Today I am speaking with Jack Vogel, co-CIO of boutique ETF issuer Alpha Architect.
I’ve known Jack for some time now and was particularly excited to bring him on the show for two reasons. The first, which you will quickly learn in the episode, is his near encyclopedic knowledge of investing literature. I’ve met few investors who have both the breadth and depth of recall that he does for both academic and practitioner studies.
The second was because he helps manage a momentum strategy.
Almost every investor has, at one time or another, at least perused the pages of Graham’s Intelligent Investor and value investing is considered by most to be as wholesome as Warren Buffett drinking a Coca-Cola while eating apple pie.
Momentum, on the other hand, is often disregarded as performance chasing nonsense, with little foundation in the realm of real investing. Yet, as you’ll find in our conversation, deep care and thought goes into both understanding the anomaly itself and constructing a portfolio that can efficiently attempt to capture it.
I hope you enjoy my conversation with Jack Vogel.
Corey Hoffstein 00:03
Hello and welcome, everyone. I’m Corey Hoffstein. And this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.
Corey Hoffstein Is the co founder and chief investment officer of newfound research due to industry regulations. He will not discuss any of newfound researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of newfound research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions and securities discussed in this podcast for more information is it think newfound.com.
Corey Hoffstein 00:46
Today I am speaking with Jack Vogle, co CIO of boutique ETF issuer Alpha architect. I’ve known jack for some time now, and was particularly excited to bring him on the show for two reasons. The first, which you will quickly learn in the episode is his near encyclopedic knowledge of investing literature. I’ve met few investors who have both the breadth and depth of recall that he does for both academic and practitioner studies. The second was because he helps manage a momentum strategy. Almost every investor has, at one time or another, at least peruse the pages of Graham’s intelligent investor. And value investing is considered by most to be as wholesome as Warren Buffett drinking a Coca Cola while eating apple pie. Momentum, on the other hand, is often disregarded as performance chasing nonsense with little foundation in the realm of real investing. Yet, as you’ll find in our conversation, deep care and thought goes into both understanding the anomaly itself and constructing a portfolio that can efficiently attempt to capture it. I hope you enjoy my conversation with Jack Vogel. Jack, thank you for joining me today.
Jack Vogel 02:07
Of course, thanks for having me
Corey Hoffstein 02:08
on. So why don’t we just start with the obvious and the easiest? Why don’t you walk us a bit through your background and how you got interested in the industry and how you ended up at alpha ARCHITEC?
Jack Vogel 02:19
Yes, so definitely a roundabout way into finance. So as an undergrad, I was a math and math education major. So technically a certified High School pa math teacher. And then coming out of undergrad, I was still super interested in mathematics. So I applied and got into Drexels, PhD in math program. And so you know, the original intent there was get a PhD in math, and then obviously, potentially teach college or just see what happens there. And you know, during my second year, I kind of became not as interested in getting a PhD in math, just didn’t want to do that for the rest of my life. And one of the professors at Drexel was actually attempting to start a financial mathematics major. And he made up two classes, one on fixed income, one on derivative securities. And I basically became super interested in finance from those classes. And then from there, I was teaching the, in the math department at Drexel for one year, but I was like, hey, I’ll just get a master’s in finance, because I really liked us. And basically Drexel kind of approached me and was like, Hey, would you be interested in getting a PhD in finance, and that’s how I went down the PhD rail. And then from there, you know, when you’re when you’re a PhD student, you generally work with a professor. And my second year, West came to Drexel University from Chicago. And I started working with Wes, basically, as his research assistant. And, you know, kind of, as things evolved over time, the alpha architect, under its numerous names before brand kind of grew. And I was like, hey, you know, I want to do this as opposed to teaching. So just kept working with Wes. And then when I graduated, I went full time working with Wes at alpha architect.
Corey Hoffstein 04:18
So I want to I want to touch on your Ph. D. program for a minute. My experience is that people who hold a PhD, typically a lot of their program involves a very dedicated research curriculum, both research that they have to perform themselves as well as reading a large number of studies. And I wanted to get your thoughts on the current, what’s, I guess, being called the replication crisis, right, where a large number of these scientific studies, behavioral studies, and arguably even a lot of the studies into quantitative finance are now being discredited as either not being statistically significant anymore. poorly designed studies. and it’s causing this crisis of confidence across the industry. I wanted to get your thoughts as you look back on a lot of the research you’ve read, and as you read research today, do you take a more skeptical view towards a lot of the research published today?
Jack Vogel 05:15
I think so you are highlighting that a lot of you know, there’s replicating anomalies paper, there’s actually numerous papers now kind of saying, looking back and trying to re replicate all of these strategies that were published in top tier journals, and therefore, you know, notified as anomalous, right. And so I think, in defense of the academic community here, I think they’re kind of getting a bad rap, because some people from the outside are like, hey, these academics are just like data mining, like, what are they doing here? Should we even trust them? I don’t think that’s necessarily what they’re doing. Clearly, there probably is, in some instances, data mining, and you know, and then you fit a story with some data, but, but really, you know, academics are trying to better explain kind of what happened in the past and stock returns. And they’re not even trying to say, hey, out of sample these should work. Right. So let me give you like, an example of something. You know, that’s kind of was anomalous, right? And now, it’s not, right. So so a good ones, kind of like, you know, the industry customer supplier links, right. And there’s cool paper, you know, showing that basically, you know, in the past, this was like before, you know, the 2000s, basically, in the 1990s. And before, you know, if, let’s say, I think the example was like Callaway and its suppliers. Right. So, if Callaway clearly was like having a good return, you should then be able to be like, Okay, well, Callaway is doing well, its suppliers should be doing well, right. And there was there was this like, anomalous long short relationship there, right? And the author’s just highlighted that this existed in the past, but they don’t say out of sample is going to work. And what do you notice? Well, out of sample, hedge funds came up with this, trading became easier, and all of a sudden, that anomaly went away. So was it nefarious when they were writing that paper, trying to highlight like a cool thing that happened in the past in the prices of securities? No. What I would say, though, is that going forward, you know, you want to make sure you understand why an anomaly exist, and whether or not it’s easily arbitrage. And if it is, such as that like customer supplier relationship, you know, it’s probably not going to work too well out of sample because there’s a lot of, you know, high frequency hedge fund type shops that are going to take advantage of that.
Corey Hoffstein 07:44
So maybe we can talk about that. Why for a minute, because so much of the quantitative finance space is based on this idea that we are evidence based to the best of our ability that we’re looking back at historical returns. And we’re trying to identify robust anomalies. But again, pointing out the fact that this is an observational science, we can’t run repeated trials. And to the best of our efforts, we can look for evidence in different geographies, time horizons and asset classes to support our hypothesis. At the end of the day, we are all scouring the same data. And so there are the risks of data mining. And so very often the proposition is Well, that’s all necessary, but not sufficient that you do need a why. And to me often that why an anomaly exists, is somewhat hypothetical, it’s not necessarily proven out in the data that we’re speculating. And it does, to a certain degree contain a faith based element that it’ll continue going forward that we have this explanation we believe in, and we believe it’ll continue going forward. I was wondering if you could touch upon that a bit. How do you think about the balance of data versus the qualitative? Why as to how an anomaly exists? And what changes your mind as to whether the Y is sufficient for you to use an anomaly going forward?
Jack Vogel 09:16
Yeah, so I think, you know, I’ve kind of covered almost full circle on this right. And full circle, meaning, you know, when you start anything in finance, one of the firt, especially, you know, PhD program, one of the first things you learn is Hey, markets are efficient, right? And what does that mean? That means that people aren’t idiots, on average, you know, you’re gonna see that if you invest in a particular type of securities, and you earn expect or hope to earn a higher return than a another set of securities, you should be implicitly taking on some sort of an additional risk right. Now, I think one of the things that’s hard to say is hey, what is Is that risk, right? It’s that’s never really defined. It’s just says, you know, in the efficient market hypothesis is that if you get higher returns, you must have invested in a more risky investment, right. And so then, you know, obviously, as shown in the literature, you know, there’s like value size anomalies that appear, right, and fama and French would say, Hey, these are just riskier investments. But then there’s some very valid behavioral reasons that potentially people are overreacting to news for value under reacting to news for momentum, right? And so you’re like, hey, well, maybe it’s behavioral. And maybe people are just idiots, and these things will work forever in the future. And I think it’s a combination of both, there probably should be some behavioral error that people are for some reason, systematically making. But the additional aspect is that for something really to work in the future, it has to have some sort of an additional risk. And so again, I’ve kind of come full circle on that. I think markets are somewhat efficient, not perfectly efficient. I think most people would agree with that. And so no, like value is an example where potentially people are overreacting to news causing prices to go lower. But also, value stocks may just literally be inherently more risky, right? So we use Enterprise multiple right now, that’s consumer discretionary. There’s like a legit risk that Amazon is going to put every one of those consumer discretionary companies out of business. So what I’d say is, I think going forward, if you’re trying to do any sort of factor investing, you kind of want to know why. And the Why should be at some level, it should be a little bit riskier, or there should be some systematic behavioral error that people are making now and that you expect them to continue to make in the future.
Corey Hoffstein 11:53
So we’re going to spend a lot of time talking about momentum today. And momentum is one of those anomalies that very often, the reason that gets a scribe to its existence is a behavioral error, that there is investor anchoring that causes initial under reaction, potentially an argument about rational inattention of investors and then eventually, once momentum takes hold that there’s a hurting and an overreaction effect. But I very rarely hear a risk based argument. For momentum, it’s almost always a behavioral argument. With you coming somewhat full circle back to the idea that there should be a risk based argument for an anomaly going forward. Where do you think the risk based argument wise for momentum?
Jack Vogel 12:45
Well, so again, coming full circle, I think it is got to be one of the two risks or behavior, right. So obviously, you’re correct in the behavior generally, is talked about, like an under reaction right. Now, as far as risk, right, like, why would you know, momentum securities be riskier? Well, I mean, let’s remember, actually, what the academic momentum finding is, right? That the academic finding is that high momentum firms minus low momentum firms have a statistically significant like alpha, right, if you don’t control for that factor, right. And so when you add that in, you basically better explain the cross section and stock terms. So and also, which is found similar to that research piece you pointed out today, is that the short book for momentum tends to actually generate probably a bigger portion of the long short portfolio returns. So in theory, if you’re saying that, you know, momentum is a long, short portfolio, and the short book, which are the extreme losers are generating a lot of the excess returns to that long, short portfolio, it’s probably the case that companies that are down a high percentage, let’s just say your average loser portfolio is down 70%. I’m just making that up. Right. But let’s say that’s the case. They’re probably riskier. I would agree with that. So trying to short them. Basically, there is risk embedded in that long, short anomaly.
Corey Hoffstein 14:20
So since we’ve started going down the rabbit hole, why don’t we just dive all in on momentum here, you guys run one of the very few momentum strategies in the marketplace, your quantitative momentum index? And maybe I’ll just ask the obvious question, why aren’t there more momentum strategies available to investors?
Jack Vogel 14:43
I mean, probably a lot of it’s just driven by the way people have historically categorized strategies into different boxes or buckets of a portfolio. Not sure exactly. You know why that happened. So You know, if you go on like Morningstar style box, or Vanguard or iShares, right, all those websites are going to show you generally, two dimensions, they’re going to show you size, right, so small to large, and then value to growth right along those two dimensions. And so, you know, a lot of people will try to pick it to the extent you’re not just picking the market, you know, they want to allocate somewhere along those two dimensions. So momentum to some people, I think, is thought of as a growth strategy. You know, there is not a massive actually overlap with growth. But I think for some people, since, you know, the way buckets have been created, this is just my experience, I think people, you know, kind of seem to bucket people into value or growth. And unless you want to add a third dimension, then now all of a sudden, people are trying to figure out where you fit on the three dimensional style box. Yeah, I think that’s probably the reason, just the original way, people were assessing funds along the value growth spectrum.
Corey Hoffstein 16:07
So let’s stay along this this vein of thinking of momentum versus growth, our friends over at O’Shaughnessy Asset Management recently published a piece called factors from scratch, I know you’ve read it, it looks at breaking down the returns of different factor strategies into the components of fundamental growth and revaluation that occurs. And what they end up finding for the momentum factor is that a really significant portion of the total return can actually be explained by accelerating earnings growth in the underlying securities that are held by the portfolio. And they go so far as to call momentum growth done right. So, to me, it seems like there is an argument that momentum might actually be a growth strategy. Now, they also point out that after about a year, that growth rate tapers off in the portfolio. And there’s a there’s a reversion back to the market rate. And there might be an anomaly explanation here, where the markets extrapolating in error at the growth rate of the securities going forward. But it does seem to me like there is the potential for some overlap of momentum versus growth. And it sounds like you’re arguing that you actually don’t think there is significant overlap. So I was wondering if you could draw apart the distinction for me?
Jack Vogel 17:32
Yeah, I mean, so what I would say about that, is that clearly, I mean, I would never just allocate a portion of my portfolio probably to a pure growth strategy. So to the extent they’re saying hey, momentum is a better way to gain access towards a growth type strategy. That makes sense. And again, I think just nuanced data definition and again, that is actually a great piece by the O’Shaughnessy guys recommend everyone read that. But so what they find in there especially on momentum, right, is actually you know, something that was in one a Robert Novy Marx his paper, no fondle, fundamentally, momentum is fundamental momentum. Right. And so, what Novy Marx finds as well as Shaughnessy guys find is that, you know, at some level, the fact that momentum returns can be explained by like fundamental momentum, meaning an earnings type of momentum. And so, at some level, you know, if you’re defining growth as the growth of earnings. Well, yeah, I mean, that makes sense. Because momentum, we already know, just from the literature, that it’s been shown that that growth rates and earnings are highly related to price momentum strategies, right. So Robert Novy Marx was paper will look at that. And basically now in his paper, he will say, hey, earnings momentum is better than price momentum. And we discussed that in our book. But you know why we still use price momentum. But at a high level, it actually gives you a little bit more confidence that, you know, momentum is not just this hole in the wall idea. You’re just picking stocks with high price returns, it’s actually you’re picking stocks with high price returns. That’s what we do. But fundamentally behind that, a reason that, you know, these winter socks on a price basis are doing well, it’s because they have good earnings momentum. And one of the reasons that these a loser stocks on a price basis have low momentum is because their earnings are going down. So, you know, to the extent we’re talking about growth from an earnings perspective, yeah, momentum is a decent way to gain access to a growth type strategy.
Corey Hoffstein 19:45
So you bring up the Novy Marx paper that talks all about fundamental momentum and actually argues that price momentum is subsumed by fundamental momentum. More recently, you’ve had the Q factor model start to gain some popularity 30 that argues that momentum is actually subsumed by their factors of return on equity and profitability, I believe. How do you look at establishing your confidence in continuing to use something like price momentum in your investment process in the face of contradictory pressures?
Jack Vogel 20:26
Well, let’s first just maybe talk about the Novy Marx paper a little bit more. So on the Novy Marx paper, what he tests specifically, is he looks at, you know, a price momentum strategy, which I think everyone understands. But if not, it’s pretty simple. You just look at stocks based on their past, you know, one year return exclude last month, and you know, you go long a winner bucket, you go short, a loser bucket. So high level, that’s the momentum factor. And again, it’s always in the context of a long, short portfolio. And then he compares that to what he calls fundamental momentum, right. And he measures fundamental momentum two ways. One is through looking at, you know, earnings announcement returns around earnings statements, and you look at the cumulative abnormal return, you sorted stocks into the those with the highest cumulative average, normal returns that your winner bucket, you short, the ones with the lowest, similarly does the same with, you know, standardize unexpected earnings, which is an older measure in the accounting literature. And what he finds is, you know, when you examine these long, short portfolios, you’re right, the fundamental momentum strategy subsumes price momentum. So like, okay, Jack Welch, clearly, like, this is what it says in the paper, I’m not going to say he’s wrong. So why do you still use price for metal? Well, there’s, there’s two reasons. So one, is the fact that in his paper, what he does is, and this is kind of standard academic paper, he basically says, Hey, I’m gonna Vall adjust the strategies to make them the same, right. And so what, you know, Vala, just, let’s just say you want to have the same standard deviation of the portfolio returns, well, what happens actually, is price momentum, happens to have a volatility around, let’s say, 18 19%, right, standard deviation of returns, the long short portfolio, your earnings momentum portfolios, which again, remember are looking at earnings, which are fundamental. So in general, if you can imagine, as the economy goes up, or goes down, your high and low should still gonna go up and down. Whereas place can be all over the place. Now, the standard deviation on these price portfolios is literally 6%. And so what that means is you either have to take the price momentum Long, short portfolio, and cut it in third, or you need to 3x lever, the long book of the fundamental portfolio and 3x lever, the short book of the fundamental portfolio. And as you know, leverage is a double edged sword. So if you do the 3x one, yes, it’s still it will subsume price momentum, but you also will go bankrupt, right. And if you do a third, well, that’s fine. But now you got two thirds of your bucket that you’re assuming is in cash. So that’s one reason that that it’s good to know that, you know, price momentum is kind of driven by fundamentals. But at the same time, I think that’s examined in a long, short context. And if you asked me right now and said, Jack, I want you to run run me a long short momentum portfolio, I would actually probably do it with fundamental momentum. So Novy Marx, his findings in there are good. And I wouldn’t disagree with anything that he says in there. I would just say, when building an investment product, I don’t necessarily have to follow that because that’s not the goal of my portfolio.
Corey Hoffstein 24:01
So let’s maybe take a step back and actually talk about the quantitative momentum index for a moment. And maybe you can give us a 30,000 foot view of how you think of the actual construction of the index and the process you employ to perform your security selection and your rebalancing.
Jack Vogel 24:20
Yeah, so Allah does, I’ll just go through the process and then you know, we can dig into each of the steps. So first step is kind of just assess what’s our investable universe. So you know for investable universe, it’s mainly made large caps in the US or international securities. The second step is, as I just mentioned, we are fans of price momentum, not fundamental, there’s nothing wrong with fundamental we use price momentum. One of the reasons we use this just getting back that Novy Marx paper is actually because a price momentum strategy has a lower correlation to a value strategy. In a fundamental momentum strategy, so we use price momentum to get down to the top 10%, top decile of securities we use past past year returns price basis, or total return, excluding last month. So in step one, let’s just do simple math, we start with 1000 securities. Step two, we go to 100. Step three, what we do is, we use what’s called our frog in the pan measure. And this measure is similar to like a low vol measure, and we take the top 50 or so stocks on this measure. So we go from 1000, and step one to 100 and step two, to 50 in step three, and then in Step four, what we do is we try to time our rebalances, around some what are called seasonal effects in the momentum anomaly. And so we’ve rebalanced right before quarter ending months. So we were rebalanced our portfolios every quarter. And then the last step is, you know, we equal weight across securities. And so those are the five steps, you know, you end up with a 50 stock portfolio that gets rebalanced every quarter.
Corey Hoffstein 26:13
So let me start with maybe somewhat obvious question, which is a very common critique of momentum, is that the anomaly just cannot survive. Real world trading costs, particularly after taxes. So what makes you think that you can run this strategy such that after transaction costs and after taxes, you still have the opportunity to capture some of the potential available Alpha?
Jack Vogel 26:44
Yes, that’s a great question. And it’s one that anyone who wants to do factor investing should probably understand. And so what I’d say is, there’s two questions here. One is taxes, one are trading costs. So first, on taxes, what one of the main reasons, you know, we preferred the ETF wrapper relative to a mutual fund wrapper is the fact that it is generally more tax efficient than a mutual fund. So, for example, you can just we’ll talk about other people’s momentum ETFs, to stay high into the right, so you can examine like MTU M or PDP, other other momentum ETFs are out there that are not ours. And if you notice, you know, they have rebalanced every quarter every six months in a general upward market, and you see that they have zero capital gain distribution. So on the tax front, the momentum wrapper definitely helps. And that’s why we did that. Now, getting into the transaction cost analysis, definitely true. That, you know, the more you trade, the higher the transaction costs will be. Which means the higher the hurdle, the original, you know, alpha, or expected return needs to be above the market. And so, there’s a couple of papers on this. I would say there’s two strands of literature that attempt to examine whether or not factor investing including momentum, exceeds trading costs. One is the microstructure literature. And this would be going back in time, you know, the Sokka, paper Novy Marx paper. So, there’s a couple papers lesbian shills owl, right. And so these papers basically make assumptions on using what’s called the tack data from academic sources on how much it cost to trade. And and and basically, there are a high level takeaway from the academics is, hey, these things don’t survive after trading costs. And then you have two other papers. One is by AQR. One is by BlackRock. So people who are, you know, highly skeptical will say, Hey, these are clearly conflicted people, right? And I’d argue, hey, at a high level, neither those firms would ever want to put the reputation at stake and give out bad information. But what they find is they say, Hey, using our data, our trading data, which is how an actual institutional investor or fun company would trade, we find that they work. So clearly, you get this divergence in the microstructure literature that academics say it doesn’t work. Practitioners say it does. And I think a cool finding, really is that if you examine it, this isn’t the AQR paper, they say, Hey, let’s just say how much it cost the s&p 500. And they use the academic data, the way that their model works, and according to the academics, no, it would cost at least 60 basis points to trade the s&p 500. That means literally for Vanguard to buy s&p 500 stocks, according to the academics, it’s 60 pips, whereas the practitioners have it as at like six pips, right? And then you ask Vanguard and iShares and they’re like, Yeah, it’s like 810 pips, right. So Clearly the academics model looks to be misspecified on a multiple of 10 times what the actual practitioners are doing. So, you know, I argue, you know, I wrote a long piece on it, I think people should read it to make sure you understand both sides of the story, but according the microstructure literature is definitely a divergence. And then similarly, people have examined it from another way, which is trying to infer trading cost. And I would say those papers clearly, once again, say it doesn’t work, I have a pretty good rebuttal that I haven’t seen anyone be able to actually say, I’m wrong, that it doesn’t work. Because there’s some massive assumptions that are being made in this inferred trading costs literature, basically, that everyone’s running a perfect factor portfolio, which literally no one’s doing. And when you make some real world assumptions, like there’s some closet indexing, or in the past, potentially managers switch strategies, but that finding goes away. So I would say that the factor anomalies should work after trading costs. But what I would say is that there’s clearly a size limit, right? If if you gave us like Warren Buffett with multiple 100, billions can’t run a momentum strategy. That is a true fact. So I just recommend everyone kind of read about it. And you know, we I tried to outline both sides of it, we still think it works. So is high level takeaway.
Corey Hoffstein 31:31
So I want to touch on that size limit component because it’s it is somewhat obvious that every strategy has to have some size limit, at which point it’ll start to impact the market and erode its own potential Alpha. But you also mentioned the fact that nobody really runs pure factor strategies. If you look at the marketplace of the very limited momentum offerings out there, you have some indices that are using a blend of different momentum measures, you have some that are taking into account fundamental momentum scores, you have some that are taking into account things like short interest, earnings surprise, some that are using residual momentum, you guys use the very standard academic 12 Minus one, but then employ things like frog in the pan, which we’ll we’ll get to, as well as some seasonality in your rebalancing. So with that in mind, what do you think the limits are, for momentum in the marketplace, for it to continue to be and potentially offer alpha?
Jack Vogel 32:37
So I mean, there’s definitely a limit 100%. Now, I think one thing that people always forget is that they tend to examine and try to like, come up with an answer in a silo, right? So you’re like, Hey, Jack, what’s the what’s the limit for momentum? And it’s like, okay, well, as of right now, you know, I can kind of examine and, you know, if you look at the practitioner data, right, you know, they say it’s like, 50 plus billion. And so you’d be like, Okay, well, that that makes sense. But, you know, implicitly, there’s also other strategies that are potentially doing non momentum strategies, right. And so, to the extent, you know, other people are kind of canceling it out. That does have an effect. Now, what I’d say is right now, it’s probably, you know, depending on the way you form, the portfolio, each one will have a different answer before you or trade trading cost will then potentially get higher and, you know, have an effect. Unfortunately, there’s no great, perfect answer to that. Because it’s going to adjust also over time, like in five years, if the markets double my answer right now, would it be would have, you’d have to double that, right? Where if people are for some reason implicitly, decided to move against the the momentum strategy, maybe you have a little bit more capacity? So unfortunately, I don’t know if I can give you like a perfect number. Perfect answer. What I’d say though, is that it’s probably in depending on who you believe, practitioners or academics, you get massive variance in what the capacity is, right? Like if we look at the Blackrock paper, right, if they’re basically like saying, Hey, we can do momentum, but we can trade over five days like that our capacity is literally 324 billion. So that’s pretty big.
Corey Hoffstein 34:39
I just want to say that all the paper mentions that you’ve had so far is going to make compiling the show notes for this episode, a very painful experience for
Jack Vogel 34:47
me. I will remember them and pass them all along to
Corey Hoffstein 34:51
I appreciate that. So I want to return back to something you mentioned really off the cuff earlier, but it was about your use of price momentum. And you mentioned that one of the reasons you like price momentum is that it offers a lower correlation to a strategy like value. And this is actually in line with the conversation I’ve had with you in the past, as well as with your co CIO, Wes, in talking about sector constraints within a momentum strategy. And so there’s a lot of new literature in the last decade about the role of applying different constraints within a factor portfolio. And the evidence seems to suggest, at least as of today, that when you look at something like momentum, taking an unconstrained approach tends to maximize your compound annualized growth rate. But if you constrain yourself to be sector neutral to the overall market, you tend to maximize your information ratio. And so there was a paper from Hartford, that I just recently read that suggested that you give up about 5% of your total return opportunity, but decrease your volatility by about 20%. You’re tracking error when you go sector constrained. But I’ve heard you argue in the past is a long winded way of getting to my question, I’ve heard you argue in the past that that benefit really isn’t as large when you consider it in the context of a portfolio sort of on the efficient frontier, that if you go sector unconstrained and consider an unconstrained strategy in the context of other things in the portfolio, a lot of that tends to cancel out and or is actually beneficial diversification. So I was wondering if you could expand upon that idea a bit for me,
Jack Vogel 36:41
there’s probably two things in there. One is yeah, we use price momentum, because as I mentioned earlier, fundamental momentum, not surprisingly, since its fundamental, has a higher correlation to value investing, which happens to be fundamental based, right. So that’s why we use price momentum. And then you’re right, as, as we’ve discussed in the past, we prefer unconstrained because what that kind of does is from a portfolio context, no value and momentum. In general, people look at that, you know, long, short portfolio correlations between those two factors, and you find that they’re negatively correlated, again, in the long, short context. But but even long, only, they have a much lower correlation than just the broad market. And so we prefer to just allow the unconstrained momentum to work. And yeah, potentially, you’re taking on some additional sector bets. But for us, you know, I think it washes out from a portfolio context. And one one side note that I would like to make about all the that one paper you mentioned, is just you always have to remember that things are like the momentum anomaly even in that that Harford piece, which actually is very educational and good. Not criticizing at all. But you know, it is in the context of a long, short basis, right. Whereas from a long only momentum perspective, your results may be slightly different. So I
Corey Hoffstein 38:17
want to talk about this frog in the pan concept, because this is pretty unique. I haven’t seen it anywhere else utilized by anyone else who is running a momentum strategy, I was hoping you could explain what frog in the pan is and why you use it as a component of your investment process.
Jack Vogel 38:39
So frog in the pan is a measure and you’re like, a frog in the pan. Where did you guys come up with that? And why would you know, why would this be in a product or an index? Right? And so the original paper was, you know, Frog in the pan. And so the whole idea, though, was this as a paper got published in review of financial studies, which is, you know, one of the top three academic finance journals. And what they were doing in the paper was they were trying to figure out if they could better explain the momentum anomaly, right? And how they did that was they wanted to look at whether or not people were potentially you know, I would say, under reacting to certain information, right? And so how they measure and why this frog in the pan come in, so frog in the pan comes in, as people know, so you know, the story is if you put a frog into a boiling hot pan of water, the frog will just jump right out, right? Whereas if you put the frog into a you know, cold cup of water on a stove and then slowly heat it up, the frog will probably just unfortunately stay in there until it’s unfortunate death. And so, neat title for a paper definitely can achieved, but the results are also cool. Know, what they find is that basically, they examine two types of momentum. So I’ll give you the example, you’ve probably heard me give it a million times, but it’s good for the listeners. So imagine you have two types of firms with high momentum. One is I’ll call it boring, big box, right and boring big box is up 100%. So it’s classified as a high momentum firm. And how it got its momentum is it was up 50 basis points for 200, straight days, right. Whereas we have another firm, and it’s called exciting biotech. And exciting biotech was bouncing around at zero, you know, going up and down every day. And then two months ago, he got FDA approval, first new drug. And on that day, it went up 100%. So both these firms have 100% momentum, yet they’ve achieved their high momentum in completely different ways. Right, boring, big box was kind of slow moving, grinding up, exciting biotech had very fundamental in your face news come out. And so what the author’s hypothesized is that the momentum premium, both on the long and short leg is probably driven by these boring big box type stores, where people are kind of under reacting to information that slowly dripping out about the firm. Whereas the firms that have you know, these kind of in your face news events, people may, you know, accurately price them when the news comes out. And so the way they measured it is just, you know, the percentage of positive or negative trading days over the past year. And so for your long book, you want to have firms that have a higher percentage of positive IE, these are ones that are probably high momentum, but the momentum is like a slower grind. And what the papers find is that basically, the majority, if not all of the momentum anomaly is driven by these boring big box type stores, momentum firms. And so we use that to filter down our top 100 into our top 50. And that’s the measure that we use in our index.
Corey Hoffstein 42:11
So as part of the story here, part of the argument is this concept of almost rational inattention that investors at the end of the day are very limited, they have a limited amount of time with which to absorb new information. And so they tend to under absorb, you know, sort of small, continuous increases versus a large discrete in increase in information, they can absorb very rapidly and adjust their expectations. And so, perhaps for the sudden one day jumps in price, there isn’t a momentum effect, because the market prices them more efficiently versus the small, continuous drips of information tend to go under appreciated.
Jack Vogel 42:53
Yeah, they’re they’re definitely the background. And that paper is they’re making a behavioral argument that investors are kind of systematically under reacting to certain types of news. And, you know, I think that’s found there’s other ways one can measure or attempt to judge, you know, this measure. There’s papers in the past that showed that low trading volume was associated with, which is kind of similar to this as well as lower volatility. And those will kind of get to the same idea that people are potentially under reacting to news for certain types of momentum firms.
Corey Hoffstein 43:31
So I want to touch on low volatility, maybe take a bit of a tangent here for a second, I know, it’s something you guys have written about quite a bit. I don’t want to put words in your mouth, but you seem to consider it somewhat of a second tier anomaly. But it strikes me that frog in the pan, from a measurement perspective might have a lot in common with low volatility, it almost seems like your screen in many ways could be processed as a high level momentum screen with a secondary low volatility screen. Do you think that’s a fair comparison? Or do you think frog in the pan really introduces a unique measure?
Jack Vogel 44:08
I would say that’s a pretty fair comparison, right? Because of the high level frog in the pan, what is it going to do is it’s going to pick firms that have a higher percentage of positive days, positive stock return days. So there’s probably going to be, you know, a very high correlation to if one just ex-ante through a low vol filter on there. I would agree with that.
Corey Hoffstein 44:32
So keeping on the same line of thought here, sort of secondary ways of which people try to measure momentum. A lot of firms and a lot of academics have written about the benefits of adjusting total return for volatility, using a risk adjusted measure, controlling for beta looking at some sort of idiosyncratic measure of momentum or something sort of residual momentum component and one of the arguments is just quite simply that if you don’t adjust for relative volatility or beta, a lot of your higher risk securities is riskier being measured by volatility will end up in the top and bottom deciles of momentum simply due to their the magnitude of their volatility frog in the pan strikes me not as necessarily trying to solve the same problem that risk adjusted and beta adjusting are trying to solve for necessarily, do you think that they are overlapping concepts? Or do you think that they are independent concepts that can actually be used in harmony?
Jack Vogel 45:44
I would say they’re probably related, there’s probably a benefit. There’s definitely some like orthogonal information amongst those two measures, you know, within low vol and momentum, relative to Frog in the Panama momentum. So potentially one could, you know, use both of them. But I don’t think I disagree, because there’s definitely going to be some additional information there. But I think the two are probably highly related. A lot
Corey Hoffstein 46:13
of firms that do implement momentum tend to use multiple signals. So instead of just using for example, 12 minus one total return, they might use a measure of short, medium and long term momentum, they might use price momentum, as well as fundamental momentum, with one of the arguments being that they are diversifying their signal. I know, in past conversations with you, you have stressed the importance of keeping it simple on purpose, I was wondering if you could provide some color on that why you guys do just use 12 Minus one momentum, why you don’t incorporate multiple signals? And why you think that’s actually beneficial for investors?
Jack Vogel 46:56
Great question. So, you know, at a high level, you know, we do incorporate, I would say, multiple measures, you know, because we do have a, our price momentum filter, then the frog in the pan measure, and then, you know, our rebalance frequency, which we may talk about in a little bit. But you know, what, why don’t we use, you know, three 612 month momentum, what I’d say is that at a high level, there could be a marginal benefit there, I wouldn’t art like vehemently disagree, that adding in other price momentum, variables would, you know, hurt or help. And I think that’s the thing, I don’t know if it would hurt, I don’t know if it would help. One thing that’s a kind of nice benefit is, you know, we know kind of that price momentum, especially intermediate relative to value has a negative correlation, right? From a long shore perspective. So we’re one looking at Portfolio benefits of the two. And then the second thing is, you know, as I mentioned, I don’t know if it’s necessarily going to help or hurt out of sample. But the third is probably most important is we do try to keep it at some level, simple, because really, for all these anomalies, as I mentioned, way back in the beginning our conversation, you know, there has to be some sort of investor behavior that potentially is causing, you know, certain strategy to work, or the things have to be riskier, and that are going to require, you know, a decent time horizon potentially to realize said benefit of a strategy. So, to the extent it’s simpler, and people can understand it, I think that at the end may be more beneficial to the end investor.
Corey Hoffstein 48:35
I think you guys use a great line there that, that sustainable Alpha requires a sustainable investor, right? Yeah. So the signal part, I guess, is only part of the equation, though I use this really hokey analogy that building a portfolio is a lot like cooking, you have your ingredients, and then you have your recipe. And as an industry, we spend a lot of time talking about the ingredients. Do you use 12 Minus one momentum? Do you use fundamental momentum, all the things that go into it, but there is the transformation of that information that is necessary to ultimately create a portfolio. And again, this is an area where a lot of firms will deviate in process. So some firms might take that momentum information and just tilt their market cap weights. Others might choose the top 33% And wait, based on the strength of the signal, some stay sector neutral, some don’t. And we touched upon that in a prior question. Talk to me how you think about the actual portfolio construction? I know you use an equal weight methodology. Why did you guys elect equal weight versus the infinite number of other variations that you could consider?
Jack Vogel 49:49
That’s a great question. So you know, how did we get into the final end solution? So I would say, there’s probably you know, let me go through three things. So, one is, and actually this was found in that, you know, Hartford hemco paper that you mentioned. One is the fact that, you know, momentum historically has generally worked better in deciles relative to quintiles ie more extremes, right. And so well, one of the takeaways from that is, you know, and how we x and he wanted to build our portfolio is, we wanted to potentially have a lot more concentration to the momentum factor. So, you know, at the outset, you know, that’s why we go directly to the top decile. And then within there, pick what we think are the top 40 to 50 names. And so by doing that, we are, you know, giving more concentration to this factor, I would say, relative to most others in the industry. And so that was one thing that we definitely wanted to focus on was the concentration aspect. That is like a point of differentiation, I believe. The second thing and you kind of were asking, you know, why do we equally well, you know, one of the pros and cons of concentration is you get a concentrated portfolio, let’s say it’s, you know, 40 stocks or 50 stocks, well, now market cap weighting becomes potentially, you potentially could have an issue where, let’s say, you run the screens, and we have 45, mid cap stocks. And then we throw in Apple, Google and Amazon, just picking the biggest names I can think of maybe a Microsoft, right, so we got 35, mid caps, and like for stocks that are massive, well, if we market cap, wait, well, now we’re all the sudden with with no restrictions, we’re now basically a fund that holds for stocks and for 35, mid caps, right. And for Sacher and it’s completely drive the portfolio. So equal weighting is good from two perspectives, what it allows us to do is, you know, take a, you know, equal bet across the 50 names, 40 or 50 names that we invest in, as well as, so you get some, you know, so when equal weighting, you get, I would say some sort of risk control embedded. And then the second aspect is, to the extent you think that size has any effect at all, it will generally tilt you towards more mid caps relative to just other funds that are using market cap weighting.
Corey Hoffstein 52:20
So to stick with my cooking analogy here for a moment, and at the risk of using a horrible pun that’ll make everyone turn off this podcast immediately. You’re not afraid to sprinkle a little seasoning on the dish, you’d like to use the seasonality effects you actually you and Wes introduced me to seasonality made me take a little more seriously. I’m still a seasonality skeptic, but the literature is quite convincing on the topic. I was hoping you could talk a little bit about seasonality What convinced you it was an important part of your investment process? And how you go about utilizing it in the construction of the portfolio?
Jack Vogel 53:00
Yeah, so I recommend everyone listening reads Korea’s piece on seasonality, which was really good found it found it pretty good. Now, how do we get into seasonality in the context of momentum? And why does it matter? So when I was working with West, like, as a PhD student, we actually were kind of like, hey, you know, I wonder if like momentum is driven by like flows in certain quarters, because as many people know, institutions, as well as mutual funds need to disclose their holdings at the end of every quarter. And there is a separate set of literature that looks at kind of how how managers will do what’s called window dressing, right? And what they’ll do, what the research has found is that managers will essentially at the end of the quarter, let’s say you’re like a deep value investor, and that you actually are doing it right. And it was a horrible quarter, right? Just because value got beat up. That happens all the time. And, you know, basically, what the literature found was that managers will what’s called window dress their portfolio to make the names at the quarter end, look better than they probably were. So what would happen there is let’s say your momentum portfolio is down. And you know, you’re afraid of like crap. Now I gotta show my holdings and explain to my shareholders, why I’m doing poorly. Well, one thing you could do is just be stick it out and be like, Hey, I’m sticking in my process not changing. Another thing you could do is you could say, hey, you know what, let’s just for like one day, add in some like Apple and Amazon, because those are like some good names that people want to say. Right? And the literature found that managers do this. And actually, the literature has also found that managers do this and they actually benefit from it and how they measured that was willing to invest or stick with a fund that’s down if it has good names in it or bad names. And actually, if you put the good names in, people will stick with you. So there’s literally like benefits in investor behavior why people would do this. So given that, Wes, and I were like, hey, maybe you momentum works well, in these quarter ending months, mainly because people are going to be like selling their losers, especially in December, but selling their losers and then buying winners. And what we found was, that’s generally fact that quarter ending months are generally the best month to run a long, short momentum portfolio. December’s definitely the best, because not only is there a potential window dressing, there’s tax loss selling, and well what happens in tax law selling, you’re going to sell all your losers, and you’re going to hold on to your winners. So what does that mean? Low momentum stocks are going to keep going down, because people are going to be selling high momentum stocks. People are not going to rebalance until Jan one. So there’s going to be literally less selling pressure there, which could increase their prices. And so as a way to take advantage of this known facts, so Wes and I, at the outset, we were like, Yeah, we have an a pub paper, you know, and then we found out that this paper was published in financial review, which is like I would say, like lower tier journal. So, you know, we basically just replicated something that was already out there. But we were like, Hey, let’s take advantage of this in our strategy, because it seems to be systematic. And so what we do is we now rebalance our momentum for firms into the high momentum stocks, basically a month before, right the at the beginning of the quarter ending months in a way to potentially front run if this behavior exists, people buying up the high momentum firms.
Corey Hoffstein 56:43
As a quant, it always just strikes me as a very weird concept. I know the literature has proven out that this window dressing exists. But as a quant, it’s just such a foreign concept of of window dressing. But it does seem to be a truly powerful anomaly. And something that can potentially be exploited if if systematically played. So switching gears here a bit, the momentum strategy can be a bit of a chameleon at times, I actually recently had a conversation with a manager who mentioned to me that if you were to look at their portfolios, current factor characteristics, they would actually load very strongly on your traditional 12 Minus one momentum. And I actually pulled up their current holdings in your wonderful free tool set that you guys offer. And it was true that compared to the market, they loaded very heavily on 12 Minus one, despite the fact that 12 Minus one was not at all a part of their process. And they simply argued that, you know, what had happened was the securities they picked and their process ended up being the ones that had done very well recently. And so the momentum factor, it actually, in many ways turned into their portfolio, which is more of a quality growth portfolio, a bit of an anomaly there. But I wanted to address this concept of we often think of style drift as being a very bad thing. But in many ways, momentum is a chameleon, it can look like many different styles, and really, whatever style is in favor, I wanted to get your thoughts about the style drift of momentum? And is it a feature? Is it a bug? Is it something that investors should consider when they’re incorporating it into a portfolio?
Jack Vogel 58:36
Yes, so I think what investors should do is understand especially because generally, you know, not everyone runs like a stock strategy. So to the extent you’re trying to access it through like an ETF or mutual fund, you probably just want to understand, you know, if there is a solver, because as you mentioned, you know, some people may have industry constraints, some people may not, some people may try to be sector neutral, etc. So, I don’t think it’s a bug whatsoever, to be quite honest with you. I think it’s actually like a benefit of momentum, meaning, you know, like, just take the classic like internet bubble, right, and then the crash thereafter. So in the internet bubble, what did you want to do? You would have wanted to be long all these stocks that were just trading at insane prices, and you kind of knew they were but trying to time when that factor is going to work when it’s not going to work. It’s I’ve just given up like literally have given up. And so a neat aspect is hey, if you were running a momentum strategy going into the internet bubble, that would have been great because you would have been in all the.com names. But then what happens in the crash thereafter is actually I think just as equally important and just as interesting, right? So what is momentum do it literally is kind of, it’s almost like just shut close your eyes, pick the firm’s have done the best over the past year. sounds kind of crazy, but not that crazy. So, you know, when when the.com bubble is coming crashing down? What’s going to happen? Well, if you’re doing it systematically, every quarter every month, what’s going to happen? Once the stocks are going down, you’re gonna be like, hey, maybe some of these like, you know, old school economy, industrial firms that are up 5%, literally, because their price is nothing but their dividends 5%, you would have went into those firms in the crash or after? So I think it’s kind of a neat aspect to it. Obviously, I’m probably biased, but I don’t know, I don’t think it’s a bug of the momentum strategy. I think it’s kind of a benefit.
Corey Hoffstein 1:00:41
Momentum is a pretty well known and established factor today. And talking about features and bugs. Is there anything that you’ve learned since launching a momentum strategy, or even even in researching, prior to launching that surprised you about managing it in practice versus theory?
Jack Vogel 1:01:05
Definitely, like if you said I could go back five years ago, and no one thing it would definitely be that, you know, concentration is, is can at times be a double edged sword. Right, clearly, and the other thing is that, you know, summary statistics are just that summary statistics. Right. And what I mean by that is, clearly, if you look at the data going back in time, right, even that new article that we kind of discuss, you’re like, hey, you know, deciles do better than quintiles concentration works? Well, totally. You can’t you can’t argue with the data, right? Date is date is make sense, probably makes sense for a lot of reasons. But one of the downsides is that well, the minute you start taking on a more concentrated portfolio, you’re gonna have a lot more tracking error relative to an index. And, you know, I think we have now kind of tried to educate potential people in our strategy, as well as just everyone who’s just trying to learn about, you know, different types of factor investing, that there’s definitely a pro con aspect of factor investing were generally concentration in the past. And if I had to guess, yeah, I can’t predict. But if I had to guess, in the future, concentration, generally lead to higher returns in the past, but it led to higher tracking error. And so to the extent you’re investing in these strategies, you just want to make sure you understand that if you are a person that’s going to literally open your statement and be like, oh, man, last quarter, my momentum fund just lost to the index by 5%, what’s going on the process is broken, you’re literally never going to be successful in it. And in that case, you’re probably better off doing like a closet index type momentum strategy. So I think the biggest thing learned would be the pros, cons, which is really just the con is really just tracking error, and potential underperformance of a more concentrated portfolio.
Corey Hoffstein 1:03:11
So speaking a bit of those pros and cons in factor investing in general, and I guess more specifically towards momentum. What do you think the biggest mistake that you see? Or maybe maybe even the biggest misunderstanding that you typically hear out there from investors and allocators about momentum?
Jack Vogel 1:03:32
At some level, it goes back to that original question. And maybe the first question you asked me, which is why aren’t there more momentum funds? I do think a lot of people are just like, hey, momentum, it’s just the growth strategy. Right? And they already have their growth managers. And I do think it to the extent for some reason, in your head, you he got a value and growth bucket. Yeah, we can throw it in the growth bucket. But I would say probably the biggest misunderstood concept is that it’s definitely not the same thing as growth investing. So that’s probably what I would say is number one misunderstood or thing that even today, I’m still surprised when I talk to people who haven’t even literally heard about momentum, and they’re like, oh, that’s just growth, and then you have to explain the difference.
Corey Hoffstein 1:04:17
So Jack coming up on the end here, I know that you guys at alpha architects spend a lot of time dissecting new research that comes to market you guys have an absolutely fantastic blog where you publish a lot of your thoughts and the new evidence. I wanted to know in your role as a portfolio manager, as CO CIO, how do you think about changing a strategy after it’s launched? The friction that exists in trying to change a systematic strategy once it has well published rules, how you think about incorporating and addressing new research that’s come to market and When When do you think a strategy needs to be changed? Versus when does it not need to be changed? I was hoping maybe you could just sort of expand on those thoughts a little bit?
Jack Vogel 1:05:11
Yeah, that’s a good question. So one is, obviously, well, let me let me go through two topics there. Right. So one would be, you know, clearly you want to be reading the new research, just understanding what’s going on. And you know, even the old research as well, just to make sure you have a full grasp on it, we have like, for example, with our value strategy, even for momentum, now we’ve tested a couple additions to see whether or not you know, they would have added any value. And maybe sometimes they’ve been marginal, but again, nothing like statistically significant. So I think two things would have to be be true for us to make a change. One is, it probably either have to be highly statistically significant, meaning that, you know, by adding this, you know, the two streams are definitely different, definitely adds value, or it would just have to be something that is, you know, maybe a small tweak that probably could add value over the long term and makes just economic sense, or, since from a asset construction standpoint. So clearly, the research plays a part and then the second part is obviously, to the extent we were allocated a lot more capital, potentially, we’d have to, you know, adjust our strategy. So, you know, if we had a billion in momentum, I think it probably be slightly different. Not vastly different, but definitely be slightly different than how we’re managing you know, 100 million in momentum.
Corey Hoffstein 1:06:38
Last question for you Jack. If you were to describe yourself as an investment strategy, or maybe better if your wife or to describe you as an investment strategy, what investment strategy which she described you as and why, and this can be any investment strategy, this could be a you’re just index market cap, you could be momentum value, you could be convertible bond arbitrage special situations, any strategy out there, how would you describe your personality?
Jack Vogel 1:07:11
I’m gonna go with a low vol as a factor. And, you know, one of the things is, you know, I’m totally fine content happy with literally doing the exact same thing every day. I think I’ve had the exact same lunch since for the past 15 years. I could have the same dinner every night. To my my wife, it frustrated her initially, she’d be like, we just had that last time like, oh, yeah, but I liked it. So why don’t we just keep having the same thing. So I’m gonna go with a low vol because I could just rinse repeat same process every day and be literally 100% content.
Corey Hoffstein 1:07:49
Jack, it’s been absolutely a pleasure chatting with you about momentum today. Thank you for joining me. Yeah, Cory. Thanks for having me on. Thank you for listening to my conversation with Jack Bogle. You can learn more about Alpha architect at alpha architect.com. And follow jack on Twitter under the handle J Vogues oh two for shownotes Please see www dot flirting with models.com. And as always, if you enjoyed the episode, share us with your friends on email, social media, and leave us a review on iTunes.