In this episode I am joined by Michael Krause, co-founder of Counterpoint Asset Management and Counterpoint Mutual Funds.
Our conversation covers two major topics. In the first half, we discuss some of the nuances of high yield bond timing and the subtleties of strategy construction.
In the second half, we discuss long/short equity strategies. For listeners more interested in the technical, this is where the meat and potatoes of the conversation lies.
We discuss Michael’s evolution from regression to machine learning techniques, the unintended consequences of accidental exposures, and managing risk through optimization while managing the risk of optimization.
I hope you enjoy my conversation with Michael Krause.
Corey Hoffstein 00:00
All right 321 Let’s go 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 new found research due to industry regulations, he will not discuss any of new found 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 00:52
In this episode, I am joined by Michael Krause, co founder of counterpoint Asset Management. Our conversation covers two major topics. In the first half, we discuss some of the nuances of high yield bond timing, and the subtleties of strategy construction. In the second half, we discuss long short equity strategies. For listeners more interested in the technical This is where the meat and potatoes of the conversation lies. We discuss Michael’s evolution from regression to machine learning techniques, the unintended consequences of accidental exposures, and managing risks through optimization while managing the risk of optimization. I hope you enjoy my conversation with Michael Krause. Michael, thank you for joining me today. excited to have you here on this episode. You have a really interesting background that I want to start with one that’s pretty unusual for the world of finance or just unusual in general, you actually started an Internet service provider at the age of 14, which you then went on to sell when you were 19. That is an entrepreneurial story I have to hear, regardless of what this podcast is going to be about. But can you just take us back and tell me about what it was like starting a business? Back when you were 14?
Michael Krause 02:08
Yeah, so I was very enterprising young kid, I guess looking back, I don’t think I give myself any credit. Maybe I shouldn’t give myself any credit. Right now that’s being not humble enough, but long story to a slightly shorter one. I at the time was an eager pre Internet user. I call it pre internet, because it’s not what we know of it today. We had a local internet access provider that we could get on we had the Cleveland Freenet, the Internet access provider that was available at the time was a Unix shell access. And what you could do on the internet at the time was pretty rudimentary. You could go do go offering, you could look at kind of the pre web. This is 1994. You could do some basic internet email, you could do some FTP. So it was a very different time. Usenet was a big thing, which nowadays we have message forums and all that sort of thing. So getting on the internet was an exciting endevor. There wasn’t even really much of a large community really, it was largely an academic thing and just starting to catch on with services like America Online. So I paint that picture. At the time. I ran a BBS a bulletin board service, I was just 1314 year old nerdy kid. This was my way to meet people. And I basically had about 15 users within my small local bulletin board system in Cleveland, Ohio, that were really kind of captive. They were excited about what I was doing. This was called the exchange BBs back then. It was a Mac BBs. Actually, I was a apple person back then. I’ve turned my back since but I do have an iPhone now. And I do cynically wince at people who are or Mac users now for overpaying for their hardware. But aside from the point that’s a tangent in itself, I basically had about 15 users that were willing to pay ahead of time for service and went up to them and I basically said, Can you pay three months ahead of time and at a startup fee? I got $90 Each from these 15 people. I went to my brother, who is about 11 years older than me, he just graduated college and actually he got into the mutual fund business just prior to this. So he was about 25. And I showed him hey look, I have people are interested. They want to see my BBs become something more substantial, you know, be able to be a way to get on the internet. So it showed my brother Dan enough, and he invested a few $1,000 in what amount is several company routers, enough equipment to set up a dial up internet service provider, how it worked was imagined two or three small homebrew Unix servers were running BSDi Unix at the time, we had four dial up modems, so that was downstream. And then upstream, our internet connection was a one single 28.8k. modem. That was how we could serve basically all of these people, all 15 people and maybe a little extra demand, you know, like anything operation wise, you don’t have full utilization. So, you know, you could make four modems stretch to I don’t remember the multiples, let’s just say 4050 users, on average, without anyone user generally getting a busy signal, even at peak times. It’s funny when I later went to do my MBA years later, you know, you’re the introduction to operation research. And you talk about queueing and utilization and bottlenecks and all this sort of stuff. And I realized that was intuitively dealing with a lot of these problems without any training. Anyway, long story short, I went to my brother, he funded the thing, I basically taught myself how to be a systems administrator, I wouldn’t characterize myself as a programmer, per se. Now I have some programming and database skills that I use day to day, but back then really, I could kind of hack anything together. And that’s kind of how I survived with tech. So I basically taught myself how to be a system admin, I got the O’Reilly books, which I think we talked about, you’re getting O’Reilly books, too. I’m so impressed with you. And basically, each book would be some sub area back in the day. So TCP IP, talking about the protocol stack and how everything was structured, how to administer DNS bind server, you know, this is fundamentally a DNS server, how to administer an SMTP server, you know, all these protocols. So every single aspect, I had basically teach myself, and that was enough to go on, we started up. And fast forward five years, cutting the story really short, we had about 8000 customers, we had probably about 30 corporate clients who had various dedicated connections, and upstream where I told you was originally a 28.8k connection. Now we had multiple T threes coming in into the premises, and it grew from instead of the upstairs in my parents house, which was basically my bedroom, it went to some actual office space, we had about 20 employees, it was quite an experience,
Corey Hoffstein 07:33
I would say, there’s a risk that this entire podcast could just become about you age 14 to 19. And we never talked about anything finance, I
Michael Krause 07:41
actually think that’s a lot more interesting than telling the truth.
Corey Hoffstein 07:46
I mean, when you when I first started chatting, I think we were teed up to talk about some optimization stuff on factor portfolios. And we started talking about backgrounds. And he started telling me about this. And it was bring me back to the days of mid 90s, I remember starting to learn about a lot of the protocols and people on message boards to say, Oh, just go read the RFCs. Right. It’s like, yeah, why don’t you go read the original document. And you could back that, you know, I think we were talking about how, in the early 90s, mid 90s, it still felt a lot more like cars seem to be in the 60s where they were mechanical, and you could understand it. And as things have gone on, they become more and more electronic. And there’s more interfaces layered on top, and you get further and further away from that understanding. And if you asked me to open the hood of my car at this point, it’s like I don’t want to touch anything, because it’s so electronic.
Michael Krause 08:32
I think what happened fundamentally is and this is a general statement about technological progress we’ve made is that everything has gotten much more high level, right. So now we abstract. Now we to do even coding, right? You don’t necessarily start with assembly language and these fundamental layers, you start on high abstract levels, object oriented layers. And it is kind of oddly dissatisfying because you don’t kind of get to get your hands dirty with what really makes things go. You’re detached from it. Yeah, I can relate to that.
Corey Hoffstein 09:07
There’s certainly a bit of nostalgia to it, but not having to remember to free memory anymore is I’ll wash my hands of that. But let’s fast forward a bit because you did sell that business. And I know you made some major changes you were thinking about being a professional pianist for a while, I believe, but you did eventually find your way towards the field of finance. So can you catch us up to speed a little bit how you ultimately made that transition?
Michael Krause 09:33
The key theme here is I’ve not actually gotten away from my musical roots ever. You know, I studied piano I play classical piano. If you see on my Twitter profile, I say I’m an amateur classical pianist, and it’s a big part of my life. After I sold the business, I went that direction, went to music school, found some great teachers and kind of pursued it to its practical limits in terms of discovering something I can actually do, feasibly. He and I came to a conclusion. Unfortunately, that really wasn’t what I was really cut out for in the long run just as a sole thing. But it’s something that’s stayed very close to me. The firm that I created counterpoint is actually named in homage of ba ba was the master of counterpoint. Right? Most people think it’s just a cool name for a business that might ring a bell. If you’re a contrarian investor, right? Counterpoint. You know, a sophisticated investor, maybe it resonates with people that way. But but really what it is, is that what I did, basically, I kind of fell in love with finance accidentally, after I sold the business, I had a natural gas partnership, it was a private investment I made, it was at the end of so the end of the 90s. Right when the gas markets were in a relatively depressed state. So what turned into was originally kind of a tax optimized investment, not particularly great on an ROI basis. It bred a fascination for all things economics and markets. So in my early 20s, like a lot of people, I took up trading and the idea of researching to invest, and amongst my earliest trades, and it was probably the worst thing that could happen for that period. I had a great trade, I was watching the natural gas spreads. This is mid 2005 2006. So this is calendar spreads difference between contract month valuations, and saw some very unusual things relative to historical data. I just made some intuitive bets that paid off and they happen to be opposite of what the Amaranth hedge fund was doing. I don’t know if that rings a bell does that? Oh, yeah, absolutely. Absolutely. Okay. So I was just, I was really an outsider in a market that I really had no idea about, maybe I lucked into decent trade, but I made some money and got the idea that, hey, this is something I really want to pursue, and not just for the sake of trying to make money, but because it genuinely fascinates me. So that seed was sown. And fast forward a few years. In my 20s. I like I said, I studied music to not much practical and I decided eventually late 20s Go back to school, I got my MBA did my CFA credential, and really set upon the goal to get myself into a position where I could be a fund manager someday. So really pursuing that vision I had, what happened later along, right, as I finished my MBA, I actually got a job at the local utility, San Diego Gas and Electric helping in the risk management team. So here, I was starting to utilize my tech skills, not just my fascination with the markets. But you know, I was doing things like programming var models from the ground up to replace some in house tools. And looking at all facets of energy risk, a really fascinating project was a modeling. This was actually an internship I had with a connection I made during my MBA. But this is how I was involved with the utility. We’re modeling a gas storage, right. So fundamentally looking at the Futures Curve and the natural gas markets and coming up with a fundamental model for what storage is worth. What’s fascinating about that, right as you can go through simulation processes, optimization processes to get to the solutions. And in the end, the shape of the Futures Curve. If you see it’s like a zigzag in natural gas, that’s a function of those very real storage fundamentals. And the gas has to come in and leave the shape of that curve as a function of the storage dynamics in the market. So I find again, I’ve kind of always tilted towards these kind of wonkish interest. So in doing the MBA, I’m time shifting back a little, I actually met my professor who we later collaborated with, and he joined counterpoint later as CO Portfolio Manager. This is Joey engelberg. He is an expert in behavioral finance. He’s very well published, especially in the meta anomaly literature. So looking at questions, for example, when do pricing of many factor anomalies occur? You’d find answers like, oh, it’s gonna be a news and earnings related periods. Right. So I met him actually doing my MBA, we later collaborated. And the funny thing is, when I joined the utility, I actually moonlighted and created an investment advisor called counterpoint asset management that was intended to really incubate some single factor equity anomaly strategies. So that’s really how I started off in the business. And then fast forward. Now we’ve created counterpoint mutual funds. I collaborated once again with my brother, who was my business partner in the internet service provider. So that gets us to 2014.
Corey Hoffstein 14:49
So I know and we’re going to talk a little bit about your factor work both in the single factor space in the multifactor space, but one of the first strategies that you launched a counterpoint, and in fact, still managed today was a tactical high yield bond rotation strategy. So I want to start there and talk about well, what’s the premise behind this strategy? And really, why would we expect this type of strategy to work?
Michael Krause 15:15
So what I like about this strategy, and this really goes in stark contrast to equity factor, or equity, anomaly type strategies, you have simplicity, on one hand, and I think that simplicity lends itself to a simple behavioral explanation, which in turn, I think has really borne out the results, especially in a time right where anything quant has been generally very challenging. If you’re in the last decade, I’d have to say, as a generalization, well, how about the last five years in particular, not really the last decade, but the last five years? If you’ve been doing any strategy that goes by long term research, and you allocate a portfolio effectively aligned with that research, you’ve not been rewarded? The best funds out there are I would call anti factor funds. Right? So going back to the tactical high yield strategy, it’s kind of unusual, because during this time, it’s actually one, one quantitative strategy that’s done pretty well through that environment. So just a high level view, what it is the tactical, high yield strategy is effectively trend following high yield index. And when it’s above, a moving average, or some kind of trend indicator, you’re invested. And when you fall below that indicator, or some kind of filter related to that indicator, you get out and you’re either in cash, or you’re in treasuries. These are all variations on the theme of the same conceptual idea. The thing about tactical high yield is that it’s, you know, I’d say to generalize, why would you invest in such a strategy? And how would it compare to other trends strategies, it’s basically a really great way to get risk on exposure. While reducing drawdown. In the end, I can show you back tests that will outperform over the long run, but I think the realistic expectation is to basically get the same return level in the long run. But without the drawdown characteristics, and avoiding the drawdown you know, helps you stay invested in sticking to a strategy as long as you don’t look at non tactical market as your reference point and always compare yourself every minute to it, you have to look at the strategy as a separate investment that you’re willing to be committed to. So this is
Corey Hoffstein 17:37
an area I know there are a number of firms that have offered overtime strategies like this. And there seem to be conceptually very high level very similar to your point, they’re applying some sort of trend following technique on some sort of high yield index. They’re moving either to cash or some sort of treasury position to preserve capital when trends look negative. But there are very subtle nuances in the way that they implement this strategy that seemed to lead to some significant disparity in the performance. For example, are you trend following on a high yield ETF versus looking at mutual fund proxy versus looking at a basket of mutual funds? are you implementing with an ETF versus implementing with a selection of high yield fund managers? And all those are going to obviously create trade offs to the ETF? You can trade every single day, the mutual fund, not as easily. I wanted to get your thoughts on those nuances, which ones do you think are really important for the effectiveness of this strategy?
Michael Krause 18:42
So this is a loaded question. We have products that are in both areas, right, we have a fund, which is our mutual fund product that addresses primarily trading mutual fund vehicles, which on the surface of it sounds completely unsophisticated, and fee layering and absurd thing to invest in. And then on the other hand, we have an ETF product that we launched, and that product only trades ETFs, you know, bike or have regulatory constraint, and design constraint in the end. So to answer it, right, what you trade and how you execute creates all of the disparity and results in the strategies. But before you zoom in, right, if you go back from a very 30,000 foot view, these strategies are all pretty correlated, as long as the manager stays systematic and doesn’t start overriding his signal with discretionary calls. So now getting into the real thick of it. When you trade high yield, you’re ideally right. If you think from a model point of view, I want to get the pure asset class, right and the best way to do it, you can’t go out and just buy the high yield index. This is one of the problems that presents itself the index doesn’t exist. As with all bond indexes, everything is a replication Some are better than others, they can be very close to each other. So you have a continuum of available securities. You know, this is a funny thing, because everyone’s fixated on low fees nowadays. And especially in fixed income, I think they miss some of the subtlety, which really, the subtlety is become something not so subtle, it’s actually a key factor aside from asset class. So when you buy mutual funds, for example, in high yield, generally, as a group, they capture the broad high yield market better than anything, right. So they get the garbage, the lowest credit quality, there’s going to be some exposure to you don’t get just a subset, which is a very liquid subset that you’re bias to, you get perhaps an increase in yield, of course, because you have exposure to lower credit quality in those indexes. And there’s another perk when you enter a mutual fund, right? The pricing services to give a stable price, they price, they try to price, every security in the fund, most of those securities don’t trade on a daily basis. So there’s a bit of stale pricing effect that turns out to stabilize and lower the volatility of these funds, versus what they may be if you had to dynamically trade every single bond within those portfolios every time you traded. So what that gets you is the ability to enter an exit closer to midpoint of more pure representation of the asset class. So trading mutual funds gives you this kind of pure exposure to high yield index more and more idealized exposure. Now, the ETFs are the other side of the continuum, right? Let’s start with hyg or J and K, any of these ETFs. So they have a liquid subset of high yield, that is not representative of the entire basket. It’s one slice of the market, which by product design is kind of a requirement, right? So the one of the reasons hyg became so successful, is because the market makers, the APS, they could trade in and out of the underlying bonds and do the arbitrage against the actual ETF units, right. So if one is mispriced versus the other, you have something to trade against, and it enables the market have more liquidity, that liquidity beget more liquidity, and suddenly it took a life of its own where the ETF trades at a bid ask that doesn’t actually represent the real underlying bond liquidity. So for example, if I wanted to go Trade Representative basket in the hyg, right now, typically, I’d need to pay about 3040 basis points to get in and out of that real basket of bonds. So if I went to a dealer on the market, I’d probably have to pay that spread effectively, whereas the participants at the ETF don’t do it. They just pay whatever the bid ask is, and they bear that cost by the risk and movement of the fund around its NAV. It’s idealized a value that the intraday nav that you see.
Corey Hoffstein 23:01
So one of the this is an area of study to bid at the request to sell my clients. And one of the things I always found fascinating about the high yield bond timing, type strategy was trying to figure out where exactly it was able to harvest its excess returns. So for example, you mentioned specifically the goal of trying to avoid those significant prolonged drawdowns, I would sort of put that in the bucket of we’re trying to avoid those periods of expanding credit spreads, right. But there’s also the opportunity to reinvest at higher and contracting credit spreads positive repricing that might be purely fundamental economic sentiment driven. And then there’s even the opportunity to potentially harvest some excess carry in high yield versus sort of a corresponding Treasury benchmark during calmer market environments. So when you think full cycle and you look at a product like this, how would you think about where the majority of the excess return comes from? Is it from the capital preservation? Is it from that repricing shock? Or is it from that, hey, there’s some good cumulative bits we can earn along the way.
Michael Krause 24:09
So this is a great question. I have some data of prepared that really points to an answer, actually, fundamentally, where you’re getting returned from a strategy like this. So yeah, just to give you an idea, I took a very generalized version of the strategy. So this is not how we implement it. The details of the exact signal aren’t really important when you were talking in broad strokes. So just to give you an idea, if you ran the strategy, it’s a very simple one where you took the 200 day moving average, and you looked at the Morningstar high yield bond category. So this is a category that includes fees of exposure you’re able to, you could generalize, if I buy a few big funds within this category, in and out, I can pretty much track this category. So if you take a 200 day moving average of that category, and what you do is you alternate between it it and three to five year treasuries when the switch being above or below the 200 day moving average, the general return difference over the long run with a simple back test like that, and this is 1990 to present is you get about 10% Return versus the index itself where you get about 6.6%. So there’s a little more than 3% of outperformance coming from that. Now, the thing to keep in mind, right? And this is why a question that I think has a quantitative answer. In truth, we’re going to have to kind of default to, you know, the why you invest in it to more qualitative reasons. And I’ll get into that. So the neat thing about this right is, again, why you have this strategy is, again, to avoid those large market routes and the damage that occurs. So I took just under that simple version of the strategy, I looked at the major signals. And if we look in the last decade, we may be in the middle of one of them, I would think we are intuitively the signal is risk off at the moment. But in the last most recent period, we’ve had two major signals, it’s the financial crisis, which started actually for the high yield market, as it turned down after the Bear Stearns route was at March of 2008. It turned down and started to see signals like that around June of 2008, the main re entry into that signal would have been around April of 2009. So you can see how this would have missed most of the downturn for that period. Right, by just being out, you missed a maximum drawdown of this index of about 27%. But if you look at the missed negative return from exit to re entry, so this means you’re not going to be able to catch the bottom, you’re just going to kind of the idea of what you said reenter at a lower level, your Miss negative return on that was about 18.4%. That’s very, very material in this long term back test that I told you, there’s another period, the 2015 2016 oil crisis. So same thing, the index fell around 10% peak to trough or that kind of drawdown. But following that signal, that simple 200 day moving average, you would have missed about 3.7%, negative total return, this includes dividends, this is total return. So I’ve just pointed out to you about 20% of missed downside over approximately a decade period, this chalks up to a good portion of that 3% differential, that three and a half percent differential in return. The other part is coming from I would call it some noise around the signal is well, there’s the part coming from the fact that when this version of the strategy, I said you’re in three to five year treasuries. So this is another kind of kind of neat conclusion, you know, when we’re talking about looking at trend following our findings, or at least, that it’s not robust to every asset class, despite technical people loving the idea of trend, following everything with some kind of moving average, or crossover, or whatever you can invent. In fact, high quality fixed income is definitely not robust to these longer term moving average signals. It’s really hit or miss. I think you’re better off, you know, just putting your finger in the air and gambling you bomb you maybe, maybe not, it depends how bad you are behaviorally, and how lacking in discipline you are. But the point here is that you’re picking up a little bit from the avoidance of the downturn in high yield, but likewise, from the updraft, in having exposure to high quality duration. And in those periods. So this is what’s interesting is, if I trend follow treasuries directly, the result isn’t so great. But if I trend follow high yield, and say, hey, when I’m out of high yield, I’m going to be in treasuries, you actually do tend to have a somewhat robust signal to picking up a little extra tailwind in treasuries. So basically, the risk off for one asset class is a good signal for risk on and treasuries. So that complements and that’s intuitive, right? The economy is crashing, you want something that does well with the Fed cutting rates and long term growth prospects falling, and Treasuries are your natural security for that.
Corey Hoffstein 29:20
So in contrast to this high yield bond timing strategy, I know you also run a couple of multifactor long short equity strategies, which I want to dive into, because I know this is an area of thinking that you’ve spent a lot of time on and also have evolved your thinking on over the last half a decade. So I want to start with basics which are, let’s just define what factors are you looking at? What are the sorts of characteristics you’re looking at and when you talk multifactor are you talking bottom up? Composite multifactor you are integrated multifactor. Are you talking sort of a top down sleep based approach?
Michael Krause 29:59
Okay, so what we’re talking Talking about there’s so many questions in that question, the place to start, I think is, you know, what sort of factors are we looking at, it’s what everyone else is looking at, to some degree, right. So you have the valuation related, the momentum related. So to give a little detail, you know, you could start, we actually don’t look at price to book, it’s not terribly robust across many market segments in this last 20 years. But there are others price to sales, price earnings, other related factors, cash flow to enterprise value, this sort of valuation idea, you want to capture a sense of value in the momentum bucket, right, you have short term momentum, so two to six month momentum, you have seven to 12 month momentum, so you’re decomposing, effectively 12 month momentum, you exclude that first month, right, because there’s evidence of rebound. So if you have cross section, by the way, she said, everything is cross sectional we’re looking at, you’re taking the universe of stocks today, and you’re ranking them versus looking at time series momentum, which is for our audience, just looking at one asset or one asset class, and trying to trend followed effectively. In the momentum category, there’s related indicators, or you know, we can call them factors or indicators anomalies. There’s a paper on one which looks actually at a simple moving, Average crossover, something like the 21 day over the 200 day, right. And if you look at a result like that, and we put these through machine learning models, so we can see the interactions, and we see how they score and look like to each other and what looks like to proxy each other, you’ll find that ones like that, which seem very technical on the surface. And again, you’re cross sectionally, ranking them, you know, they rhyme, they’re very similar, you know, two to six month momentum is very, very similar to that. So the point is, you can reinvent or re characterize a lot of different fundamental effects. And you know, they can have different names or different settings, but they’re effectively telling you something similar. So that’s value and momentum. By the way, momentum can be again, like I said, that return of the most recent month, whereas it signals not to buy the most recent winner are short, the most recent loser, you’ll get the opposite effect, you’ll actually short the most recent winner, and by the most recent loser in that, right. So what we effectively do, but before I go on, right, we also have sentiment category. So looking at short interest, looking at revisions to earnings, and revenue expectations, looking at analysts behavior, so analysts, for example, one thing they often do, and my co manager, Joe engelberg, he wrote a paper on this is that analysts are often way behind the curve, if they set very high price targets, typically, that’s a predictor of negative returns. So high price targets relative to current price, that’s a great predictor of falling short, right, whereas often they’re behind the curve. And it’s when they’re setting a low price target, and the stock just is pulling ahead of it. Those are the stocks, actually, that tend to do better over the long run. So imagine all these different factors, you take them into one model, and then you create one composite score of them, and then you put a portfolio together. So this composite approach of multifactor is what we do, rather than just taking a few separate factor portfolios have, let’s say, momentum and value, and then just combining them and you know, netting them out, we don’t go with that approach, because we find there’s a better payoff over the long run, to factoring the interactions between the factors, which of these separate simple factor portfolios don’t really do effectively?
Corey Hoffstein 33:37
I know this is an approach that you’ve evolved significantly over the last several years, you started with more of a standard regression based approach, you now incorporate a lot of machine learning techniques into what you do. So hoping you could take us back sort of to the start of the approach and talk us through some of the evolution and the thinking, and really what drove these decisions?
Michael Krause 34:00
Right. So in our process, we’re a small firm, as we started out, we have three people in our investment group not to be underestimated, though, because a small group with the right data capabilities can do an awful lot, right? We launched our first fund tactical equity fund at the end of 2015. And originally, when we modeled it, we were assuming some operational constraints. First of all, we didn’t have access to international names effectively, you know, with having a small company, I mean, a small fund, we had the problem to determine of you know, how best to score, but really, the toolbox we’re working with at that time was a regression toolbox, right? And furthermore, about trying to get the most bang for your buck out of all the different factors. We kind of came to this is interesting, actually, this goes to the end of the story, we came to realization that the low volatility anomaly really dominated a lot of the long term In return from this exposure, so you know, this is just out of our own testing, right? If you look at a single name data, it’s really easy to come upon the conclusion that the most volatile names in the long run tend to do pretty horribly. Right? This is across almost any market out of sample, you’ll see that staying short those names is another point of survival. Another challenge. So what we came out of when we launched the fund was we were trading in us only. And effectively, the fund did a blend it does still does a blend of tactical, but also multifactor exposure. And one thing we did was, we basically maintained a short book that was a small amount when we were risk on. So imagine something like 100% long, and then against a 15% short book, when risk on, right, and then when there would be a tactical switch. So imagine you get a signal to get out of the markets, like the 10 month moving average on the s&p, something like that, then we would rotate the portfolio into a more market neutral targeted portfolio but dominated by volatility filter. So we were effectively looking at 70%, long, 35% short, now we’re getting into a discussion of construction and how to effectively run these strategies. And the one thing to know about this right is we weren’t optimizing an industry exposure. We weren’t saying, well, if our short basket is full of pharmaceuticals, and oil stocks are going to do terribly much about it. I mean, there were some discretionary trimming, but there wasn’t anything terribly systematic about what we were doing, it was pretty much an ad hoc approach to try to manage risk. So we’re optimizing on beta. And hoping that beta estimate is somewhat robust. I say hoping because we know estimating beta presents its whole set of problems. So in the end, what happened, we launched a fund and it was trial by fire immediately, there was a sell off in early 2016, it was associated with that same oil market crash. And our short book was dominated by oil stocks. And I’ll cut to the chase, when we were still market neutral, we are effectively risk alpha in the fund. We had an overweight exposure to short oil effectively. And being a small fund. What’s nice is we can have a small cap exposure. There’s a lot more inefficiencies in the small cap space. That’s a whole nother area unto itself. But the consequence of that naturally, when you’re not optimized, is that you have accidental exposures that you’re not hoping for. Right? So in this case, imagine being short, it wasn’t a large portion of the portfolio. Actually, when I looked back, it might have been 10% of the short of the total portfolio exposure, maybe 8%. Something like that was actually energy shorts. But I’m not actually I correct myself, it was 4%. Now my memories coming back. But those shorts, on average for a four day period doubled. Right? Imagine being short, short night, well, we’ve just seen it. If you’re not managing risk in this environment, you’ve seen some incredible moves, where you have sectors just going up 100% in three or four days, the perils of long short investing, lead you to this. So I think we saw about a 400 basis point move over this period, in our risk our market neutral portfolio, effectively, that it wasn’t dollar neutral, we’re still it was only 35%, short, against 70% long, but it didn’t matter because we weren’t long enough oil stocks on the long portfolio to offset it. So that long story gets you to a conclusion of, okay, we have to go to the drawing board and really see how can we really improve this so we can live with this and all states of the world. What that got to in the end was by mid 2017, we came upon a few things. The idea that the way to run long short the real right way, with $1 neutrality in industry about actually a sector neutrality, industry, sub industry sub sectors, that becomes a little tougher, but by running with sector neutrality, and also even optimizing sometimes on some other factors. For example, in our energy portfolio, even despite being sector neutral, we actually optimize against price movement of oil to have zero beta to oil price. So that way, we’re not long a bunch of refiners and short all the producers because you can again have a disastrous result. Right. So again, you know, running a market neutral portfolio is about setting up the optimizer to get what you’re really after it’s the factor exposures. It’s not the accidental industry exposures or industry momentum, that really can wreak havoc, it can help you but Murphy’s Law, you launch a fund, you’re only going to get the bad output of what happens and and that certainly is how it feels more often than not.
Corey Hoffstein 39:51
It’s funny, I was just reading a paper earlier today that was talking about momentum Based Investing and using residual momentum right so you are basically regressing out all these other factors. And one of the things they talked specifically about was regressing out a macro oil factor, basically saying, if you don’t use that as a factor, normally you would use market beta and value and size and maybe some other style factors if you want. But they started adding in these macroeconomic factors saying, there’s certain of these factors that if you’re not careful, you can find yourself way off sides. And either your short or your long book and the short book can get you in particular. So I like this idea of accidental exposures. I think that’s people I find, at least it’s my hypothesis that portfolio managers don’t tend to be wrong and what they know it’s they’re wrong and what they don’t realize they’re
Michael Krause 40:41
betting on. It’s what you don’t know what will hurt you. Right?
Corey Hoffstein 40:45
Exactly. It’s these accidental exposures that you don’t figure out you have until far too late. Whether it’s specification risk, timing, risk, style risks that you have, you didn’t have, you know, all of a sudden your momentum was very sensitive to value, or whether it’s your particular sector. So I think these accidental exposures are really important. Let’s talk about this optimization based approach, because it’s easy to say, Okay, we use optimization, but optimization is a huge field. And there’s a lot you can optimize over. So maybe you can tell us a little bit about okay, let’s narrow down how do you think about the optimization problem?
Michael Krause 41:19
Right? So if you go out of the box with a straightforward Markowitz optimization, right, and you leave it relatively unconstrained, you give it the universe, let’s say you have 1000 stocks in the universe, it’s going to give you 1000 weights, right? It’s going to you’re going to have insignificant weights for a lot of the portfolio. And if you don’t constrain for example, position size, you’ll end up with absurdly large positions. And there’s a lot of different ways about trying to solve these problems, right? Some managers, some larger funds, by necessity have to go with CAP weighting schemes. Right. So now you’re effectively constraining the solution to kind of a function of market capitalization, right? Smaller funds like ours, which were more nimble, we have targeted an equal weight approach, right. So the idea isn’t to have every stock in the universe in the portfolio, it’s really to get the best of the top decile, the best of the bottom decile, and you mix them together in a way that really solves for a lot of the other problems. And with that, we can have smaller counts of securities. Now, I don’t think having a concentrated portfolio and factor investing is what we’re after here. So I’m not after having 1000 names. I’m sorry, I’m not after having a 30 pure play long and 30 pure play short names. That’s not the intention. But having an equal way to exposure, really, to kind of get a result will enables you to get a result. That’s more in line with all the research effectively most research is done on equal weighted portfolios. It’s not uncalculated portfolios. And you know, it’s kind of funny when you talk about this, you’re you’re kind of jogging my memory to a lot of papers that I’ve seen, and it’s something I don’t see talked about. It’s not in the fin twit world, certainly, it’s the fact that this equal weight versus cap weight, as it applies to neutral portfolios, long, short portfolios, it’s not a very discussed topic people aren’t talking about, well, if we look at the Seminole momentum study, or the value study, how are we waiting the portfolio, there’s a lot of ways to skin, the cat and almost every factor, you could look at multiple dimensions. And you could look at it versus a cap weighted versus evaluated dimension, you might get different outcomes.
Corey Hoffstein 43:36
Optimization is a really big area, lots of academic papers written on it. And there’s all sorts of ways in which you can optimize all sorts of things. You can optimize for all sorts of ways you can design your constraints, your functional form and target that you’re trying to optimize over. When you’re looking at building the optimization problem. What are the big muscle movements that you’re trying to hit? If you were to sort of itemize like your big constraints that you’re trying to focus on are the big targets? What are they
Michael Krause 44:05
the guiding philosophy around choosing the fight you’re going to fight with the optimizer is around trying to avoid UNECE or unintended exposures in the factor space, right? So those unintended bets, right, our concentration, industry exposures that are unintended, and as well, then country, or again, larger sector asset class exposures. Now what can you do, right, there’s so many dimensions that we optimize on the around concentration, right? We target equal weighted portfolio. So there’s a discussion right there about how do you optimize for equal weight using a mixed integer optimization rather than a straight quadratic approach is going to let you solve for that, right that lets you address some of the concentration issues that you get because now your account mapping your position size on sector and industry, right? It’s pretty straightforward. You can take a sector classification, and likewise, constraints. So your net of a sector is zero or you allow some wiggle room, right? If you have some other constraints that are or if you have that constraint binding and you choose, you decide other things are more important. Likewise, a beta, your market exposure, right? So it’s about unintended factors getting rid of them. So as I said earlier, we do a little in the energy space with getting rid of oil exposure, oil price exposure within the energy sector, that’s number one. But likewise, trying to get rid of market exposure the proper way, right. So how do you which goes into not only how do you run the optimizer? How do you estimate beta? And how do you do that across a lot of different assets, right, that creates all sorts of challenges directly, really
Corey Hoffstein 45:56
two inputs to most portfolio optimizations, the return side, and then the covariance, I won’t actually go back to the return side a little bit, because we talked about some of the machine learning techniques that you utilize, or at least on the surface, we mentioned that you use them, we talked about some of the factors and characteristics you look at, but we didn’t really dive into at all the machine learning techniques that you’ve explored. I’d love to get some color on how you’ve tried to apply different machine learning approaches to the return side of the equation,
Michael Krause 46:29
right? So you’re going to love this, you’re going to love this, the best result is in ensembles, what we found, right? If we walk through us this question earlier, right about evolving from regression approaches, all the way to a machine learning approach is what they have in common, right is if you start with the regression approach, in the paradigm of more advanced machine learning algorithms, you have regressor approaches, right? So they start to look like actually, you know, the idea is you’re taking a predictor, and you’re putting that through the model, and then you get a score some kind of prediction based on it. Often a regression approach is used within these other models, whether it’s a gradient boosted tree or a random forest, one of the things right is a big deal about you know, in the machine learning space, it’s how you prepare your data, you don’t just give the model prices and hope you’ll get something out of it. Something we found when we were implementing originally is that a lot of research out there was put out by computer science type people, the people who develop a lot of these machine learning algorithms, and often very little experience in the finance space, right. So they how to prepare the variables and how to really give it something to work with to give those mouths something to work with is a big thing. It’s a very big thing. And those papers which would try to do something, throw some prices through our neural network or through throw some technical patterns through a neural network, we just missed the boat entirely. They you’re basically trying to predict a shape with some kind of other input shape and leaving so much on the table. Maybe in a high frequency vein, you could pull something off there. I’m sure anything can be done. Right. But you know, one thing we learned is early on when we had you know, when we were set up to do regression approach is only something we would do. We found, for example, that affects within different factors were nonlinear, right, you would have, you would have I’m going to talk about the asset growth anomaly. This is basically where companies that do acquisitions, a lot of CapEx, those companies tend to underperform relative to companies that just don’t do much with their balance sheet in terms of growing too quickly. So if you do a cross sectional rank, and you look at those asset growers, they acquires all these companies that basically are often reckless with their shareholders, those companies tend to perform poorly in the long run. Now the opposite, right, the people who don’t asset grow, well, they don’t perform poorly, but they don’t outperform either. So it’s not a buy indicator, right? It’s only a short indicator, you can say it’s a buy indicator of what to avoid, but it’s a measurable at that point, because you want to somehow get a pure exposure to the asset grower. So you need to short it naturally. The point of that, right is that instead of having a linear regression model, which will often have a weak effect, imagine you break up the problem into deciles. Or even you just say top and bottom decile only I’m going to ignore everything in between and you create a dummy variable, right, that represents Oh, is this a top decile asset grow or a bottom decile asset grower? Imagine doing a three factor regression with top bottom decile asset grower bottom decile asset grower and then the market you gotta get rid of the market factor? And what you’re gonna get, often you might have a statistically insignificant effect for the bottom decile companies that don’t grow assets, but you will have a strong loading to the high decile asset grower what that informs right how does this translate it’s how you prepare your variables is every Then, as it is with a regression as it is with machine learning approaches. And the neat thing about a machine learning approach, though, is now imagine you have all these different factors, especially tree based approaches that can look at how all of these different factors interact with each other. You can achieve the same thing with a regression manually, where you can construct joint dummy factors, you know, you can say, hey, what happens when there’s a high asset grower times a high volatility name? And that’s a joint dummy. And then you have a fourth factor you regress upon that can tell you that, but here’s the problem. How many different combinations are there? And at a certain point, how much are you trying to squeeze blood out of a turnip? Right? So you have constraints what a researcher can do. Now you can put all those factors into a machine learning model, like a gradient boosted tree, you run an output, and it’ll show you the interactions, you can get a ranked report showing, oh, this is how it works. So you know, when you think like, I think AQR did a paper about momentum in Japan, that finding that Japanese names don’t exhibit much momentum versus other markets that do, maybe there’s a behavioral explanation about people who trade in those markets versus US markets. I don’t know what the explanation is. But you plug that into a tree bottle, you see the interaction, you’ve basically hired a PhD to do research by brute force for you. Now, the question is, is this a robust factor or just something that’s a product of data mining? So again, now this goes back to the discussion we had earlier right about tactical high yield, we have this strategy that seems robust over time, why the heck does it work? And I think it’s important that there behavioral explanation, right, the idea with trend in general, right, is that people take time to absorb the truth, things don’t play out instantly in the economy. And this benefits trend followers naturally, the same goes in picking factors, I think you have to have an economic explanation, which is why right, this is, I think, your biggest argument, why to be very, very careful, I think, and almost dogmatic about asking these questions about value, has anything changed? Should we ditch value, suddenly, we have people’s behavior impulses changed. And I think people overthink it. Basically, the reality is, we’re in a time we’re in a wave of enthusiasm about stocks, that has rhymes with history. And we have a history of making those airs collectively. But that’s what factor investing is all about. It’s about taking a disciplined side against that, you know, investing in behaviorally consistent characteristics about the markets that will sustain in the long run. So I
Corey Hoffstein 52:41
want to talk about the second really important input as well, that covariance matrix, which I know you’ve spent a lot of time working with, for listeners who are maybe a little less familiar with the whole optimization process. One of the problems with the covariance matrix in traditional mean, variance optimization, is what that naive mean variance optimization is going to do is basically break down all of your securities into these independent principal portfolios. And each of those portfolios is going to be ranked from the one with the most variance to the one with the least. And traditional mean, variance optimization is then going to take all these independent portfolios and lever them up. So they all introduce the same amount of risk into the portfolio, which ultimately means that the independent portfolio with the smallest variance gets levered up the most. And when you look at it from a mathematical perspective, what you find is that is most likely to be the part of the covariance matrix that is just complete and random noise so you’re ultimately jacking up your noise and reducing your signal. And so there’s all sorts of techniques of dealing with this shrinkage Eigen value clipping. And Michael, I noticed is an area you’ve spent a lot of time on. So I wanted to get your thoughts as to what’s been effective for you. What hasn’t, maybe leave a little bit of bread crumb for our listeners who are exploring this area.
Michael Krause 54:07
This is a fun area. And I do love tinkering with the optimizer and often struggling with some of these problems. One of the things we found, right, is that you can test a lot of ideas, right. And you know, from a high level, think scientific method, you have kind of a controlled setup. And you try something, you control it, you test it against your baseline, and you have a way to kind of measure what is better than what I had before, right? So it’s something we can do very easily right when we’re trying to create our covariance matrix, which by the way, for our listeners, covariance is effectively a combination of volatility characteristics and correlation characteristics and inter asset correlation characteristics of the portfolio you’re trying to construct. Right? When you look at the problem, right? You’re trying to Measure, essentially, what can be improved upon. So in correlation, I can compare my model result of correlation. So talking about that one subcomponent to an actual after the fact realized correlation, so I can take Apple and Google and run a sample correlation, you know, in their history. And we know that history is the actual right. So now we can go and test prior to knowing that actual history and create different models, right, so you can have a model of correlation. Let’s go back to covariance. But essentially, it’s all connected, you can have a model of covariance, that is simply by prior history, you can have a model of covariance, that’s based on taking a bunch of factors that all the members of the covariance matrix are sensitive to. And again, constructing estimates of covariance on those factors. Another thing you can do a little more cutting edge is do that same exact process of looking at Inter asset correlation. But instead of a regression approach, effectively, you plug it into a machine learning model. And again, those factors that are, you know, the assets are jointly sensitive to will give you a loading. So we’ve been able to test all these approaches. Now, you mentioned quarry, you have shrinkage, you know, which is effectively about trying to get rid of some of the noise in the matrix that causes estimation error, because the optimizer is very sensitive to small perturbations, small differences that can come from noise. So there’s another method that’s a de noising method that’s been put out there I read about recently, if you basically take you show the eigenvalues of a matrix, the Eigen values, or you’re basically mapping your principal components, the first Eigen values are very high numbers, those are telling you hey, it’s normally the market factor is that top Eigen value, and then you go down the list. And most of the eigenvalues of the matrix are garbage. It’s all noise, right? So the idea is you take those lower values, you normalize them, you take the average of them, you kind of reset them, this is a de noising method. But all these get at kind of the same thing, let’s reduce the noise and try to show the best of our signals, so the optimizer can make its best decisions. So here’s the kind of neat findings we’ve made around this. Number one, if you have other constraints that are perhaps more important than the covariance matrix, you’re going to often get a result that’s hard to determine or even kind of, it shows you the changes you’re making to the covariance matrix are kind of insignificant. Which sounds counterintuitive. How can you not if you improve your matrix, how can you not reap the benefit of it, but the reality of it is, and I’ll give an example, in our investment process, we basically take an our universe of let’s say, 5500, liquid enough stocks globally to trade, we picked up the bottom deciles of those within market segment when we rebalance, and that’s all we feed the optimizer, right? So in effect, you might have 1000 names going into the optimizer, we don’t have all 5500 going in. So now we’ve said hey, out of those 1000 names, maybe I only want to make portfolios out of half of them, or even less than half of them, maybe a third of them. The reality is that with an equal weight constraint, right, I can often I’m going to end up with the same decisions, because what’s dominating is the expected returns the other side of the formula versus the covariance matrix. All of this is said with guarded optimism. If we can improve the covariance matrix, well, we’re going to reap a benefit from it. Well, it’s kind of interesting. We have fed perfect hindsight or perfect foresight correlation. So basically taking the correlations that were realized cheating effectively, into our correlation model to test Hey, how does this improve things versus a non cheating method where you’re looking only at hindsight factor returns, or whichever method you’re using, and finding, you’re not going to get much results. So it can be a lot of consternation, depending about how your process works about something that may not be all that valuable. I say this in a garden manner, right? Especially if you’re, if you’re thinking about any one pair trade, you want to improve your correlation, the best you can we try our best. But the point being, especially if you have a very constrained model, in other places, the characteristics of the benefits you’re looking for, unless you’re optimizing on a daily timeframe where your tolerances are very tight, you may not see the result empirically in your testing, which is kind of a counterintuitive conclusion. So this is a long, long if circular answer to your question. But yes, you can use all these approaches. We’ve even tried the machine learning approaches, we get a better result we get lower, you know mean squared error where we take basically factors relationships, given pairs in our correlation matrix, we have an output, that is a better estimate. It’s not as good as perfect foresight. But still, you get very much Original improvement, I find that if you’re running a relatively constrained optimization, with small enough universe, a sub universe that you optimize, I think other parts of the process are going to dominate. So what you’re looking at, I may be able to make result and shows, you know, lower volatility or lower variance in my back test. But you know, I don’t know how much to believe that it could be just a function of noise and air. Because ultimately, remember, I’m running a quarterly rebalance model, but I’m evaluating on a daily timeframe. And you know, even a correlation number over three months, is going to be somewhat disconnected from a daily timeframe observation,
Corey Hoffstein 1:00:40
to your point, it’s been shown in established that different weight constraints are effectively shrinkage techniques in their own right. So when you move from totally unconstrained mean variance to constraints on mean variance, even before you start applying these techniques to the covariance matrix, you’ve already done it in effect through the optimizer, it’s always interesting to see sort of the marginal benefits of all these other numerical techniques that can be potentially mitigated through other actions you can take and in your case, it’s the optimization process and the constraints you placed that move you a long way along the curve,
Michael Krause 1:01:16
I would say that that’s and expected returns are amongst our most important inputs, you know, this is what we’ve empirically discovered, we got a lot out of shrinkage, right? There were improvements definitely to be had, that were very easy to procure. But you know, when you started putting real firepower on the problem, and the machine learning model, or even cheating, effectively, just to set a baseline for what your result could look like, if you got it perfect. When you see how little it moved the needle past what shrinkage gave you? It’s kind of depressing, because naturally, you think, Oh, if I can unleash, you know, all the firepower on the world on this problem, I can get a lot more out of it. No, it’s not the case. You know, the reality is, hey, what’s explaining our portfolio returns are really the factor movements. It’s not our shortcomings in the optimization process.
Corey Hoffstein 1:02:04
So I very selfishly have to ask this next question, which is, you’ve done quite a bit of research into Portfolio stability in the idea of rebalance timing luck, which is a real passion topic of mine. So I’d love to hear what some of your discoveries were. Okay, so
Michael Krause 1:02:19
I have a, I’m like you, I have a real gripe with everyone who evaluates returns out there of anything, it’s obviously there’s hindsight bias plays into things. But you know, I often see people post charts about almost anything and try to compare a factor portfolio, they’ll look at a few percent difference and make it into something. And the reality is, often what they’re looking at is noise when you’re making my new comparisons, whatever it may be. So that’s borne out from our experience, for example, and doing our research our testing on these multi factor portfolios, we found when you rebalance, whether it’s which month of the quarter, so I told you, we have a quarterly model, when you rebalance can have a very, very different impact. In the short term, the shape of the long term test is very similar. But I can have variants within one year, that could be plus or minus 10%, you can have a lot of air thrown into any single incidence, right within the model. Again, the long term picture is a very different one. It’s one that says, Okay, it’s gonna be plus or minus 10. This year. But overall, the picture is the same picture. The point being, you know, we realize there’s kind of a inevitable tracking error from your ideal, but you realize your ideal is kind of formed out of some randomness. So what we do, and I think we’ve talked about this before, you have a kind of a trenched approach to rebalancing. And we do exactly the same thing. And it’s really to replicate our testing. So we basically, when we run our tests, we run with the first month, the second month, the third month of the quarter is our kind of rebalanced turning period, to test our ideas, just to make sure everything is consistent. Everything works. And we see these large differences, but how do we get at making the real life portfolio kind of look like our back test? Well, what you do effectively is you stagger your rebalances. So for us something we practically do, it’s very simple. We imagine we have multiple market neutral portfolios, we rebalance. Imagine on a quarterly one, and then in the mid quarter, and we’ll have half of our exposure in those independent separate market neutral portfolios. And then 45 days in you have the other half of the portfolio, a separate newly optimized market neutral portfolio. So we’re rerunning the model. We’re getting more recency for this period, things go stale, the minute you rebalance, especially with faster moving factors like momentum, category factors. So with that, having staggered rebalance is a very practical solution. It gets you to a more robust answer. And it mitigates I think some of the noise from timing luck, but I think it’s the point to remember even with our high yield strategy We run effectively a single factor trend model. I don’t put terribly much weight in any one signal. Our investors may, I think it’s all noise in the short term, it’s a gamble, the long run very different picture. Right? So we say it’ll all average out in the long run. And I think that’s a prudent investor has to look at it that way.
Corey Hoffstein 1:05:19
So going forward, what are you really excited about researching?
Michael Krause 1:05:22
Oh, researching, I thought you were going to ask what am I excited about with respect to the markets? But with researching? Well, you can answer that as well. Let’s
Corey Hoffstein 1:05:30
Michael Krause 1:05:31
The big thing we all want to know, especially for those of us in the factor space, we’re clearly on the wrong side of the trade in the short term, right, living through the value is trash route. And anti factor is good period, it’s a hard thing to do. I’m very excited to see a turn, like we have in multiple times in history, right? So I would like to experience that just as a proving point, maybe there’s a little ego involved, you put so much work into something, you want to see a good outcome. But that does terribly excite me. As far as research. I do love testing these ideas. You know, actually, it’s funny, you mentioned the idea of clipping eigenvalues and de noising covariance matrix. I love these little side projects. We test these ideas all the time, we have, for example, an ensemble approach we use in a lot of what we do not just the factor side, but the tactical side. It’s always nice to see what new models, what new algorithms can come to light that we can add to the pool of essentially committee members voting at the table. And the same goes, what are the new signals? For what it’s worth an ongoing research basis, we don’t add a new factor very often. And that’s not for lack of trying, we try a lot of things. And it’s just hard to get any improvement after, after you’ve exhausted so much of what’s out there, right. So I think it all fundamentally boils down to we’re seeing results in our testing that’s consistent with academic research. And I’d be suspicious of factors that come out of the blue that teach us something totally different than what we’re already seeing. But it’s always fun to test these new ideas. That’s what keeps things interesting. I guess you always have a fun pool of ideas to play with keeps the game going. Right,
Corey Hoffstein 1:07:22
Michael, it’s been a wide ranging conversation, but one that I’ve had a lot of fun chatting with you about. Thank you very much for joining me. If the listeners are interested in finding you finding out more about your research and following you Where can they do that?
Michael Krause 1:07:36
I think a good starting point is on our website. We have counterpoint mutual funds.com We have a factor scoreboard. And the neat thing about that, right is that for most advisors, or professionals who are not running the factor portfolios themselves and don’t have their own data sets, they often don’t have access to see what the real academic kind of long short portfolio returns are. So there we post that that live and daily, essentially comes out of the models, the factors that are in our model. And it shows broadly what the categories of factors are doing. So we hope that’s a valuable resource for people out there who are just curious to get a little more wonkish about things.
Corey Hoffstein 1:08:14
Well, thanks again for joining me, Michael.
Michael Krause 1:08:16
Thank you, Cory. I appreciate your time. Thanks for having me.