A (3:21)

Yeah, my background is a little unique when it comes to investing. I come from an engineering family, starting with my grandfather who actually worked on the Manhattan Project. And I always liked math. Growing up, assumed I would be an engineer. Did that throughout my education. Got a job at kind of one of the coolest engineering companies you can work for. Right. Work for GE designing aircraft engines. But it turns out engineering doesn't actually involve as much math as one might think. And so I knew this wasn't what I wanted to do for my entire career. And so I left to go to Harvard Business School and really decide, trying to figure out there what I wanted to do. And that's where I took a lot of investing classes. And that's really what drew my interest to investing. To me, it's a large puzzle that, you know, a large game that's really hard to solve because there's so much noise. And to distinguish between the signal and the noise is, Is very difficult. And I really enjoyed that, that aspect of it. But you know, to your original point of my time there, I learned so much at ge mainly on implementation and operational efficiency. So many people might know Jack Welch, one of the kind of famous ex CEOs, and he was really known for, for their quality initiative, Six Sigma, which essentially means they want every process to pass a six sigma criteria, which means that it fails less than one in every six sigma or it succeeds 99.999% of the time. So there was no tolerance for any kind of mishaps. It really improved operational efficiencies, cut costs, etc. And we were trained on that. I Learned how to implement and do that. And that's. Those are the kind of the lessons I've taken into investing because you can have those same type of operational efficiencies when it comes to implementing evidence based strategies.

B (5:06)

What was the course at Harvard where you were just like, whoa, like, I know this is what I want to do with investing. Like sometimes there's a course that really sticks out.

A (5:15)

Yeah, it was, man, I forget the title, but it was basically investment management. And what they did was, you know, kind of lucky at Harvard that you have access to a lot of the best PMs out there. And so they brought in all the most famous investors to basically give an hour lecture of how they invest, why they invest, why it should work. And you would ask questions, figure out, is this something I would believe in, want to invest with? And so you got to see so many different types of investment strategies. And that was the class that really got me. We actually had one lesson on dimensional fund advisors, which is ended up where I ended up working next. But you got an exposure to so many different types of firms and how they invest.

B (5:57)

Kind of sounds like our podcast trying to bring up, bring people with different investment strategies.

A (6:01)

Honestly, it is very similar. Yeah. And an hour is about our class time and so yeah, it is actually very similar to that.

C (6:09)

I wonder where our invite to Harvard is, Justin, for us to come.

A (6:13)

We're probably, we're probably not the people they're looking to bring in there. I would guess if I had any poll I'd get you guys in, but I don't think I did.

B (6:20)

Speaking of dfa, I mean, DFA is like a juggernaut now. I don't know how many, maybe they manage trillions, I'm not sure. But in terms of evidence based investing. But what would you say how, how did that experience influence sort of how you look at investing?

A (6:34)

Yeah, my time there was, was invaluable. I learned so much. You know, they're kind of the godfather of this space in some ways. I had an absolutely fantastic manager. You know, they really taught me the tools of how to distinguish between signal and noise, how to understand the research, how to communicate ideas. And you know, we really feel like at Longview Research Partners we're standing on the shoulders of firms like DFA and other, other firms that have been in this space for a while and you know, just taking it to the next level, the next evolution. And so yeah, my time at DFA was, was invaluable how if you were.

B (7:11)

You know, talking to someone for the first time. And you were trying to explain evidence based investing in your own words for, let's say a beginning investor, someone that's not, you know, doing this day to day, a professional, you know, how would you describe it? And then where do you think maybe it gets misunderstood in the marketplace from time to time?

A (7:30)

Yeah, you know, I'm a purist when it comes to evidence based investing. So to me, an evidence based structure approach is something that's systematic, meaning rules based, transparent, you know what they're doing and you have a strong confidence that this will work in the future, that small periods of underperformance aren't going to deter you, make you change course, et cetera. I think where a lot of people might misinterpret evidence based investing is they might think of it as well. As long as you have a lot of data and you have a process that's evidence based. And I don't think that that's really true. I think there's a lot of active managers who are moonlighting as evidence based investors by saying we have lots of data, we have a good story and that's why we should outperform. And for me, that's no different than the traditional active managers. And maybe their data is good, maybe they will outperform, they have some alternative data or they start listening to know earnings calls and judge, you know, how many positive words there are, what have you. But ultimately that's very similar to active investing. There's a lot of literature that shows that that doesn't work. It doesn't really cover the fees that you pay to get it. And so I think that's really where the distinction is between evidence based versus maybe active. In that sense, it's good to hear.

C (8:51)

Evidence based investing too, because we're kind of in a world right now where number go up is is evidence based investing to a lot of people. So although there is momentum, I guess.

A (8:58)

But they're not using momentum in the.

C (8:59)

Way you or I would.

A (9:00)

Right.

C (9:02)

So you may have answered this a.

A (9:04)

Little bit already, but we've had Larry.

C (9:05)

Swedger on the podcast and we've had Andy Burkin as well, and they've probably come up with the best definition I've heard of, like what an investing factor is. But we still like to ask anybody who's in the investing world, like, what do you think a factor is? Because there's so many things out there these days that people refer to as a factor. So like, what would you consider a factor to be? And like, what are the most Important characteristics of a factor.

A (9:25)

Yeah, yeah. Jack and I know Andy and Larry very well. And yeah, my definition is gonna be pretty similar to theirs, but maybe I'll shift on and give an analogy. So to help explain what kind of a factor is, if you were, if I came to you and I said, hey, I've got this, this drug, this pill that you can take, and it's gonna extend your life by 10 years, what would you need to convince yourself that that's actually true? Right. The first thing you might ask is, well, is there a sensible reason, Is there, is there a, a link between this pill and what might cause me to live longer? Right. If I said I went to my backyard, got a bunch of dirt, put it in a pill and gave it to you, there's really not a good reason as to why that should make me live longer. But if I said it was full of amino acids, antioxidants, things we know are related to health, okay, there's at least a sensible story there. Then the next question you might ask is, well, have you tested it? Right. This is drug trials. This is how medication works, is have you tested it? And for me to be confident this is going to work, I want to see it tested for a very long period of time with many different groups of people. So if you told me, hey, I found some aboriginal tribe in the Amazon, I gave it to 10 of them and we did one generation test and they all lived an extra 10 years, that's just really not going to be enough to convince me. There's other confounding variables there. But if you said, hey, we've tested it across, you know, all seven continents over multiple generations, and this, you know, everybody who took it lived longer. Now you've got something going there. And so, you know, it's got to have a sensible reason. You got to have data to back it up. And then lastly, it needs to be implementable, it needs to be realistic. And so if I said, yeah, this will make you live longer, but you got to take this pill every 10 minutes for the rest of your life and it costs a thousand dollars a pill, it's also not just, it's not going to be implementable, you're not going to be able to do it. And so when you think about what a good factor is, it's something that's sensible, there's a good story behind it. It's related to evidence and data, and I can actually implement it and do it in the real world. What do you think?

C (11:22)

You mentioned a very long period of time. How do you think about that? Because one of the things I've noticed about people like you and me that are factor investors is we think about that very differently than your average person who thinks three years is a very long period of time. So how do you think about that, what a very long period of time is?

A (11:36)

Yeah, it's, you know, there actually are statistical tools that can help you determine that. Right. This is what a T stat is. It tells you how confident you can be in your data. And if you don't have a lot of data and the data is very noisy, then you need a lot of data to, in order to be, to have any confidence. And so when you, you know, stock markets are extremely noisy, and so you need a lot of data. And so for me, when I think about a lot of data, I'm thinking 50 to 100 years. And I think most people, I get pitch products all the time of, hey, you know, this thing I've been doing for the last three years has worked really well. You should invest in it. And to me, that's just like, you know, somebody says three, ten years, to me, that's a joke. It's not. I don't even consider it at all. You need decades of information before you can say anything with any type of confidence. So when we think about the factors.

C (12:26)

That have met the test you talked about, I mean, most people would argue value and momentum do quality, potentially low volatility. Size is kind of questionable these days. Some people do, some people don't. But how do you think about taking all that? When you're building a fund or you're building a portfolio, how do you think about deciding, like, what to use out of all that stuff?

A (12:43)

Yeah. So, you know, for me at least, you want to use the least number of things that tell you the most information. So, for example, if you're, let's say you're a basketball scout, right. You're a basketball scout for an NBA team. I played a lot of basketball growing up, so a lot of my analogies are going to be probably related to my own life. But, you know, if you're trying to get the best recruits and you might say, well, everybody in the NBA seems to have really big hands, so that is a good sign. You're going to be a good NBA player. But also everybody has really big feet. And so if you have big feet, that's going to be a good sign. And so when I go through my recruiting process, I'm going to look for people with both big hands and big Feet. Well, it turns out that there's no new information there. People who have big hands also have big feet. And so by combining both of those factors, you're not getting any additional information. You just need the one. And so when we think about the factor zoo, how there's 200 documented academic factors, most of them are just the same thing in a different, in a different light. And so there's probably about five factors that actually have meaningful value. And once you incorporate those, each additional one just really doesn't add any value. And all it does is add opaqueness to your process, more turnover, and just then ends up leading to worse results for clients.

C (13:59)

So we'll get into how you do this a little bit later on. But so do you. All those factors I listed, I mean, do you use those in some capacity or do you use a subset of those?

A (14:06)

Yes, yes, we use all those. Right. So for, you know, for us, you know, we think value and profitability or value and quality are the key ones. Those are the ones that you need to work in tandem. But we also use momentum, we use size. There's also other premiums that are, you can call them anomalies, premiums that you want to factor in. So things like high asset growth. So companies that grow their assets, that could be through net issuance, it could. There's a lot of different ways, or companies that are merging for cash, things like that. There's lots of other ways that you can kind of pick up the pennies from an implementation perspective that we factor into our portfolios as well. But in terms of the big boulders, that's going to be value, profitability, size, momentum, the traditional ones.

C (14:53)

And you mentioned you have a lot of small little things you do. I mean, are you a believer in a composite type approach? So when you want to represent value, you've got some people who say I should use a variety of factors to represent value. You've got some people who say I use the price to book or something like that.

A (15:05)

How do you think about that? Yeah, we use price to book and mainly because when you have other factors, you pick other things up. So let's say, for example, you use price to earnings. Well, price to earnings is related to the quality metric. And so you're sort of picking up some quality with that metric. And so we want to isolate value. We think price to book is the best way to do that because it has the lowest turnover. And what we found is adding more variables just ends up increasing the turnover in your strategy, but doesn't actually increase your Explanatory power of returns. And so we just stick to price to book for our metric. So we don't really use composites.

C (15:42)

So going back to your whole evidence based approach, one of the things I think about a lot is we've been in a very different kind of market for a while and we've been in a market that's dominated by large cap tech names. And you know, for people like you and me who look at 100 years of data, that's not usually what works over the long term. So I was just wondering, like, how do you think about that, like as a factor investor, as you're looking at that happening, like how do you think about what evidence says about what's going on in the market and this domination of large cap growth tech stocks?

A (16:06)

Yeah, this is always the kind of competing narrative of stories versus evidence. And the stories are extremely enticing. Right. It's these companies that seem to can do no wrong. They just keep going up in value, they have all of these resources and so it feels like they're just going to take over the world. The thing about markets that people need to understand is they do a great job of incorporating that information. Everything I just said, people know that's already in the price. What you have to ask yourself is do you think they're going to grow more than people expect already or less? And when you look at the data, what you see is that all of those large companies they had to get, they got there somehow and that was through outstanding performance. You don't become one of the biggest companies by underperforming. And when you look at the data over time, those large companies outperformed. But once they became those largest companies, their future returns were below the market. Why would that be the case? Well, they have a large gap growth tilt. If you think risk and return are related to those companies are less risky. So investors don't demand as high of a return and so their future returns are lower. It's exactly what you would expect, exactly what you see in the data. And so for us, you know, you have all these large tech names. You know, for us we think they have lower expected returns. We want to tilt away from those. They could always keep going up. But we think on a relative basis they have lower returns. And you know, you have to be worried about concentration and that kind of stuff. But that's why we always encourage investors. If you're globally diversified, you include small cap stocks. You know, it's not a huge portion of your portfolio that would be encompassing These stocks.

C (17:46)

Yeah, that brings up an issue I think a lot about which is this idea of mean reversion. So you know, if you look at history, companies with very high growth rates mean revert. Companies with very high margins mean revert as well. And I think, I think that's always going to be true. But we may have seen some less of that than we saw in the historical data with some of these like mag 7 type stocks. So it makes me think about, and you're probably a good person to ask because you look at this from an evidence based framework like as something changed in the world where maybe these big companies can grow at higher rates for longer periods than they had than they had been able to in the past.

A (18:17)

Right. It's. So the funny thing is it's certainly possible that they could grow at higher rates if things have changed, which maybe things have due to kind of the globalization of the economy. But again, investors know that and they would factor that into the price. So if companies can grow more and for longer and faster, people know that and that's incorporate into prices. You know, that gets down to this. Like anytime something isn't working, right. So we have all this evidence that value works and then we have this period of time where it doesn't. People think something's broken and they want to fix it. They want something that works all the time. And if something worked all the time, you wouldn't be paid to invest in it. And so there's going to be periods when value underperforms. If it always outperformed, there would be no value premium. And so there's going to be times where you have to suffer through that. And you know, as long as you can stay committed to that, you'll end up in the long run coming out ahead.

B (19:08)

And I think there's one little nuance here with your specific portfolio within the ETF that's worth pointing out. It's not like you don't have exposure to those companies. It's just the tilts aren't as heavy as like a market cap weighted. So just for our audience that's listening to this, it's not like the way that you actually run money. It's not like you avoid these. There's still exposure to these large stocks. It's just at a lesser. So I'll let you kind of explain that.

A (19:34)

No, just. You're absolutely right. Yeah, I was just kind of talking about our philosophy in general. But when you talk about how you want to invest and how our fund invests. Yeah, absolutely. We're going to hold every single stock, right? We, you know, starting with market cap weights is where you want to start, and then you're going to deviate from that if you have good reason. And so for us, we think good reasons are trying to pursue higher returns. And so all of those large mega cap names, they're going to be in the portfolio, they're just going to be at a little bit less of a weight than they would be in a market cap weighted portfolio because they have lower expected returns. You mentioned the struggles of value.

C (20:07)

How do you put that into context? It's obviously been a very long period, but anybody who studies history will show there's been other very long periods. But one of the things I always struggle with is how would you know? Corey Hofsten wrote an interesting paper called Factor Fimble Winter, and what he was trying to look at is how long statistically would value have to struggle before you could actually say it doesn't work anymore. And it's an exceptionally, exceptionally long period of time. And so it's sort of a challenge for those of us that use value, like I do to say, all right, people say the world has changed every time this thing struggles, but maybe someday the world will change and we have to adjust, like, based on evidence, what we're doing. So how do you think about, like that balance between those two things?

A (20:47)

Yeah, exactly. Yeah. So you use another analogy here. So imagine I gave you a coin, right? And I said, you know, determine if this coin is a normal coin where it's just 50, 50 heads, tails, or it's a weighted coin where 60% of the time it flips heads. And the only way you can test this is if you just keep flipping the coin. And that's like value versus growth. And you flip the coin for 90 years. And in that 90 year period, you got a lot more heads than tails. And you can use statistical tools to say, okay, there's about, based on that data, there's about a 97% chance that's a weighted coin, right? Because you got so many heads, there's still a 3% chance you just got lucky that you, you flipped more heads, right? Then after that piece, you then flip the coin another 20 times and you got a few more tails than you did heads. And the question you have to ask yourself is what, you know, what can I say with that data? How many more times would I have to flip tails in the future? And to throw out that 90 years of data that I have to convince me that, no, this actually is just a Weighted average coin. And when you get into that research you talked about, it's a long time. And that's because we have a very long period of this working of you flipping that coin and it basically showing up heads more than tails. And then you got a short period where you got a few more tails than heads. And that is totally expected based on the, you know, the history of research we have. The same thing happens with stocks. You know, nobody questions the equity premium. Everybody knows, yes, stocks are riskier than bonds, they should outperform. But we've gone through decades where stocks have underperformed bonds, and nobody questions that. Although I guess in the seventies they did right. They said the equity premium was dead. And so these types of narratives always come around whenever these things don't work again, people just want it to work all the time. And if there was no risk, there would be no return. How do you think about the idea.

C (22:39)

That value has to be redefined? You mentioned you use the price to book before. And as you know, there are some people who say the price to book, you know, in the world we're in now with high intangible assets, with, with companies, I mean, intangibles are a much bigger portion of the economy than they used to be. Price to book or value in general doesn't work as well, or it has to be redefined. Like, how do you think about that?

A (22:58)

Yeah. So again, this goes back to the same narrative of this wasn't working. We got fix it. And companies with a lot more intangibles have done well recently. And so people are saying, oh, well, let's factor, you know, factor in, factor out intangibles, and then that'll fix the value premium. Now, if you think about where that value premium comes from, when you talk about price to book, companies have lots of different types of assets. All of these assets have value. They might have a plant, and that plant produces cash flows by making widgets, or they might have a patent that allows them to do something. So you have tangible and intangible assets. And if you remove intangible assets from a company's book value, you're removing information. We know that that asset exists, and now you're removing it. And so we don't think that's really a smart way to go to create a better value proxy. Now, on the opposite side, you know, you have two different types of intangibles. You have externally developed and internally, meaning internally is things I did myself. So we invested in R and D and developed these patents ourselves. And then externally, we bought it. We bought another company. Well, those companies you buy, those are on your balance sheet because you have to keep track of it with goodwill. If it's internally developed, it may not. Right. And so we said removing those intangibles doesn't make sense. That's removing information. You don't want to remove information from what you're doing. But what you could do is you could try to capitalize R and D. You could say, okay, all this research we've done, that has value, let's put that on the balance sheet. Well, when you do that, it's just really, really noisy. It's really hard to determine what that value actually is of that R and D. And sometimes you overestimate and sometimes you underestimate. Also, these premiums work in conjunction. So when you capitalize that R and D, that now changes your profitability of that firm and the historical profitability or quality premium goes down. And so all of these things work in tandem. And making any adjustments to intangibles, when you look at the data just really doesn't come out and say anything meaningful about, about returns. It has something meaningful to say about the last five or 10 years. And so people think that, that they extrapolate that. But when you look at the full data set, you just don't see that. And, you know, even when, you know, we talk about value a lot, you know, value has worked outside of the US Just hasn't worked in the US and so when you take a global context, you know, people really focus on one country. Well, we've got 45 other countries where, where it's worked, and you don't need to make those adjustments. And so I think it's just people trying to solve problems that, that really don't exist.

C (25:31)

So as part of your argument here, you think intangibles are very hard to value. So in other words, like, obviously a drug company is investing in R and D. That might be a different thing than what Google's doing. And there's a lot of variation there. Is that part of what you're saying?

A (25:41)

Oh, yeah, absolutely. Yeah, you'd have no idea. And, you know, there's people who've done this test, right? So you can look at companies that were acquired and you can say, how much did that company buy their intangibles for? Right, because whatever their book value is, whatever they paid for that delta is the extra value they paid for their intangibles. And then you could look at, okay, let's capitalize that company's R and D over the last 10, 20 years, and you compare what that was worth in the marketplace to what you capitalized and you find no, no statistically significant relationship that sometimes people pay way less than with yourself. All those R D expenditures, sometimes they pay way more. And so it's just really hard to determine what the value of that is. And so trying to do it just adds noise and no signal.

B (26:26)

I have two more on value just before we move. I don't know if we're moving off of it or not, but.

A (26:30)

Yeah.

B (26:33)

What do you think of this idea? Like in value stocks outperformance I've seen with different types of strategies, like amazing performance coming off of like bear market lows, like at the, you know, the trough of the bear market where you get this explosive performance. You know, you have stocks trading at, you know, price to books below 1 PE is less than 10. And, you know, as we come out of that and things aren't maybe as bad as the world thinks, you know, you get like, really good returns from value. So, you know, but what I, what I sort of struggle with the two things are we were at least in the last, like 20 to 30 years we've seen, you know, it's less and less bear markets than we have maybe historically. So economic expansions are being extended longer. Plus you have the Federal Reserve Reserve coming in and maybe influencing markets like they haven't in 50 years previous to that. So that's kind of the first sort of question I'm getting at. And then I'm trying to reconcile that with like the fama French, like paper on migration that show that much of the value outperformance comes from value stocks migrating to like, maybe the neutral and maybe even the growth category. And that seems like a much smoother. And I haven't looked at how the return sequencing is in that paper, but to me that would be like a more smoother sort of outperformance of value. So there's a lot of stuff baked into that question. I'm just wondering your thoughts on any of it or all of it.

A (28:00)

Yeah, so. So it ultimately comes down to the relationship between kind of risk and return and how investors incorporate all information into prices. And so when you say, where are we at in the economic cycle? And value might do better in bear markets or bull markets or when interest rates change in one way or another. And when you look at the data and evidence, you really find no relationship there. And that's exactly what you would expect. It's not that interest rates don't Affect the performance of value companies, they obviously will. But that gets incorporated into prices immediately. And so once that interest rates come comes out, there's no new information then. And so all of that is already captured. And so there's really no way to from that perspective to time the value premium in that way of trying to, you know, it performs better in bear or bull markets or whatever. You can find anecdotes, right? You can pick periods of time when value did really well or interest rates change and the value premium upticked, but then you can find anecdotes that show the opposite. And what you have to do is look at 100 years worth of data across all 50 countries and look at where they were in the economic cycle, look at the future value premiums, and what you find is really no relationship. What you do find is your point of migration, that stocks move around, prices are always changing, investors are always updating what the company's worth. And investors demand different returns for different stocks and then they demand those returns at different times. And so stocks are constantly moving around the kind of growth value landscape. And what you want to do is invest the ones that are value. And then prices change. Some of those companies, they go up in price, they become neutral growth companies and different companies become value companies. And you got to rotate into those. And it's really that rotation, that turnover that allows you to capture that value premium.

B (29:49)

You kind of answered my next question, but I just want to ask it anyways because. And it has to do with interest rates. Like I'm wondering, and I agree with you that, that this information gets priced in. But I wonder, like with from 08 to like 2022, where we had this like artificially historically low interest rate environment, I wonder where maybe investors got it wrong was how long rates were going to stay low. And therefore that kind of helped growth versus value. I don't know if that has any.

A (30:21)

Oh, oh. So what actually happens certainly impacts performance, right? So the fact that interest rates stayed low for longer maybe allowed growth companies to grow for longer than people expected. But you know, hindsight's 20 20, right? You don't know what interest rates are going to do. And you have yield curves, you have what the market thinks is going to happen. And so what ended up happening was a risk was realized. There's some probability that interest rates are going to stay low for a really long time. And it's kind of like flipping a coin. It's, you have a 50% chance of heads or tails if you flip it once. You never get 50% heads. Right? You either get heads or tails. And so prices are set based on some probability. It turns out interest rates stayed longer than people expected, which was good for growth stocks. But you can't predict that ahead of time. There's no way to know that. There are people who think they can read the tea leaves, and those are active investors and they can try to do that. When you see over the long run, they struggle to do that consistently. But yeah, obviously what happens certainly impacts performance. It's about before the fact. Do you know that? No. And so you have to position yourself based on what you do know to try to maximize returns.

C (31:33)

So one of the logical things that can flow for some people out of this underperformance of value is maybe when it's down, I want to add exposure to it. And I know you've looked at cliff asness work around sin a little, and this is something like I struggle with because I think that makes complete sense and it's something I actually do personally. But I also know it's very, very hard to time it. So, so how do you think about that, that idea of factor timing? And can you add exposure to a factor like value when it's out of favor?

A (31:57)

Yeah. Yeah. So, okay, going to go back to the same framework of risk and return. The reason why value stocks outperform is because investors demand a higher return to hold those because they're, they're riskier. And so a sort of traditional factor timing approach where you go from value to growth should never work. Right. Value stocks are always priced to have a higher expected return. And so if you ever try to shift, you're going to come out behind. But that doesn't mean that the value premium is constant throughout time. The risk or the return investors demand for taking that value. Risk changes over time. Sometimes they demand a lot of return, sometimes they demand a little bit of return. And so when you look at valuation spreads, meaning let's take the growth half of the market and look at their value metrics and the value half of the market look at their value metrics. Sometimes they're really close. Growth companies look like value companies. Sometimes they're really far apart. And you can say, well, what does the value premium look like in those two environments? Well, the value premium is always positive. We already mentioned that. But when spreads are tight, the value premium is a little bit smaller. And when spreads are wide, it's a little bit bigger. And so one approach you could take is you can say, all right, I'm paid more for taking Value risk during certain times and I'm paid less during other times. And I understand that I'm paid more because there's more risk. And if you want to increase your returns when spreads are wider, you can say I'm more comfortable taking more tracking error and I'm going to lean in a little bit. And when spreads are tight, I'm a little bit less comfortable taking tracking or I don't want to take as much tracking error because I'm not paid for it. And so I'm going to do a little bit less. This type of sin, a little type of, as you mentioned, cliff assness, that type of thing works. You get a little bit more return. Now you have to keep in mind things can always get worse. Spreads get wide, they can always get wider. And so this is not a free lunch. You're taking more risk, but it's a way to increase returns by using, using what you know about valuation spreads to capture a little bit more of the value premium when you're paid for it.

C (34:06)

Do you have any feelings on like if momentum helps with that? So, so in other words, if spreads are wide, if I wait for value to start to come back a little bit, can I, can I do better by waiting for that momentum to be in place?

A (34:16)

So you certainly want to factor momentum into any decisions that you make. I actually don't know of any research that I'm sure there is something exists about the momentum premium with sort of valuation timing specifically. But it's certainly the case that stocks that have done well recently tend to continue to do well and stocks that have done poorly tend to continue to do poorly. And you want to factor that into your process. You know that's, you know that exists on the sector level, on a stock level. And so if you were to do this type of thing, yes, you'd want to factor in momentum. What I would caveat is momentum acts over a very short period of time. You're talking a matter of months. And these spreads can be really wide or really tight for years. And so they act on different time frames. And so you'd want to use momentum to inform you, but they act over different periods of time. And so it wouldn't have a, using momentum in conjunction with that wouldn't have a huge impact.

B (35:10)

Just going back to the actual implementation of this. So is this like, is this like an yearly review of where value spreads are and how, and how does it flow like to the actual positions in the.

A (35:23)

Yeah, yeah. So again, you don't want to be making any huge Adjustments really quickly because that incurs a lot of trading costs. You want to do this slowly over time. So it's something that we continually look at every single day. But again, spreads don't change that much over short periods of time. And so, you know, maybe every quarter you might want to relook at it and it might be a change. And the way we approach it, there's many different ways to approach it. The way we approach it is we look at the historical data and when spreads are in sort of above one standard deviation of its historical average, so call it like a third of the time. When the spreads are in their most 1/3 times time wide, we lean in a little bit more, we add a couple percentage points of weight more to value, and when you're in the bottom third, you lean a little bit less. And so we're talking about a couple percentage points. We're not talking about huge moves here where you're going to go from 10% value to 50% value. We just want to lean in a little bit and try to capture a little bit more of that return.

C (36:20)

Do you have any thoughts when you look at combining factors? Do you have any thoughts about what we call one approach, the sleeve approach, and one, the consensus approach? But some people will say, I'm going to use all my factors, I'm going to put them together, I'm going to weight them to create a portfolio of stocks. And other people will say, I've got my 10 value stocks, I got my 10 momentum stocks, I'm going to have them on an island, and then I'm going to put them together. Do you have any thoughts on those different approaches and how you do it?

A (36:41)

Yeah, to me, you really need to combine them. And it comes down to kind of one of the core tenets of our firm in terms of solving for expected returns. So if you think about back to school, right, when you know you valued companies using the dividend discount model, you said this company is, you take the future profits, discount it to today, that's what it's worth. And the typical active manager, they look at, you know, they assume some future profits, they assume some discount rate, and they say, is this stock over undervalued? But we know that that discount rate that you're using, that is your expected return as an investor. And so we want to solve for that. That's what we care about. And so we trust price, we trust markets, there's so much information in that price. And we trust the price and we solve for the discount rate. Now, when you do the math on that. You basically get quality over value. You get a ratio. And so what's important is the ratio of those two things. And if you look at factors in isolation, you lose a little bit of that. And, you know, we don't pursue factors because factors are better. We pursue factors because they're highly correlated with expected returns, which is what we care about. And so when you look at them in conjunction, you get a more pure exposure to what you care about, which is higher expected returns. And so in our portfolio, we solve for, for that expected return for investors. And the only way to do that is to look at value and profitability in conjunction. Right. You don't want value stocks. There's some value stocks are value stocks for a reason. They don't make a lot of money. There's some high, really profitable stocks that are really expensive for a reason because they're so profitable. What you want is the company that has a lot of profits, has a lot of cash flows, but also doesn't cost very much. And you got to look at those things in conjunction.

B (38:32)

Is there anything that you do on the negative screening side where you're trying to avoid like the very word to your point? You know, some of these value stocks are actually value traps. I know you guys are kind of buying the full market, but is there anything being done to screen out like the really bad companies or is it just like we're gonna get. We're just gonna have let. They're gonna score lower, certainly on the profitability. So the.

A (38:52)

Yeah, so they are going to score lower. We at the kind of extreme ends, we do exclude it. Right. So for us, the bottom call it 5% of the market. The ones that just look the worst, those are mainly small cap companies that have huge growth prospects and no profits. Think like a healthcare company. It's got a new drug, hasn't been approved. People think it's going to be, you know, the next big thing, but it has no profits. Those types of lottery stocks tend to underperform. And so we exclude those from the portfolio. And so, you know, when you think about a, you know, a portfolio of U.S. stocks, like 3,000 stocks, there's a few hundred that are in that bottom 5% of those just lowest return stuff that we do exclude and have no weight in. When you think about our overall exposure.

C (39:39)

How do you think about using momentum? I've seen momentum used in a lot of different ways. I mean, some people use it as a key portion of their process. It's combined with all the other Factors like in their composite score. And I believe DFA does it the other way. You can correct me if I'm wrong, but I believe DFA used it more in the buy and sell decision. Like, you know, my factor system says I should buy something. Momentum can kind of tell me when to buy it or when to sell it. Like how do you think through that?

A (40:00)

Yeah, we're, we're, we're probably in between those two things where we're probably closer to the DFA approach when it comes to it. You know, when you think about momentum, it has an extraordinary amount of turnover. Things like the value and quality premiums, they have about 25% turnover a year. Meaning if a company becomes a value company, it's about a value company for about four years and then it, and then it migrates Momentum, you're talking 2,300% turnover in a year, meaning it's a, it's a momentum, it's high momentum for a couple of months. And so that becomes very difficult to capture. And especially when you're a large firm because the amount of turnover you'd have to do and the amount of assets you have, you just couldn't do it. And so we, we use it in our sort of trading screening process where pretty much only going to buy up momentum names and we're pretty much only going to sell down momentum names. So we're able to kind of pursue those pretty aggressively, but within the constructs of going after size, value and profitability. Right. So if you want more value profitability names, let's buy the ones that are up momentum. If you want to sell the large growth ones, let's sell the ones that are down momentum. So you're capturing that momentum premium, having a meaningful tilt, but you're not churning the portfolio and having 200% turnover to try to get it.

B (41:17)

Let's just go back to something you said because I haven't ever heard this and maybe I've missed it and all the, I'm sure it's, it's out there because you said it. But you know that four year reversion of value. Can you just kind of shake that.

A (41:30)

Out a little bit?

B (41:31)

So you're saying that you know when a stock migrates from, from value to like out of value, like it's like a four on average, like a four year migration.

A (41:43)

Yeah, four or five years. Yeah. And I think it's important to know that that value is a relative metric. Right. It's not, it's kind of, you could say it's kind of like if you wanted to line people up by based on their height, it's not like value is saying everybody under five foot is, is value or something. It's saying no matter what room you're in, you could be in all Scandinavians, everybody's over six foot. There's always somebody that's shorter and somebody that's taller. Right? And the same thing with stock prices and markets. There's always some companies that are value and some that are growth. And prices constantly change. And so stocks are constantly moving around. And so when a stock becomes a value stock, sometimes it's a value stock for a day and it goes back to growth because new information comes out and price goes up. Sometimes it's a value stock for decades, right? There are, you know, there are funds that have small value funds that have been around since the 1980s and they hold the same stock the entire time. The average is about, you know, five years. And stocks sometimes go to growth and then come back and you have that migration. So it's really that level of turnover that you need to have in order to continually capture the value premium. And you can see this in indices, right? You can take a Vanguard small cap value index and you can just see when it rebalances once a year or four times a year, however many, often it does it what their turnover is and it's in that, you know, 20% range.

B (43:02)

Moving on to the size premium, I mean, there's been, as Jack was just kind of mentioning, there's been a lot of debate around whether or not that premium sort of still exists in the market. So, and that means like smaller caps, you know, they, they're riskier, so they should reward investors with, you know, the expected return should be higher. That's the argument. But know, some have looked at the data and said, you know, small cap premium hasn't kind of existed for the last 20 or 30 years or something like that. So what, what do you think about that?

A (43:31)

Yeah, so the size one, I will. That's a tricky one. Right. So we mentioned before that these, these factors interact, right? That you want to do value and profitability together, but they also interact with size. And if you just look at small caps in isolation, they don't outperform large caps. But when you look at it in conjunction with some of the other factors, they do. And the reason for that is there is a subset of small cap companies, we kind of hinted at this, that look really, really bad from a value and quality standpoint. And if you remove those, then you do see a small cap premium that those small caps outperform the remaining large cap cap names. And so the main reason for that is just there are a lot more small cap names and you just see more extremes. Everything just seems to be more extreme. Within small caps you have a larger opportunity set. And so you do see a small cap premium, but only if you do it in conjunction with value and quality.

C (44:29)

One of the interesting things we were talking about before is that this idea of size, like the size of the firm in terms of implementing factor portfolios, because obviously we've got some massive, massive firms that are implementing factor investing and then we've got some smaller firms. And so how do you think about that? Like, is there an advantage to being a smaller player in the space in terms of your ability to move in and out of positions and your ability to harvest these factor premiums?

A (44:53)

Yeah, we think so. And that's what we've seen in the data that we've looked at. And the magnitude of that benefit of being nimble really depends on what we're talking about. So again, I'm going to use my background as analogy. You could think about the market as, you know, as a plane and the market is huge. There are a lot of the market cap of the whole market is big. So you can think of it like an A380, just a massive plane. And so these, there's a lot of room, there's a lot of seats. And so these asset managers can fit a lot of assets into this plane. But there is a limit and there's regulatory limits. You can only own a certain percentage of each company. There's filing requirements. Now some of these companies are willing to make those filing requirements, like Vanguard, you know, they own 10 to 15% of every single company. There are other big firms that, you know, invest in the space that aren't willing to file. And so investors with those firms may not be able to get the full exposure to some of those, some of those securities. The requirements of how much you can own also change with industry and sub industry. Gambling companies, insurance companies, they limit how much you can own even more. And so you, these large companies don't have a full exposure necessarily to, to some of these sub industries. So that's one thing. But so the kind of the size of the, of the room, size of the plane, you also have another thing of the size of the door of the plane. And so if you think about whenever you fly, you land that A380 at Heathrow Airport and you've got to deboard the plane it can take a very long time to deboard that plane because the door is only so big. And sometimes you don't really care if you get off the plane very quickly. Right? You're just trying to get home for dinner. And maybe if you're the last one out of the plane, it's fine, you're the last one on the cab line or you, your last one to get an Uber and it takes you an extra 20 minutes, whatever to get home. So you suffer some cost, but it's not a huge cost. But sometimes you got a train to catch or a meeting to go to and the fact that you have to wait 30 minutes to get off this plane has a huge cost to you. And that's the same with investing is when new data comes out, right? So if you're pursuing, let's say, value, profitability, that type of thing, those things last over years. And so if it takes you a couple months to get into a value or profitability stock, you're still able to capture those premiums. Not that well, right? Because it takes you six months, but you just lost six months of a four year premium. But there are other things that act over much shorter periods of time like momentum or things like high asset growth investment. You know, take net issuance. A company issues stock and typically that company has lower returns in the future. That only lasts about six months to a year. If you know you're on that plane and you're at the back of the plane and it takes you six months to divest from that stock, you didn't capture that premium you were trying to capture. And this is the benefit of being small and nimble is that when some of these shorter term drivers, these other things that we do to pick up the pennies, we're able to get in and out of these names extremely quickly without incurring a lot of costs. If you're an index fund, you know, you're opening up the exit doors, you're trying to do anything you can to buy everything on one day, right? And you're going to incur a lot of trading costs. The alternative is to say, you know, we're just going to go, be patient, we're going to slowly get off this plane just through the front door. And for those investors, they're suffering an opportunity cost or it's going to take them longer. And sometimes the opportunity cost is small, sometimes it's really big. And we just avoid that by being small and nimble and, and we're able to get in and out of those Securities.

C (48:22)

I would think this is particularly an issue when you think about something like small cap value. I mean, is that right?

A (48:28)

Yeah, exactly right. So the size of that door changes based on where you are in the market. So if you're going after large cap companies, whether you're a Vanguard or another big firm, there's so many assets there, there's tons of liquidity. You can get in and out without too much problem. When you talk about small value stocks, the liquidity there is pretty low. And it can take a very long time to get in and out of these stocks. Or if you trade all at once, it can be really punitive from a cost perspective. And you have to think about it not at the fund level. You know, my fund only has a billion in assets. That's not too bad. You have to think of it at the fund family level, because all of those funds within one fund family are going after the same stocks and they're competing for that same limited liquidity. And when, let's say, like that net issuance example, a company issues a bunch of stock, all of your funds want to sell that stock now because it has lower expected returns. Well, which strategy gets to sell it first? You can either sell them from all strategies on that day and you can crush the price, or you can do it slowly over time. And then you have to decide, okay, who gets to go first, who gets to go second. And you know that, that process, each firm handles that in a different way. But let's just say you had one fund that held 100 shares and one fund that held 10 shares. You might say, well, that's a fair way. You can't treat any fund unfairly. So you might say the fair way is just whoever has the most gets to go first. Right. And we'll use that as our order of operations. Well, when you do that, you bias larger funds, right? Because I have a larger fund, I probably have more shares. So that's not ideal. You could say, okay, well, then let's negate that. Let's do it as a percentage of assets. Whoever has the most percent of their fund in this name, they get to go first. Well, when you do that, you favor more concentrated portfolios, right? So if you only invest in small value, each small value company is going to be a bigger portion of your portfolio than if you invest in the total market. And so no matter what methodology you pick of who gets to go first, second, you're biased in something. And investors should understand when you invest with these large fund families, those are the inherent Biases, those are the inherent lags, the inherent opportunity costs that you're going to suffer. And that's really why we launched Longview Research Partners was the fund families we were using, we saw that cost, we saw, hey, this stock, we don't want anymore. And I saw the fund that I was invested in slowly sell it over six months and I didn't want that exposure, I want it gone immediately. And so we felt like we could do it better in a more nimble way. And that's really a lot of the implementation value we add is being much quicker to trade on these things and be able to more efficiently get exposure and capture these premiums.

B (51:09)

Yeah, I mean, that's the one. There's so many benefits of, you know, ETFs, but the one thing is, is, you know, most of them are transparent. And so when you start moving out of a position, I mean, sometimes if it's a long, you know, sales cycle, like you're saying, like it could be, you know, hedge funds or high frequency trade or whoever might sniff that out.

A (51:30)

They absolutely could. Right. So if they knew how you typically invested and they saw you start, you know, exiting one of those things, they could. Now if, if you trade a very small percentage of daily volume, you can try to hide that as much as you can and not have a large impact, which I think is what most firms do. I think that's the smart thing, right? You start trading in big volumes, the hedge funds can take advantage, but again, the less volume you trade each day, the longer it'll take to get out of it. And the more volume you trade, the more you're going to push prices, the more hedge funds are going to take advantage of you. And so it's a double edged sword and, you know, there's really no good answer there. And every large firm faces that trade off. Some large firms do it better than others. But as an investor, you don't have to make that trade off. You don't have to choose to invest with one of the big guys. You can avoid those conflicts altogether by investing with a smaller firm.

B (52:21)

As we get toward the end here, I want to ask you a couple questions just about maybe the future of investing and some of the developments we're seeing. The first thing is how do you think artificial intelligence, some of these, you know, large language models, machine learning, does that, how does that play into sort of the role of factor investing? I mean, do you see, where do you see that maybe having the most impact?

A (52:48)

To me, it has the most impact on, you know, to me, AI is a great tool. It's not an oracle. And so I'd be very worried if you were using AI to try to figure out what the next factor premium is or overfitting the data to say that this will work in the future. Academics have been looking at historical data for decades and have found about one thing a decade that works. And when you have AI and you're looking at thousands of signals using thousands of different weightings, you're going to find something that randomly works by luck. And it really, you know, concerns me that you might have people investing in certain ways thinking that this is has the same level of evidence behind it. But when you test enough things, something's going to work. I think there's the old adage that like, you know, the amount of rainfall in Uganda determined who won the super bowl or something. Right. You said these things that you have enough data, you can find something that proves something else, even though there's no correlation there. So we use it as a very strong tool. We're able to develop tools to help us invest way quicker than we could before. In my prior work, it would take years of development work to develop some new tool that helps us invest or tackle some area that we're trying to do. And now we can code up a tool within weeks that does exactly what we want to do. And so it makes a smaller team much more efficient and makes them able to do much more complex things. But I would never use it to determine what I should and shouldn't buy.

B (54:15)

Along these lines. I mean, what do you think? What areas of investing do you think there's probably the biggest opportunity to see meaningfully a meaningful improvement over time here?

A (54:32)

I think, you know, we met, I kind of had mentioned before, you know, the research out there, research is pretty much free, right? Everybody kind of knows what drives returns, and implementation, I think, is really what's key. You're not really going to be able to generate much alpha from better research. I think you can generate a lot from how you implement portfolios. And we talked about some of the ways we, you know, we do that. But I think there's a lot more innovation in that space that's going to be upcoming ETFs allow you to do a lot of different things and I think mainly around things like tax efficiency and cash drag efficiency where I think you can create new products. And these are things that we're working on in the lab to really improve after tax returns for clients. And, you know, those are things that are just straight implementation Alpha things that people can take home to the bank that will make a meaningful difference in people's lives, both in, you know, financial planning as well as investment.

B (55:30)

So we like to ask all of our guests two standard closing questions. And the first one is, what's the one thing you believe about investing that most of your peers would disagree with you with?

A (55:39)

Yeah, I would say, you know, to me there's a big fascination with uncorrelated assets. And I think the, you know, the traditional call it 60, 40 portfolio is a fantastic portfolio. It is unbelievably diversified. It is low cost, liquid tax efficient. And to get me to move money out of that into something else, you need to have a really compelling case. And just saying something is uncorrelated to me is not a compelling enough case. And we see a lot of different strategies out there, whether it's market neutral strategies, hedge funds, etc. Where they say, you know, this has different performance than what you have invested in today and that's good for you. But you know, the first thing you really need with uncorrelated assets is that it expands your opportunity set. If there's some market neutral firm that's going long, some stocks short, others. Well, I already have beta exposure in my portfolio. So whenever stocks you're short, I'm probably long in my portfolio. And so now I'm just paying a lot of fees to have a net zero exposure to that name. Ultimately there's only, call it 3,000 companies in the US 3,000 streams of cash flow. And you have to ask yourself as an investor, what do I want my net weight to be in each of those names? And then what product do I use to most tax efficiently, most cost efficiently, get access to those. And combining a bunch of uncorrelated things. When you probably look under the hood and combine all those net weights, you could probably get the same net exposure in a much cheaper, more tax efficient way.

B (57:17)

And then the last question is, based on your experience in the markets, what's the one lesson you would teach your average investor?

A (57:25)

I would say if it sounds too good to be true, it is. There's a lot of investments out there that, you know, are just changing payoff diagrams, you know, with structured products, or are promising you the world. And if that's the case, it's probably too good to be true. You know, just staying invested in a low cost, diversified solution, sticking to the path, you're going to come out way ahead than the people who are constantly trying to change and hit home runs. A lot of singles get you ahead of the guy swinging for homers.

B (57:56)

Great conversation Matt, thank you very much. We look forward to following the fund and hopefully the success of the fund and really appreciate your time. Thank you.

A (58:04)

Justin Jack Thanks a lot. Appreciate it.

B (58:06)

Thank you for tuning in to this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the Excess returns network@excessreturnspod.com if you have any feedback or questions, you can contact us@excessreturnspodmail.com no information on this podcast.

A (58:27)

Should be construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.