boomer generation and managing that quite well. So I’m wondering if that is an isolated event, if the US pensions have followed suit, or if this is an anomaly as far as their strategy and execution?

Jonathan Briggs (07:09):
I’m not a pension consultant, so I don’t really know the health of the average pension system in the world, but there are certain countries that have had the foresight to look ahead and say, “Look, we have a demographic problem. And we have a younger generation, which is, it is large. We have a lot of excess requirements going forward for health and benefits.” And so they set up a process to essentially do a wealth transfer, intergenerational wealth transfer to take care of the older generation, their retirement.

Jonathan Briggs (07:36):
And CPPIB was certainly one of those incredibly insightful and long-thinking, long-range thinking in that regard. And have collected assets over the last, I don’t know, 15, 20 years to address that. And they’ve been incredibly solid. Actuarially, I think they’re well provisioned for the next 75 years. And they take those assets that they gather and they reinvest them on behalf of the people of Canada to further the returns as much as possible. And I think somewhere like Norway, I think has done something very similar, some Middle Eastern countries. It’s a remarkable decision to make by any society to invest that kind of energy to do that.

Trey Lockerbie (08:16):
As I understand it, CPPIB has something like $600 billion they’re managing and it’s understandable at that size, that you are essentially forced into finding systematic ways to invest because it’s just the sheer size of it. So if you can understand this effort to develop systematic investing and that approach. I know we’re going to get into that.

Trey Lockerbie (08:37):
I’d like to start with some pretty basic questions around systematic investing, or quant investing for shorthand. Talk to us about what defines, say, a quality factor versus a value factor versus something like a momentum factor. Let’s go from there.

Jonathan Briggs (08:52):
Yeah. In fact, when people talk about quantitative investing, they basically lean into this concept of value quantum momentums or the pillars. In fact, AQR, which is now one of the preeminent quantitative investing shops, their founders published a bunch of articles related, in The Journal of Finance, et cetera, on these types of behavioral ideas. Not behavioral, but in these types of exposure ideas, they go back quite far in time. So Fama and French were the original creators of the ideas of these broad cross-sectional exposures that explain terms.

Jonathan Briggs (09:20):
And what I mean by that is, what they do is they take a group of companies which maybe it’s the S&P 500, or the Russell 3000, and what they’ll do is they’ll essentially create a characteristic which could be a ratio of some quantity that basically describes something about a company. They then take these and they rank them across the entire set of companies that they’re interested in looking at. So again, the S&P 500, the Russell 3000. And these then describe a portfolio. So the process of transforming these quantities into a portfolio, which is very straightforward, and then they backtest it or they run it live.

Jonathan Briggs (09:54):
Now, it turns out that these three categories, they’ve become, they’re not a single factor, but they represent a bucket of factors. It values this idea that as a free cashflow at a price, so this idea that there’s a certain amount of free cash flow a company has and if the price is very high, then that’s less attractive than a company that has high free cash flow, the price is very low. So fairly intuitive. And you can rank all the companies of interest in that context. That’s a value factor. This idea is that something over price is a value factor.

Jonathan Briggs (10:26):
Quality is essentially a measure of profitability, for instance. So gross profits to total assets. Again, it’s a ratio to normalize, to the size of a company nut again, you can rank. The more gross profits compared to the total assets you have, obviously the better the company you think it will be. And so you can rank on these things.

Jonathan Briggs (10:42):
Momentum is actually very different. It’s basically saying if I look at a particular company and I see the trend and its returns over the last year or six months, or however long you want to look for that trend, it again indicates to you that trend should continue. That’s the underlying thesis. So then you can rank companies based on these trends.

Jonathan Briggs (11:00):
Now, the idea is then you have these three factors, these three buckets that all sort of look alike in terms of within a bucket, and you can create portfolios of each of these and then add them all together. So you’re essentially averaging across all of these, what we call characteristics of value, quality, momentum, or a characteristic that you can average across.

Jonathan Briggs (11:18):
Now, you can be bespoke in the amount of weight you put into each one of these. And that of course is part of the secret sauce for some quantitative investors. But in the end, you end up with an aggregate portfolio. So everything gets squished together and you have an aggregate view and this then gives you, the ones with the highest ranking is the one you want to go long. The ones with the lowest rankings, are the ones you want to go short. And so this is quantitative, I would say a very typical or traditional quantitative investment strategy. You can use it in equities, you can use it macro, you can use it in credit.

Trey Lockerbie (11:46):
And talk to us a little bit about this idea of smart beta. Is this encapsulated in what you just described? Or is this a whole other level?

Jonathan Briggs (11:52):
Yeah, so what happened there was, this is a fascinating story. It’s the evolution of quant in some ways, maybe not for the better. So BGI and AQR and places like this, were building these factor portfolios and using them for investing. But just in the way I described them to you, I think it should become clear that they’re not so sophisticated that you can’t actually remember them if you walk out the door and go to some other place and reconstruct them. So there’s this diffusion process where quantitative ideas became fairly well known in the investment industry. In fact, people published articles about them, obviously in The Journal of Finance, they made books about them.

Jonathan Briggs (12:28):
So what happened was that providers of product, sort of took away from the hedge fund world, the quantitative hedge fund world and said, “Well, look. I can just repackage these very same things and sell them back to whoever wants them, whether it’s retail, whether it’s institutions, at a very, very cheap price.” Because again, the process of building this is very, very systematic. You can have a computer do most of the work.

Jonathan Briggs (12:50):
They rebranded this smart beta. So they took something which was considered proprietary and exceptional and alpha, which is this idea of being able to beat expected returns, where it comes back and says, “Okay, well now this is something very commoditized. I can sell it to everyone.” And this became this smart beta revolution, where everyone is buying exposures to things, whether it’s price earnings, whether it was actual price, whether it’s momentum, whether it’s quality factors. The smart beta just became a way to sort of justify that this thing exists in the context of a particular type of regression framework and quants like to talk about things in regression framework. So it was just a natural way to start marketing the product.

Jonathan Briggs (13:29):
I think it’s had some fairly dramatic effects on people’s view of quantitative investing, because it looks very commoditized at this point. In fact, in the process of talking about what I do to other institutional investors, one particular very smart investor came back and said, “Here’s how I view smart beta lifecycle.” Do you have a second? I can read it to you. It’s absolutely hilarious.

Trey Lockerbie (13:49):
Yeah, let’s go in.

Jonathan Briggs (13:50):
Okay. So there’s 15 steps. I know it sounds like a lot, but it’ll go quick. One thing I want to point out is quants basically talk about backtests, which is this idea that I can simulate an investment strategy and show you the returns that would’ve had. And you should feel very comfortable then that this thing will perform like that in the future, which of course, everyone should know, that’s probably not the way it’s going to play out. But instead the backtesting is part of the whole process of quantitative investing.

Jonathan Briggs (14:14):
So step one, launch product with amazing backtest. Okay, it looks great. Two, experience underperformance. Okay, terrible. Three, show clients that similar underperformance has shown up in the backtest. So, it’s just statistical noise. Four, experience more underperformance. Five, show clients that the level of underperformance has never appeared in the backtest, so it’s sure to revert. Six, experience more underperformance. Seven, publish a paper. This is critical because only smart people can publish papers and smart people are good at investing. Eight, experience more underperformance. Nine, tell clients that the strategy requires patience and that the backtest results are more indicative of the future than the live results actually are.

Jonathan Briggs (14:55):
10, experience more underperformance. 11, claim that smart beta providers as a whole, have overpromised, but that you are absolved of any such wrongdoing, because you were criticizing everybody else yourself. 12, experience more underperformance. 13, tell clients that you are a good diversifier, since you are underperforming while others are outperforming. 14, experience some outperformance, but not nearly enough to make up for inception to date underperformance. Declare victory. That’s sort of the view now of the allocators to quantitative investing.

Trey Lockerbie (15:25):
Now is that one of the reasons we’re seeing, what I’ve heard you call, a quant winter that started it as of 2018 or so?

Jonathan Briggs (15:33):
Yes, this is exactly right. This is just the result of investors who put money into quantitative strategies, particularly institutional investors, are quite frustrated. So in 2017, 2018, 2019, 2020, this idea of value quality momentum, just didn’t perform well. And it was broad-based across most of the quantitative investing universe.

Trey Lockerbie (15:54):
And talk to us a little bit more about the commoditization, because what is needed for that to revert? Some new innovation, I suppose, that would need to come and disrupt the market. Correct?

Jonathan Briggs (16:05):
Yeah, exactly. So actually this was the choice that faced me on the research side of things. I looked at this and I said, “Look, Goldman Sachs has shown up at my door, offered to sell me this thing.” And this was prior to 2017. So in expectations, something this commoditized, and other providers too, whether it’s Vanguard Assurance [inaudible 00:16:23], should I expect outperformance based on these things? And I made the decision that, “No, I don’t think that is likely to continue the performance we’ve had in the past. We need to do something different.” And then to do something different, you obviously have to invest time, energy, and resources, which is never an easy decision in an ongoing concern, particularly of an asset manager.

Trey Lockerbie (16:43):
So you dug into some research. I’m curious what you found when you were assessing people like Buffett, Klarman, Soros, et cetera. You’ve written about this underlying theme that you found that came from studying those three in particular. So I’m curious, what was the thread that kind of tied those three together?

Jonathan Briggs (17:01):
The idea was you have successful investors. These were folks who had a long track record, had shown resiliency across many market conditions or market cycles. So the question really was from the quantitative perspective, what we’ve been told always or intuited ourselves or argued amongst ourselves, was that folks like these shouldn’t be successful by definition. Why? Because they don’t have massively diversified portfolios, meaning they don’t have three, four, 5,000 stocks in their portfolios.

Jonathan Briggs (17:28):
They’re actually very focused on understanding companies at a very detailed level, but of company, they’re not thinking about these things as, “If I backtested this idea, would it give me a great result?” They’re really saying, “Hey, this is a company. This has a purpose. It exists to do something. And is it a good thing?” And that can be applied to macro as well. This doesn’t have to be just company that applied credit, it can be applied to currencies, whatever it is that you can really sort of dig in, try to understand what is the real economies doing, and then trying to understand what investors think about it.

Jonathan Briggs (17:58):
As it became more and more evident to me that what we had was commoditized on the quant side, it became more and more eventually, as I said, that in the fundamental space, there’s a lot of rich understanding of what markets should be doing or could be doing, or might be doing. And as a quant, you’ve kind of ignored that channel, right? You may have a superficial interest in it.

Jonathan Briggs (18:19):
So for me it was, well, if we’re going to look someplace new, there’s this whole body of work, which we’ve disregarded because it isn’t statistically interesting. And what is it? We have some shining examples of folks who have outperformed the markets for a long time, we should learn something. And that was, it’s a bit a humble pie. It’s basically being able to say, “Hey I’ve adjusted, whether it’s the applied math background, the PhD, plus all the statistical analysis we did at Barclays Global. Step back and maybe somebody else could teach you something new.” And that was really provocative for us and for myself.

Trey Lockerbie (18:52):
What, in your experience, did you see as sort of the shortcomings of that quantitative approach? Is it that, obviously it’s needed for the scale we talked about in the pension arena, but is it applicable to use that same approach when you have a much smaller account? Does it break down anywhere along the way?

Jonathan Briggs (19:08):
The concept of quantitative investing, which is this idea that your returns or your risk adjusted returns are a product of two things. One is, the skill you have, which is intuitive to everybody, multiplied times the breadth or the number of attempts you have to express that skill.

Jonathan Briggs (19:26):
So think about it as gambling. If you can count cards, go to Vegas, you get thrown out of the casino probably, but let’s assume you don’t get thrown out. If you count cards and you sit at a table and you play blackjack, single deck blackjack, the advantage conferred upon you by counting cards is small. It’s not huge. And so to really reap the rewards of that ability to count, you have to sit at the table and play many, many, hands.

Jonathan Briggs (19:47):
And so that’s a perfect example of a combination of skill, which you have to beat the house, plus the number of times you can make those bets. It’s kind of like gravity. Maybe you don’t believe in it, but it’s true. It exists. It’s statistically closest thing to a proof you can do. And so particularly in investing. So that’s a long way to say that those, that property of investing a skill times the number of bets you make, is true for anyone.

Jonathan Briggs (20:12):
Obviously the trade-off between skill and the number of bets you make, can be made, which is why we see fundamental investors taking very few bets, but they potentially have very high skill, whereas potentially quantitative investors have the lower skill, but have many more bets. So you can make up for those two things. If you think about it like somebody trying to do this on their own, that number becomes really problematic. So the number of bets you have to make becomes almost unmanageable if you’re managing a portfolio with 10,000 securities and it’s almost impossible for an individual to do, or do well.

Trey Lockerbie (20:45):
Now my understanding is, or at least originally was, around this idea of quant solving for the psychology aspect of investing. Kind of taking the human out of the equation because as humans and our biases, we’re prone to make so many mistakes and really shoot ourselves in the foot, so to speak, when it comes to investing.

Trey Lockerbie (21:03):
How does the quant approach solve that exactly if you’re trading so often, as you mentioned? Obviously there’s trigger points that you’re being alerted on to get out of a position at certain points. So talk to us a little bit about what that looks like.

Jonathan Briggs (21:17):
There’s a bunch of interesting points you brought up there. One is how do quants trade? And then the other thing is the biases when you are trading. How do you, in the second judging, in the triple judging, in the quadruple of looking back and maximizing your regret. So yes, indeed the systematic process, which is tied to this idea of a backtest, is to try and remove the biases.

Jonathan Briggs (21:39):
So you create a thesis and that thesis you believe in and make empirical evidence around it being properly vetted and properly tested, like a scientific method almost. Then when you let it go, the impetus to interfere with that is very low. So there’s a big barrier to jumping in to try to overcome that. And so that prevents you from introducing your own biases. So certainly in your research, you could have introduced your own bias, which happens a lot, by the way, even in quantitative space. But getting the process moving, the thesis is to try and remove that effect.

Jonathan Briggs (22:14):
And also the idea that you can test things in time and geography. So you test an idea in Japan, test it in the US, Canada, Australia, emerging markets, and you test it through multiple cycles of the macro cycles. That helps you build up a body of evidence that whatever you’re doing probably doesn’t have as much bias as you might have if you were just to do it off the side of your desk, off the top of your head.

Jonathan Briggs (22:35):
Now that’s the idea that we can introduce some firewalls to the bias problem, but quants don’t necessarily have to trade quickly. So when I said that we made lots of bets, there are two ways to make lots of bets. One is to make of bets across many stocks. And one is to make lots of bets through time. So high frequency traders might trade three stocks, but do it every millisecond. That’s a lot of bets. As long as their bets are independent of each other, then that’s a great way to bring in that idea of breadth.

Jonathan Briggs (23:04):
But there’s another set of quants, like myself and, Barclays Global Investors and AQR, who are not really looking for speed. They’re looking for understanding a thesis that says I can make not many bets in time, not too many bets in time, but a lot of bets across names. When I say cross section, it’s the company dimension or the time dimension. So the cross section is the company dimension. Time dimension is a whole different game. And it turns out that you want to use as much of both as you can to increase the statistical significance.

Jonathan Briggs (23:34):
But I want to caution people not to think of this quant as fast trading all the time. That’s certainly like the Flash Boys and things like that, it became very popular and are easy to understand that speed is an advantage. But I would flip you over towards the fundamental space, which says speed isn’t the only advantage, but being able to predict is another advantage. And fundamental investors do that all day, every day. And so perhaps a mix of those two ideas, which AQR and BGI sort of extended into and I think that we extended further in that direction is, can you make things that are more forward-looking, more prognostication to help you with and then use that less speed, but more future looking?

Jonathan Briggs (24:15):
The primary reason you want to do that is because high frequency trading has an impact on markets that erodes whatever returns and because this is your paying cost, because you trade more and more or more. And markets are smart. As you trade fast and you trade more and you trade with more dollars, more and more people are attracted to that behavior and front run you. So it becomes this sort of endless, like a fruitless exercise to try to trade too fast, because then you erode all the returns that you may have had by understanding something better.

Jonathan Briggs (24:44):
So if you really want scale, if you really want to trade a lot of assets, a lot of dollars. And this is why institutional investors are attracted to these ideas is that you want to trade slower and you want to hold for longer. And then that reduces your frictions or your transaction costs.

Jonathan Briggs (25:00):
So quants have traditionally never really gone out as far into the future as fundamental investors. Fundamental investors literally could get, like Buffett, I don’t know, maybe he’s looking out 10 years, maybe seven years, five years, but they’re really pushing the boundaries of forecasting into the future. And quants have not done that to date. These ideas of quality value momentum, you’re looking at about a three month horizon, maybe a little bit longer with some value factors, maybe six or seven, maybe eight months out. So they really left behind this whole idea of long horizon forecasting, And I would argue that actually, quite deeply, that quants are not forecasting, in the strong sense, that a fundamental investor is doing, if that makes sense.

Trey Lockerbie (25:40):
Yeah, it does. And what’s coming to my mind is, especially for retail investors, understanding these three factor approaches and how they integrate together potentially. So if you wanted to build a portfolio with them, I’m wondering if any of the factors tend to overlap or if they’re kind of all in their own island. So for example, value and momentums almost seem to be opposites, right? Because you think of earlier companies like Facebook, Amazon, when they weren’t generating any earnings, you could easily look at that on a value factor basis and say, “Well, this is a terrible investment. There’s no earnings.” Whereas, if you were a momentum trader, you would’ve done quite well.

Trey Lockerbie (26:17):
And value has been underperforming for a very long time. So obviously if you’re packaging a portfolio together, you’d probably want some exposure to each one. I’m not looking for financial advice, but I’m curious, is that a common approach, even on the institutional side, where they’re trying to put a certain allocation towards these different buckets?

Jonathan Briggs (26:36):
So this is true even in our framework, which deviates from quality value momentum, but to your point, that’s exactly right. So this is diversification. Diversification is the number of stocks, but it’s also the number of ideas. So momentum as an idea, or value is an idea, or quality as an idea, ideally what you’d like them to have, is different return signatures through time. And what that allows you to do is, build a portfolio which is robust across different regimes potentially. And so that’s exactly what they’re looking for. So diversification across factors is part of the lexicon for quant investing.

Trey Lockerbie (27:10):
All right. You’ve done all this research for many years now and you’ve developed this economic framework that I’d love for you to lay out for our audience and talk to us about how the elements of quality value momentum evolve from here.

Jonathan Briggs (27:24):
So, as I said, the ideas behind quality value momentum, my expectations had paled a bit. And I’ve also mentioned the fundamental investing world of, it seemed like a very fruitful direction to go and look and learn from. And so that’s exactly what myself and my team did. We basically took a pause at the time and we sat back and we said, “Let’s try to reexamine some first principles, what it is that we actually believe that markets are doing. And let’s see if that ends up back at quality value momentum again, which is fine. In which case, we’ve come full circle. Or perhaps we end up in a different place.”

Jonathan Briggs (27:57):
And luckily I had a thought partner in this. This incredible human being, Frank Ieraci, who’s now the Senior Managing Director of global equities, fundamental equities at CPPIB. I’ve known him for 12 years almost now. And he and I and my team at the time, we sat down and we started to think about, “Okay, first principle, what do we believe in?” Well, first of all, what we said was, markets are forward-looking. So price is a function of things expected in the future. It’s not a function of things looking backwards, of things looking forward.

Jonathan Briggs (28:25):
Of course, that expectation of some futures is predicated on all the information available to you now. So that includes sun spots, that includes how many people drive in the morning. It also includes every… so it’s a very general statement, but it’s quite powerful. So it allows you to ask other questions like, “Okay, well, if prices are very forward-looking and prices changed through time, then can I say something about the change in price?” It turns out you can. You can do some very simple manipulation of that idea and you combine some things together and this gets a little bit on the mathy side.

Jonathan Briggs (28:56):
But what pops out is that, “Well, okay. Changes in price, which are really returns, are described by some sort of attention based on surprise.” And what I mean by surprise is, markets expected something in the future, they got something else, and then there’s a correction. So this idea that markets are forward-looking and they react to revealed information, and that the size of that reaction is based on the attention being paid to it. Again, these are very general concepts, but that’s a really key part. So markets are always looking forward, looking to expect something. And then they have to react when that something shows up and it’s different than what they expected.

Jonathan Briggs (29:36):
The next piece is, “Okay, well, how are we going to discover what those things are that markets are expecting?” We want something that makes markets move a lot. You need big returns that are available to you. We wanted to move frequently, which is this idea of breadth. So we built many bets at the game. And we certainly don’t want the game to go away, whatever it is that we’re looking at. We don’t want it to end in three years, or five years. We want this thing, whatever this surprise thing is, we want it to go on forever.

Jonathan Briggs (30:00):
And the final piece was, well capital markets aren’t their casino. Capital markets are there to allocate capital. This is the Benjamin Graham, the voting machine, weighing machine. So the short horizon, sure, maybe there’s a voting component, but in the long horizon, capital markets are there to allocate capital efficiently. That was the foundation. So it’s this idea of surprise, which is based on expectation. So what do markets expect? Can you understand that?

Jonathan Briggs (30:25):
This rearrangement, once markets are surprised, this idea of surprise has to be worth the pursuit, and then the idea that, well okay, let’s look at fundamentals and let’s look at it through the lens of an empirical investor. So let’s think about this. Let’s design a set of experience to test all of these. And that’s really what we did. We basically spent about a year and a half thinking about these ideas and testing them.

Jonathan Briggs (30:49):
The test is interesting. So bear with me for a second. So if you look at how fundamental investors invest, so you can go online, you can say look at KPIs, for instance. That’s kind of like the language of a fundamental investor. KPIs tell fundamental investors a lot about the state of a company, what it’s likely to do in the future. And you can buy KPI libraries by the way, there are thousands of them, thousands.

Jonathan Briggs (31:11):
And so assume you take all of those KPIs and you say, “Well, I’m going to be the perfect fundamental investor. I’m going to assume that I can predict KPIs perfectly at any horizon, one year, two years, three years. Then I’m going to take those predictions and I’m going to put them in a backtest framework. I’m going to basically be like a quant. I’m going to create a quant portfolio out of these ideas, and I’m going to run a backtest and see what happens. See if any of these ideas create really lucrative portfolios.” Interestingly enough, most of those ideas don’t produce lucrative portfolios.

Jonathan Briggs (31:40):
And the more you go too far down that rabbit home and say, “Well that’s absurd.” But as a quant we are like, “Oh, of course we do this. Fundamental investors don’t do anything. Of course this is obvious.” And I’ll come back to that, because that’s actually quite arrogant. And we had to eat crow on that. But what we did find is that various measures of cashflow across different horizons were incredibly powerful. Meaning if you could predict these cash flowy type variables in a portfolio context, you almost achieve perfect foresight on returns themselves, which is a remarkable concept.

Jonathan Briggs (32:11):
It means that, oh my goodness, maybe fundamentals, cashflow fundamentals and returns are tightly coupled together, are tied together. I don’t want to say causally linked, because that sets up a whole argument about specific mathematical definitions of causality. But let’s just say for the sake of argument, there’s a causal linkage between these cashflow growth, growth of cashflow through time and returns. There’s a horizon component to this.

Jonathan Briggs (32:36):
In a short horizon, not so much. That tie to returns in a one month horizon is not so strong. That tie to returns at a three year horizon is almost 90%. So you’re really starting to see this interesting interplay between, again, the Benjamin Graham, the voting versus weighing, that in the long horizon these growths of cash flows really describe a causal relationship that markets are weighing on constantly. In the short horizon, there’s a bunch of other stuff going on that don’t necessarily, at least in the variables we looked at, explain the terms. So this is remarkable.

Jonathan Briggs (33:09):
So the question to ask then is, set aside quality value momentum for a moment, which have none of that forecasting power at all, not even close to that. These other things, wow, we couldn’t have accidentally found these, because they’re so close to returns themselves. They have to be statistically significant. There’s no human bias that could have introduced that. And by the way, it works, again, globally across all time. So then, “Oh well, these seem like really rich things to understand.” So kudos to fundamental investors to really think of just cashflow models. I think that’s the right thing to do. But again, all these KPIs and useless. So the question was, “As a client, can we predict these things?” And this makes all the difference. Now we can lean into the predicting realm of things and how do we do prediction, which opened up a whole new world for us.

Jonathan Briggs (33:55):
So then, “Well, what do you use to predict?” You have tools? Linear regression is one tool, but everyone listening to your podcasts heard of the AI revolution. So linear regressions are linear, meaning they’re very simple relationships that they can capture,. Machine learning and AI captured very, very complicated relationships. The problem is, with machine learning and AI is that it requires a lot of data to help organize themselves to pick out complicated relationships.

Jonathan Briggs (34:26):
The worst thing you can do is to give very little data to a machine learning algorithm and tell it to figure stuff out, because it will figure out relationships that aren’t really true. We call this overfitting. The problem we found was in trying to forecast returns with machine learning, which has been done by many, many organizations, I think some had some success, I think very few have had success, is that the complexity of returns themselves don’t lend themselves to scarce some amounts of data.

Jonathan Briggs (34:52):
So you really are operating at a dangerous end, and really the deep end, like a dangerous part of the swimming… I don’t know if that’s a real expression. I’m probably mixing my metaphors over here. It’s a dangerous part of the ocean, because you have this thing which is very dynamic, returns are very dynamic. And you have this very limited pool of observations to train your machine, your non-linear machine learning algorithm on to try to predict returns.

Jonathan Briggs (35:13):
And so this creates a problem, right? So if you start to use this technology, it’s kind of like giving somebody who’s untrained a weapon, to somebody who’s untrained, it’s quite dangerous. And so what’s happened is there’s been a bunch of folks, a bunch of managers who tried to use it with disastrous results. So in sample, it looks like when you try to test, it looks great, when you try it live, it looks terrible.

Jonathan Briggs (35:33):
I think one way that that has been short circuited is that in the really high frequency space, again, you go back to lots of observations, every millisecond is a thing. And I think you’ve seen some success, you’ve seen because again, the numbers of observations are really, really high, it gives the machine learning something to play with. On the other hand, on the long horizon, you’re talking about three months, six months, three years, there’s just not a lot of independent observations and so the machine learning really struggles to create good forecast that you’d expect to work out of samples, so when you go live.

Jonathan Briggs (36:04):
But what we observed was, quality, the proper use of cash flows is not the same as returns. While those things, remember we said seem to be causally linked together, they’re a very different beast. They’re not being impacted by the speed of human thought, they’re being impacted by the speed of human interactions, which is buying and selling and producing goods and services, which is just not as fraught with chaos. And so by structuring a problem where we’re trying to predict a fundamental outcome, you basically change the dynamics of the thing you’re trying to predict.

Jonathan Briggs (36:35):
And I think fundamental investors really knew that. They basically said, “Look, price is really, really hard, but if we assume this cointegrated relationship between price and fundamentals, let’s focus on the fundamentals. And again, we’ll think about it in the longer horizon.” So absolutely brilliant. If we set about to replicate that behavior, let’s look at, let’s do this forecasting exercise. And this thing turns out to be well behaved.

Jonathan Briggs (36:54):
So fundamentals turns out to be well behaved relative to returns. So we started the march down the machine learning path. We started linear stuff. We slowly increased the amount of degrees of freedom that this thing was allowed to play with. And then we said, “Okay. We’re going to let the dogs out. We’re going to try machine learning.” But again, not completely crazy, because we’re saying we’re going to focus it on particular variables related to real economic fundamentals.

Jonathan Briggs (37:18):
And that started to work. It’s like, “Oh, this is really interesting.” But again, we never achieved anything close to perfect foresight on these variables, on these cashflow type variables. We really struggled, “Okay, how do we get our accuracy up? Okay, we did something interesting here.” The returns that we can generate with portfolios like this when we backtest and don’t look commercially viable. No one would pay for these things. Now what?

Jonathan Briggs (37:37):
Okay. A couple of things. One is, first of all, we probably should put really interesting things into the machine learning process. Something we know has value in forecasting the future. And here’s where I come back to, eating crow moment. “Oh, KPIs might work. Bingo.” So now you start putting in KPIs into a forecasting problem for fundamentals and, “Oh my goodness. All of this fundamental knowledge in the world was not for nothing.”

Jonathan Briggs (38:06):
This is a huge companion of human intellect. People spend time denoising business models. They look at business models and they say, “Let’s throw away the insignificant things and look at the significant things.” And so fundamental investors have really spent a lifetime understanding how certain variables interact with each other within a certain business model. Once you bring those into the forecasting exercise, you really start to see a lift in the forecasting accuracy.

Jonathan Briggs (38:31):
Now, we went back to the fundamental investors that we’d spoken to about this. Of course they’re like, “Oh yeah, you idiot. We never said these things had value in forecasting returns. We said, KPIs had a lot to say about cash flows. And cash flows have a lot to say about returns.” And so as a quant, basically, where was I? I’d basically gone down the yellow brick road and ended up being a complete believer in fundamental investing.

Jonathan Briggs (38:53):
And actually it was this idea of surprise. Remember, I talked about surprise. Well, if you give machine learning a forecasting problem, it’s going to spend a lot of its time and energy and budget when you give it a certain number of variables, a certain number of observations to work with to help calibrate it. It’s going to end up giving you a lot of things that are already well known, which is unfortunate. It may do a great job, but it’s telling you stuff you already knew.

Jonathan Briggs (39:15):
So, “Okay, well that’s not good. Let’s add some structure.” Surprise is the structure. So you have to understand what markets expect, subtract that from what the outcome will be, which is surprise, and then force the machine learning to focus on the stuff that’s not known, which goes back to this idea that surprise is what defines returns. But we did it in this way which was through a fundamental channel.

Trey Lockerbie (39:39):
Now surprises can happen either up or down. What I’m kind of curious about is, there’s been a lot of surprises, especially in the last couple of years. And when we had the original COVID market crash, that was obviously a surprise. And then there was a surprise to the upside following thereafter. So how would a machine learning approach have performed through some event like that, both up and down?

Jonathan Briggs (40:02):
So to your point, expectations are not a single beast. There’s multiple cohorts of investors out there investing in a particular style. And so they have their own expectations to avoid being stuck with one set of expectations, which can be right or can be wrong. In which case, you’ve limited your ability to earn returns if you’re always fighting one set of cohorts, when another one is actually dominating price. You have to understand the heterogeneous nature of investors.

Jonathan Briggs (40:29):
Bridgewater talks about this a lot as well, but we’ve internalized these ideas very deeply ourselves, is that even if we take a very simple example, there’s a dominant view in the market which is expressed by the dollars being invested in a particular view. So let’s say, Trey, you say sales growths is going to be high for this company. And there’s a bunch of people like you and you all put in a lot of money into that view. Well, if that were the only view in the market, price would shoot up, it would go crazy.

Jonathan Briggs (40:55):
But we know there’s a set of people who disagree with you in the market and they’ll invest a certain amount of money to disagree with you and they’ll impact price as well. So you end up with this equilibrium state, which gets created in price, where there’s a certain amount going one direction, a certain amount going… and there’s certainly one that’s dominating, but that dominance is not so severe that price becomes crazy. It doesn’t just go one direction or another very, very rapidly.

Trey Lockerbie (41:18):
The chaos of cohorts.

Jonathan Briggs (41:20):
Right. The chaos of cohorts. I like that. So you want to understand because not everybody is going to be right. At some point, there’s a truth moment where your bet is marked to market, you get a contrarian view that’s marked to market. And typically markets figured out that shortly before the reveal, so there’s a lot of correction that goes on, but there’s a mark in the market. And in that moment is where you see returns becoming really, really interesting.

Jonathan Briggs (41:45):
So in the case where you were right, let’s say you were the dominant view, let’s call it consensus for argument’s sake, and the contrary view has to correct itself. So it would’ve been losing money all along the way, because you were dominating price, price was moving against their view. Contrarians look at you and they say, “Oh my goodness, you were right.” They switch directions. And they match your position. You would get an extra bump at the end of the day. Returns would go your way.

Jonathan Briggs (42:09):
And so what you would see for Trey, what you would see for your portfolio if you were betting or your stock position when you could extrapolate to portfolio of these, you would see a slow diffusion of returns heading your way. You’d have a nice ramp up in returns, and then followed by, once the information was revealed, maybe some slight bump, still some post announcement drift.

Jonathan Briggs (42:30):
The person who was betting against you of course would lose all along the way. Terrible. But on the other hand, maybe I was right and Trey was wrong, even though, and you dominated your price returns up to a certain, up until just before announcement, you would say, “Oh my goodness, I need to switch my position.” You would flip your position to match mine. And in that flipping, you would move all your price, all your dollars towards my position and change the path of return.

Jonathan Briggs (42:53):
So for me, as a contrarian investor, would’ve been losing money. And then as you push and you realize that you needed to switch, my returns would’ve shot up. And then again, a post announcement drift where I would’ve had a decay. Now what’s really cool about this is we’ve just described to you the return signature to the factors that we talked to you about earlier, which was momentum and value. So as a consensus being right, you would’ve seen, in the impacted price, people following your view and piling in. At the very end contrarians would’ve realized you were right. You would’ve had an extra bump and then a decay. This was like a very nice momentum shaped pattern. Conversely, when contrarians are right, you see this negative return followed by a pop in return space and a decay. That’s a value signature.

Jonathan Briggs (43:36):
And so we made a full circle when we came back and we said, “Oh my goodness, this idea of surprise, we have already evidenced in different quantitative investing strategies.” One is value. And one is momentum. These represent different cohorts in the market betting differently, and yet there’s a convergence at the end. But the idea is we can measure both of them and have a better forecast of the future if we can arbitrage both sets of cohorts. And so you end up with a beautifully diversified set of return streams based on who you’re arbitraging, who you’re trying to bet against. And the probability is, in a cross sectional sense, you’re getting it right in multiple dimensions.

Trey Lockerbie (44:13):
When I hear you talk about that, what’s happening for me, is just this resolving to buying and holding. Because I think so often we’re not really thinking about the cohorts in play and a lot of people are just thinking, “Hey, it’s zero sum. It’s right or wrong. It’s me and this guy on the other end or gal on the other end and this is the trade.”

Trey Lockerbie (44:34):
But we’re all sitting around the same poker table. But we’re not. One person’s playing poker. The other person’s playing craps. The other person is… It’s just like there’s this wash or this pool of different people with different time horizons, with different strategies, that is creating so much noise. That just makes me want to sit back and say, “Look, this is why I don’t want to look at my screen for 10 years.”

Jonathan Briggs (44:56):
That’s right. In which case, in that sense, you’re just buying the equity risk premiums, buying an index fund allows you to coast along with all that noise and ignore it and just get the value of being long in equity with a group of equities which are likely to go up over time.

Trey Lockerbie (45:11):
So that sounds like a robo-advisor to some degree.

Jonathan Briggs (45:15):
That’s right.

Trey Lockerbie (45:15):
But robo-advisors though, they’re just looking at your age and your time horizon and your risk tolerance, et cetera, et cetera. And they just say, “Here’s your package.” But it’s not very active beyond that, as I understand it. Is that correct?

Jonathan Briggs (45:26):
That’s exactly right. So the activity is in what’s the weight of fixed income versus equities or commodities or whatever you’ve got. That’s their active bet. The big addition for institutional investors in particular, is that they’re looking to increase beyond the asset allocation. They’re looking to increase their returns because they have obligations, they have like Canada Pension Plan Investment Board, that has obligations in the future. So being able to increase beyond just passive exposure or semi passive exposure, to risk premium, provides some extra juice to help cushion the blow that they’re going to have in terms of meeting their obligations.

Jonathan Briggs (46:00):
In fact, when you go to a pure long-short portfolio, you’re not even really, it doesn’t really even matter that you’re in equities or fixed income or commodities, not in the sense that you still need to do securities selection. You have to understand the underlying system that you’re working in, but really you’re not trying to harness the risk premium within those asset classes. You’re really just adding one versus the other. It could be anything. You don’t really care as long as you’re right, as long as you’re getting those bets and the numbers at the table, the numbers of times at the table, is large.

Trey Lockerbie (46:30):
Well, what’s so enticing about this approach is, the only other reference point I have on it that comes to mind is Jim Simons and his fund. And you hear about these outsize returns that are just in 60% plus a year for 20 years. So it’s really enticing and it draws you in.

Trey Lockerbie (46:48):
But as I understand it, that approach also had limitations, or at least they found some sort of optimized scale that said, “Look, we’re only going to run a fund of this size and we’re not letting in any more money.” So talk to us. Is that a limiting factor? Do we hit a ceiling with the approach that you’re describing as well?

Jonathan Briggs (47:04):
Yeah. I think actually any investment approach will hit a ceiling. So there’s only so much you can move around in markets before you move the market. And then you know that you’ve lost whatever edge you’re going to have. So the bigger you get, the less nimble you are in terms of being able to express your positions without people being able to follow you or front run you. So RenTech, they hit a limit. And so they capped the amount of capital that could go in.

Jonathan Briggs (47:31):
To be fair to RenTech, they certainly invested a lot of time, energy and money into building machinery, working with data. It’s very expensive. It’s only natural that at some point they have to close to maintain the returns that they’re trying to and barely compensate themselves for the amount of effort they put into that.

Jonathan Briggs (47:48):
That’s true with that any strategy. Fundamental strategies as well, right? They’ll reach a capacity, albeit if they’re working in large capped space because of liquidity, they’ll reach it later than say a quant who’s potentially operating it at smaller scale, or in smaller stocks than they are exclusively. Does that make sense?

Trey Lockerbie (48:05):
That does make sense. When you’re talking about machine learning, one other question that came to my mind was around the inputs and comparing it or juxtaposing it with the robo-advisor approach. This machine learning approach sounds much more complex and nuanced. And I’m wondering, is it capturing headlines and news on certain companies? Is it incorporating all kinds of this plethora of data from all over the world?

Jonathan Briggs (48:28):
Yes. So, so we’ve taken a ton of data. Look, our costs are huge for computing data, and I’m sure it is with RenTech and Two Sigma. It’s really, and in some ways we could use more data. So we’re always looking for other data sets, because this helps inform our forecasting exercise. And I think you can’t play in this without a ton of resources. As an individual, you could never compete at this scale for these things. It’s an arms race. Perhaps there is an arms race for distance to the exchange, which is high frequency players played in for a long time. And that’s sort of been tapped out. We don’t hear about people building any more microwave towers or maybe they are, but not as many as… The Hummingbird Project, or whatever it is, it’s not the thing anymore.

Jonathan Briggs (49:11):
Prediction is the thing. And that prediction is going to create a new set of constraints and arms race around data and in all likelihood and around talent to do machine learning. And around frameworks. As I said, how do you take all these very powerful things and focus them on the way that has a high probability of success?

Jonathan Briggs (49:27):
We found one way, perhaps others will find others, but that process of understanding is very hard. It takes many years of experience to understand how this plays out. And so it’s an intimidating and daunting thing to think about, if you haven’t sort of grown into it over the course of 15 years of your life to internalize all of this and understand it in this coherence.

Trey Lockerbie (49:47):
Well, what’s coming to my mind is DeepMind’s AlphaGo example. They’re projecting out these probability trees, essentially. And each thread has a certain weight. And then as it proves out one position or the other, it’s learning that and predicting. And I’m guessing this is not that dissimilar as far as the approach where it’s, there’s laying out these probability trees, if that’s the vernacular, but-

Jonathan Briggs (50:12):
Good enough.

Trey Lockerbie (50:13):
Okay. Bear with me.

Jonathan Briggs (50:15):
You’re totally right. So I’ve spent some time talking with DeepMind here and there, and folks who work there and researchers and things. And yes, you’re exactly right. I mean, AlphaGo was actually quite beautiful in many, many ways. Almost exquisite. Good for them. But they were able to harness a concept that we can’t in the world of investing. They can create data, so they can play many, many games. And they create this adversarial relationship between different cohorts. They can create cohorts who play one way and another way and another way. And they can force them to play each other at super high frequency and learn and learn and learn and learn. This is ideal for machine learning and AI.

Jonathan Briggs (50:48):
On the investing side, we just have to sit and watch paint dry. You just have to wait a thousand years for more observations, for more cohorts, particularly if you’re going directly for returns, because the dynamics of returns themselves are so problematic. So you have to find a different, you have to provide structure for machine learning in that context, which we haven’t seen. I don’t want to take anything from DeepMind, but in the context where you’re having limited observations, it’s a very, very hard game.

Jonathan Briggs (51:12):
But you’re exactly right. We are looking at probabilities. We’re not looking at certainties. So that’s how our portfolios evolve, is that we look to the future at various horizons and we say the probabilities are changing at various horizons. So if you could imagine companies being successful in the short-term and not in the long-term and vice versa. And so we’re playing this probabilistic system and letting our ideas flow into portfolios that try to capture these through time. And it’s quite complicated.

Trey Lockerbie (51:39):
Well, to add to the complexity, you also have our own government now creating more and more liquidity to enter the markets in really unforeseen ways. And so I’m wondering, how does the computer accommodate for that sort of outlying? That [crosstalk 00:51:53]-

Jonathan Briggs (51:54):
So for instance, Robinhood. They created a whole different world for retail investors. It’s free trading and lots of access to leverage. But remember those are cohorts. So there already were cohorts. There’s head funds, there are mutual funds, there are ETFs, there are flows in these. Now you just have another one, which is very, very strong. So if you try to imagine there’s a cohort, one cohort, which is we’re simply buying it, but one cohort of retail investors, where would they line up on these expectations of future events? And if you’re better than they are, then obviously they’re arbitraged.

Jonathan Briggs (52:30):
A deeper question is, will retail look more and more like GameStop shenanigans? Or will it look like fundamental investors? Where would they end up and how would they impact prices? Because if they end up in a GameStop sort of situation where fundamentals don’t matter, then it’s problematic for our strategy. It doesn’t mean that it’s impossible for us to work in, but if they dominate the market over all horizons, then that’s a problem.

Jonathan Briggs (52:54):
What I would hypothesize is, what we’ve seen so far, is that’s a short horizon thing again. So this idea that voting machine, weighing machine. We’re seeing a big impact on the voting side of the equation to reach our flow. Will that stay that way? I don’t know. Interesting to watch, but again, they’re a cohort. We can look at what they do.

Trey Lockerbie (53:12):
Now, talk a little bit about your strategy, because now you have created your own firm, Delphia and you’ve implemented the strategy. Is it meeting your expectations?

Jonathan Briggs (53:21):
Yes. It’s meeting our expectations, because we’re seasoned at this. We look at our backtests, we look at our thesis and we say, “This is what we think we should expect out of sample going forward as we invest.” And we’ve met those. And I’ve been quite delighted that we haven’t run into the things that we know are out there. So even when you look at a backtest, there are certain conditions in the world where every strategy is likely to, maybe not at the same time, but every strategy has a weakness.

Jonathan Briggs (53:45):
And the way I look at it’s like, we’ve had a nice nine month run. We know there are bad stakes in the world out there. None of them have shown up in that nine months. We know they’re there. It’s kind of like some horrible venomous spider roaming around. You can step on it at any time, but it hasn’t happened yet. And so we’re quite happy. It’s been a joy to watch that.

Trey Lockerbie (54:03):
You know, I’m tempted to ask you about, as you were entering into the finance space, what books and resources really inspired you. Most people default to like Intelligent Investors, Security Analysis, but with you, I’m like, it’s probably Moneyball or something that like… But what resources have made the biggest impact on you that maybe others can at least grab for themselves?

Jonathan Briggs (54:24):
By the way, the Moneyball is hilarious. So we call this process of trying to understand what moves markets, we call it moneyball, obviously it has a nice ring to it.

Jonathan Briggs (54:32):
Yeah. So, there are two books which I look at as foundational for me and I have my team read them obviously. So one is, Active Portfolio Management by Richard Grinold and Ron Kahn, which doesn’t tell you anything about forecasting the actual returns, but it tells you all about portfolio construction. So assuming you have some forecasts, how do you put portfolios together? And this book was, it was seminal in the field and continues to be the Bible, the rock on which fundamental invest, every quantitative investing is built upon.

Jonathan Briggs (55:03):
The other one, surprisingly is John Cochrane, has a book called Asset Pricing, which talks about, in a very generalized and quite beautiful framework, how anything can be priced, whether it’s a pair of tennis shoes or wine or equities or fixed income insurance. So again, hopefully you can see the connectivity. And that view of the world is very aligned with me now and our team, and our investment style, but in the context again of the Active Portfolio Management book, which is this portfolio construction handbook.

Trey Lockerbie (55:32):
Now, is your strategy only available to institutions, or what does the outside capital look like?

Jonathan Briggs (55:39):
We have two sets of strategies. One set is available to institutional investors. Another strategy has just become recently available for retail. The reason you have to create separate strategies is, the legal requirements for investing with institutions are very different than for retail. And so I’d love to be able to say that we can take exactly what we do for institutional investors and give it to retail. But the law won’t allow you to do that. Perhaps there’s been some evolution in the last year in assets regulations, so perhaps there are some things you can do at closer approximation to what you can do for institutions.

Jonathan Briggs (56:12):
I would say the biggest difference is that one uses leverage that is pure long-short. That seems to be suitable for institutional investors who can handle that kind of approach. And then if you go to the other side, you think about long only or [inaudible 00:56:23] products that are available for retail. But as I said, things are evolving, so perhaps there’s some way to make one available for the other in the US. The retail side is only available in the US. Institutional side is available to anyone global.

Trey Lockerbie (56:38):
Very cool. Well, Jonathan, this has been a real pleasure and really enlightening. I would love to do this again sometime soon. But before I let you go, I definitely want to give you the opportunity to hand off to the listeners where they can learn more about you and your research and Delphia, and anything else you want to share.

Jonathan Briggs (56:54):
We have a website, delphia.com. We’ll be creating an institutional website as well in the near future. There’ll be papers in the… we think, our thought processes, primarily on the institutional side. There’s always some interesting tidbits about our thesis on the retail side.

Jonathan Briggs (57:08):
I would say, just look forward. We’re just getting started. We’re only nine months in. I’ll be talking to folks in different channels shortly in the near future. Thank you for having me. This is very cool. You have a great podcast. You’ve had some really incredible people on here. It’s an honor to spend an hour with you.

Trey Lockerbie (57:23):
Well, I appreciate it. Let’s do it again soon.

Jonathan Briggs (57:24):
All right.

Trey Lockerbie (57:27):
All right, everyone. If you’re loving the show, don’t forget to follow us on your favorite podcast app. And remember that we always love to hear your feedback. You can always reach me on Twitter, @TreyLockerbie. And if you’re just starting out, go ahead and Google TIP Finance. You can find all the courses and resources we’ve built for you there. And with that, we will see you again next time.

Outro (57:44):
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