Today we are joined by Wesley Gray who is the CEO of Alpha Architect, a firm in the US that specializes in concentrated factor strategies. Having completed his MBA and PhD at the University of Chicago – the Harvard of the finance world – Wes is an authoritative voice when it comes to quantitative research and factor investing. Incredibly, he took a 4-year break during his PhD, joined the marines and went to Iraq, and has also written several books. He went from value investor and stock-picker to having a strong quant focus and realized that it was possible to eliminate the human biases while still capturing the factor premiums. Our talk with Wes illuminates the nuanced nature of factor investing, behaviour versus risk-based factor premiums and active management versus passive and indexing. He discusses the process of collecting data for his PhD, the rules according to which they structure portfolios, how their boutique firm differs from larger advisor companies and who their ideal client is. Wes also shares his views on selecting the best quant model, hedge funds, value premiums and market-cap indexing. Join us for another insightful episode!

Key Points From This Episode:

•    Wesley’s experience as a stock picker and riding the wave of small-cap value. [0:03:31.0]

•    The Value Investors Club as a data source to test stock-picking skills for his PhD. [0:06:43.0]

•    From stock picker to a quant and realizing the need to eliminate biases. [0:09:38.0]

•    The rules that govern how they build portfolios in his firm Alpha Architect. [0:14:26.0]

•    Comparing Alpha Architect to Dimensional Fund Advisors and AQR. [0:17:13.0]

•    Understanding reliability in the context of relativity and defining their ideal client. [0:22:28.0]

•    Advice for retail investors about quant shops and choosing the best quant model. [0:26:55.0]

•    Wesley’s view on hedge funds and their strategies. [0:32:57.0]

•    Why education rather than assets should determine the active risk that is included in a portfolio. [0:36:24.0]

•    Thinking about persistence in the context of a behavioural component. [0:38:03.0]

•    Why value premiums are not dead and how it relates to behavioural theory. [0:43:22.0]

•    The global explosion of market cap indexing and guidelines for investing. [0:47:28.0]

Read the Transcript:

So your story is fascinating, and if you could start, because I love this part, can you tell us about your experience as a stock picker?

Sure. Kind of lot of ups and downs. I kind of started investing old school, Ben Graham, Intelligent Investor, BibleThump and value stock picker guy. I literally did that when I was like 18, when I could officially really do it. The problem with me on stock picking is at the first stock I bought with any sort of skills, this thing called Swisher Sweet cigars, like super crappy cigars. I don’t know if you guys ever smoked them, but I bought this thing like 6 bucks. It was like deep-value, huge return on capital, huge return on equity. Kind of like even a Buffett stock. And I think it was like two or three months later, it went from 6 and it got bought out I think for Philip Morris for like 10 bucks or something. The reason I say it was like a terrible experience is I made a bunch of money, but then I was like, “Oh! This is easy, man. Why doesn’t everyone just be a stock picker, buy value?” So that was kind of like my intro, and that was also right around like the internet bubble time when everyone was making that money. So I had that one good pick, but then obviously because I was doing value stuff, all my other picks weren’t doing anything. Then I got confirmation bias, like, “Well, all these internet people are crazy. They don’t know how to make money. They’re just buying over-valued stuff.” So I just kept doing kind of the net net where you’d buy companies for less and working capital. It looked like liquidations where there’ll be some stub stock where the liquidation value was like a dollar and selling for 80 cents and there’s an actual active liquidation going on. So I was just trafficking in like under 50 mil total trash deep value stocks, and I did that for a longtime. That was obviously a good time to be doing it, kind of that 98 – This was probably around 2004 until I joined the servicing. But as you guys know, small cap value that run from probably around 2000 to 2004 was epic. And at the time, I wasn’t really quant. So I didn’t really understand how factors work. I was a stock picker that just happen to be in a wave of like small cap value whooping it on. So my Swisher Sweet thing went well. Had a dry run there with the internet bubble, but then obviously had some monster wins where I’d make 100% return on these little stupid value stocks and I just – Yeah, thought I was a genius. I did that all through like the first two years of the PhD program there as well, which I started in 2002, and then kept stock picking basically till around 2004, which is when I left to join the service and was gone for about four years or so.

So this leads right into a question about your PhD dissertation, which was at the University of Chicago. To me, as I understand, it was about proving that value investors have stock picking skills. In fact, your advisor was none other than the recent Nobel Laureate, Professor Eugene Fama.

Can you talk about that experience at Chicago and with Professor Fama?  

Yes. So one of the things, like on backgrounds on this PhDs is you’ve been having PhDs hammer on the crisp dataset, which is kind of like the priced dataset that all academics use. So everyone, and their grandmothers, been hacking on that thing for 50 years. So essentially the only way you’re ever going to get a dissertation done these days is you got to go find your own data source and kind of build up your own story. Then one of the things I had been doing as a stock picker is I was part of this thing called Value Investors Club, where it’s like this invite only thing. You got to apply. It’s like stock pickers anonymous set up by this guy named Joel Greenblatt. I’ve been on that thing for years and I read every single pitch, because I was contributing pitches and I’d read everyone’s pitches and I was like, “Man! These people – Of all people who are stock picking, these individual hedge fund type seem to have some value add. So, I was like, “You know what? One of the things I’d like to test is let’s actually see if this is a systematic effect. Does anyone actually have stock picking skills?” Literally, I read – I think there’s like 4,000 of these stock pitches, databased them up, did all the analysis, and you got to do all the factor aggressions. See if they have alpha, blah-blah-blah. I got Fama to sign off on it. Obviously, he made me confine the language to, “Yes, this is true, but it’s for your sample. It’s not an equilibrium effect. Not everyone can beat the market forever.” So like it was my little win against the [inaudible 00:08:21] bosses founder and I thought it was fun.

Yeah, I remember. So I entered the program in 2002, and then in those PhD programs, the first two years, you’re just getting beat down on tests and like typical academic work. Then I left for four years. I was in the service. Then when I came back in 2008, I’ve been into war, I’ve been to combat. Maybe I was probably overconfident and I just wasn’t afraid of Professor Fama to be frank. Eventually I became more afraid, but I was almost just too dumb and too overconfident. So I was just like, “Oh! What the hell? I’m going to do exactly what you probably shouldn’t do on a dissertation,” because at the time I was probably little overzealous. But that’s why I think I was able to do that. Because obviously most PhDs are probably not going to go down that path.

Unfortunately, it’s been good and it’s been bad. Because I’ll tell you another one, because I think you got another question about how the heck did I go from stock picker to a quant. Well, it’s very easy. So I’ve done all those – I’ve been doing the stock picking thing and then I finally I got convinced, like, “Hey, maybe this whole quant thing is probably a good idea, because it seems like a lot more efficient way to invest and having to do all these work.” So literally I launched a fund in September 2008, which you guys probably remember, it was like the worst time ever to startup a hedge fund, and it was literally like a quant, like deep value quant model long side, and then it would just short, like R2K futures, because I was doing small value. This was to be kind of market neutral. So I launched this fund and literally I got my short future, or sorry my short – I was using cash, not futures. It got called in. So I couldn’t be short anymore, because at the time, people would think that’d be impossible if you’d tell them that, “Oh! Yeah, right. R2K could never be called in on short.” But guess what? It happened. So I couldn’t even be a hedge fund anymore. So I was like, “Okay. Now I’m long only.” Then of course, again, being overconfident and being an idiot, I had this quant model. It was going to limit all my biases. Now I have to be long only. Now I’m seeing all these net nets and all these chaos and I’m like, “Oh my God! This is best opportunity ever to be a stock picker.” So I literally talked to the LPs and I was like, “Guys, we need to ditch the quant model and go back to stock picking, man!” This reminds me of like 2001, 2002. Of course, I did this, and it had a few good wins, but then I had a few like just insane losses. In the end, after I think 2008, 2010, essentially it was R2K with like twice the vol and about 10 times the stress. Of course, if I just stuck with the damn quant model, I would have beat the R2K and me and not had all the stress. That was kind of like for me, man, I was like, “You know what? I’m done. I’m not doing stock picking anymore.” And kind of ever since then, I’ve been just pure systematic. I don’t do it anymore.

You touched on it briefly just now, but can you talk a little bit more about the importance of biases when you made that switch from being a human stock picker obviously with biases to quant? How did you realize that you had to eliminate the biases?

Again, this is a personal thing, because there definitely are stock pickers, and I know a few who they’re just machines or robots. For whatever reason, they have way better capability to kind of harness and manage these problems. I used to think I could do that too. But for me it was just eating like tons of humble pie. So I kind of did – If you guys know the Warren Buffett story, there are stories about him like 70% was capital in GEICO, and obviously that ended up being a good thing and he’s a multibazillionaire, and I ended up doing a similar thing where I had this super concentrated portfolio, this like uber small like net net liquidation deal, and one of them in particular, which ended up being a fraud and like a total bust, I think I got it up to like 60% of the capital in there because I’ve done so much work, and when you do so much analysis, what happens now – Of course, you read all the psych studies that say I was just succumbing all the problems. But as you read more and more information, your confidence goes up, but a lot of times your forecasting ability of like predicting doesn’t really move. You need the first few pieces of information that kind of get you in the right ballpark of reality. But then what happens is a stock picker, like me, is you get so obsessed and you read everything. You do all these work. You spend all this time and you just keep getting more and more confident in your decision and you end up doing stupid things like I did where I had like 60% of my portfolio in one penny stock, which after the fact obviously seem stupid. But I actually did that. I think it was just because I was falling prey to all the standard problems and I realized through experience and after getting my ass handed to me that I just need rules, man, because there’s no amount of convincing yourself when you’re in the fight that you’re not suffering from overconfidence. You’re not believing your own story. So you just need rules. At least I did. So for me that’s very important, but other people are different.

So let’s talk about those rules and how you brought those rules to being at your firm, Alpha Architect. And can you talk about how those rules impact how you’re not building portfolios?

I always came in to like the quant world, like the academic world, from a stock picker mentality. So you do like the sorts on portfolios, even like book to market or whatever P ratio and you always say like, “Wow! If I just hold like the top decile of cheap stocks, this seems like a pretty great strategy.” It works. It’s got super high expect of returns. Obviously, it’s totally inane with a lot of tracking error and it moves allover like the general benchmarks. But at the time, I was just focused on trying to make money for my own personal dough, and I just thought that was super interesting. I could build these portfolios. Just do simple decile sorts, like going to French’s website. Anyone can verify this. It was like, “Hey, it makes sense to me is if I’m going to quantify some concept, like buy cheap stuff,” i.e. the value anomaly. “I’ll just buy the top decile and own them, because that seems to be where all of the mojo is.” So that’s kind of when we set up our firm, because I hadn’t been ever really in the business of asset management. So I was not thinking about tracking error, like constraining, all these stuff. It just never even crossed my mind, because I’ve never been in that world. We just always start our portfolios like, “Hey, if I want to capture the value premium, what is like the best way to do this?” Without even considering all these other things out there in like institutional constraint world. So all of our portfolios, we ended up just moving it towards what we call like focus factors. But the idea is we just want to be amped up in the particular characteristic we want as much as possible. Yeah, it’s going to have like tons of user cratic vitality and what have you, but the idea is presume you’re going to pool these things together and you’re going to use them on top of whatever, your vanguard funds. So a lot of that will kind of washout, but you still get access, like the higher expected returns associated with being I super concentrated factor. So that’s how we kind of started, mainly because we never got brainwashed by being in the real world, where now I know after the fact had I gone to like a normal place, we would probably never came around to this idea, because we just wouldn’t have been thinking like that. So that’s what we do, concentrated factor portfolios basically.

Our listeners are at least somewhat familiar with Dimensional Fund Advisors, because we talked about how they think quite often. Can you talk a little bit about how Alpha – Because you mentioned factor investing and higher expected returns. And I think our listeners are familiar with that type of language. Can you compare Alpha Architect to Dimensional?

Sure. Yeah, we actually model a lot off of Dimensional. So first I’ll tell you where the areas that – I mean, really, I respect those guys and gals. Marlena Lee, we’re old Chicago mates. But I mean guys just broadly. But DFA and AQR, like one of the things that really resonate with me with both those firms is just focused on like education, and it was almost like they’re academics that are just doing investing, which that kind of made sense to me and resonate with me and I just like the, “Hey, this is not proprietary. This is transparent. You can go redo the results yourself. Here’s our model.” Obviously, sometimes they hide a few things here or there. But the big muscle movement is transparency and education and evidence-based. We obviously resonate with those two firms on those points 100%. But the other thing is you can’t just say, “Hey, I’m going to go in the business and redo DFA or redo AQR,” and they’re monsters and I’m like, “Do I really want to be managing like hundreds of billions of dollars and managing thousands of people?” Not really. I used to be in the Marines, and I like managing small teams. I don’t like managing people problems. I was like, “Well, what can we do that’s kind of like that in spirit but differentiated?” So what we do is, again, we just do the kind of more focused factor versions where, yeah, they have way less capacity. DFAs got – Whatever. $700 billion. If you said, “Hey, Wes. Here’s $700 billion.” Obviously we would not be doing the kind of strategies we’re doing, because we couldn’t. We just got too much money. But we’re a boutique, and I kind of like that. So we have the luxury, I guess, of because we don’t have billions upon billions of dollars, we can do these more concentrated smaller portfolios without having to worry about crazy market impact problems or just functionally not being able to do these strategies. So that’s really a difference. We’re just much more concentrated in our factor approach, where they’re more, what people call like closet indexing or lower act of share, which is fine. It just serves a different purpose, but we’re just different in that regard.

Yeah, it’s a great way to describe it. It’s just the difference in the concentration of the factor.

Yeah, exactly, and there are just tradeoffs. Obviously, if you do more concentrated, it’s going to be more crazy. If you do less concentrated and it’s more molded to look like a marketing index, it’s going to be less crazy, and that’s just the tradeoff that people have to deal with.

So, can you talk about that tradeoff, Wes? Because we talk a lot about, and I always say that one of Ben’s favorite words is reliability. So by going more concentrated, how do you view the impact on reliability of capturing that higher expect return from – Especially the value factor?

Yeah. Essentially, there’s a tradeoff. So, if you believe in like the risk-based story, let’s say like DFA tells, which actually I believe into, at least in part, and that story is essentially, “Hey, the more you own cheap, because cheap is generally a proxy for risk, the higher the expected return you get.” Okay. Great. But then you have two dimensions. Well, I could do a portfolio that owns like a super diversified basket of, say, whatever, the top 500 cheaptest or something. Yes, that thing is going to be – Because it’s got so many more holdings, it’s going to “be more reliable” and have a lower dispersion of possible outcomes. At least relative to like whatever, S&P 500. However, the other thing is, well, if I believe in the risk-based hypothesis. Well, if the top 500 are good, imagine the expected return profile, the top 50 or the top 100. If you believe in risk-based hypothesis, well then the more you amp up in that characteristic that proxies for risk, the more risk you’re presuming buying. Therefore, the higher the expected returns are getting. So as you get more focused, your expected returns mechanically, in theory and in empirically, should go up. However, because those are more concentrated portfolios, obviously the distribution of outcomes is going to be wider. So then it’s really a question of, “Well, I could go for lower expected return, tighter distribution outcome, or I could go for higher expected return, wider distribution outcomes.” And then it’s really just an understanding of like what do those histograms or distributions look like and I am willing to take on – Essentially, do I want to get higher expected returns but take on higher distribution outcomes, or do I want to get more lower expected relative returns and get tighter distributions? So it’s just a tradeoff I’d say.

The reason that we always talk about reliability is we’re generally speaking to sort of mass affluent clients who have – They’re wealthy, but they don’t have so much wealth that they’re never at risk of running out of money, like a regular wealthy family. Not an ultrawealthy family maybe. For them, I think reliability is super important.

To be clear on that, the reliability, depending on who you’re talking to, is really about relativeness reliability. So it’s about how you reliably beat the market. For example, really, this is the tradeoff. So I could build you a system that’s super low tracking error, and this is fake, but I make a system that beats the index by 50 bibs a year highly reliably. I could build another system that beats the market over like a huge cycle by – I’ll make it up 5% a year, but it’s unreliable as hell. So your instate long-term growth rate is way better, but the question is, in the short run, do people want the thing that hits the single all the time or do you want the thing that will actually make you potentially rich, but it has these relative underperformance? It’s not like it goes to zero. It’s just on a relative basis to some baseline S&P 500 or whatever. You look like an idiot a lot of the times, which is a key differentiation, because there’s the risk. There’s relative risk, benchmark risk and then there’s straight up, is this portfolio going to go to zero or does it have a drawdown profile that it goes down 100% when the market goes down 60 or 70. That’s another. That’s like absolute risk. Those are two things that confuse people a lot of the time when people talk about these factors in portfolio construction.

For sure. So when we’re talking about this concentrated structure and the wired distribution of outcomes like we’re just talking about, do you think about your ideal client differently than a firm like Dimensional or maybe a firm like us where we’re dealing with people’s retirement money? How do you think about your ideal client?

Yeah. I mean, in general, our idea of client is a lot of – It’s like everyone wants Warren Buffetts, right? So we want people that are highly skeptical, willing to learn, been humbled so at least they’re open to the idea of like doing quant systematic and they realize there’s not some magic genie stock picker out there. These people have super long horizon. They understand the rat race of trying to beat the index every quarter. They know that’s a loser’s game. It’s the long horizon, permanent capital, super sophisticated investor. We love those people, but obviously everyone loves those people and it’s like finding a diamond out there. That’s fine. But then the other type of client, it’s really like the financial engineering client. Because we always say, “Listen, in the end, you got this cake. It’s got a lot of market and it’s got a little bit of whatever you want.” Like this value factor, let’s just say. Then there’s different measures out there, tracking error. Like active share is a pretty intuitive one where it’s saying, “Hey, is this cake 95% just the vanguard fund and 5% of what you want, or is this cake 100% of what you want and not much vanguard fund?” So, what you can do as someone who’s a user of different products, you could for example say, “Hey, Wes. You guys got this manic fund that’s like so doped up in value. Yeah, I’m not doing 100% of that, but I only need 10% of that. I can bolt that on to a vanguard fund and I’ve now replicated the closet indexing version of value for like half the cost, because I financially engineered the same thing, but at lower cost, because I’ve broken up the pieces into the pure activeness and like the closet index kind of market beta piece.” In theory, our stuff can be used by sophisticated users to a lot of times replicate other things cheaper and or just add diversification to whatever their version of the factor is already doing. In theory, it could be used wider spread to many clients, but obviously needs to be sized much differently and the person who’s baking that cake for the client obviously has to be pretty sophisticated in how they form the portfolios and what have you.

So is this how you think people should look at all the different quant shops that are out there? Pick something I’m generally speaking here. Pick something that’s diversified broad-based, like Vanguard or Dimensional or whoever and then go and select what you call the doped up options on top of that? How do you view this whole quant world for the retail investor?

I mean, I think for the retail investor, the market is kind of moving towards this barbell, where you got super scale kind of kind of closet index market beta-ish stuff. Maybe it has a little bit of tracking error on some factor. Then you got the boutiques who are doing nonscalable, super weird niche stuff. Yeah, they’re not getting huge allocations. Then you got the middle row. So you’ve either got like super cheap, superefficient monster scale product, and then you’ve got super boutique, super niche weirdos and anyone in the middle is kind of screwed, because what are they doing? They can’t compete on the scale guys on price. They can’t compete on the botiques, because they’re not as focused. I think the world in general, at least down in the states, you’re kind of seeing this barbell of Vanguards and iShares –

Who’s in the middle? The active mutual funds are in the middle? Is that who’s in the middle ground?

The active mutual funds that aren’t actually doing anything different, i.e. like people that have super low active share. They’re inherent kind of closet indexes. Down here, those people are getting smoked out of the business very quickly, because people are now like, “Hey! Wait a second. This is basically an overlay chart on S&P 500. Why am I paying you all these fees to do this different stuff when you’re really not different at all? I’d rather just take that fee bucket, put most of it over here in like Vanguard, or iShares or whatever and then I’ll save for my active best on factors or stock pickers or what have you. I’ll go allocate that to people who are actually doing stuff that’s highly different shaded and presumably unique and also hopefully affordable, tax efficient, blah-blah-blah.” So I think that’s where the market seems to be going down here, where like you said, you have kind of like these core portfolios of like very cheap, very broad-based, and to the extent that the client has the behavior to handle it, because they can deal with tracking error, and the user knows how to do it and they want to take a bet on value or momentum or whatever. They can then start peppering those in as kind of satellites to fill a client-specific goals or needs or what have you.

So how do you think about – You have quant models that you’ve been involved with developing from an academic perspective. And I know you have Jack Vogel, who’s like a super data guy and he’s helping you build those models. But there are other shops that are building models. So if I’m an investor or a portfolio manager or whatever, and I want to add that doped up value premium or whatever, how do I choose how’s got the best quant model?  

Yeah. I mean, honestly, one of the things that we’ve learned overtime is there’s really no such thing as best. It’s about understanding the transparency of what the heck are these people doing and how does this fit in my process or how does this help clients achieve a particular goal? I think the most important thing when you’re dealing with quant shops is how transparent are they? Because you need to understand what they’re doing, because you then need to explain it to someone and how focused on education and reality are they? Because all these models that are doing anything that’s going to add “excess return” in like a back test or historical sense, well, the reality is those things earn expected excess return, because they probably suck. They’re either higher risk or behaviorally being challenging to own. So it’s not really about like how awesome your quant model is. It’s about how reasonable is your quant model and how much do you help us explain this to clients so they can actually sit and deal with this quant model. I think the ability to help on the behavioral side to help people stay on their seats, which is not really pure quant, but kind of pure quant with a behavior element is what allows people to actually be successful. Let me summarize. If I had a black box quant shop who doesn’t tell me what the hell is going on and they just say, “Hey, it’s all proprietary. Just trust me.” Even if it was free, I would not want to do that, because presumably whatever they’re doing will have a bad streak. I’d much rather go to a shop that’s like, “Hey, here’s what we’re doing. Here’s a bunch of materials that explain, outline why this works, why it stinks sometimes. When we’re going through bad times? We’ll help explain that to your clients so they don’t blow out exactly on time.” That actually has a lot more value than, say, this black box quant shop that let’s presume they had an even better quant model. Let’s just assume that was true. I think you need the whole package these days.

That’s really insightful commentary. We just had on a recent episode Ted Seides, who worked for David Swensen at Yale. Ted told us that what David was so good at was having a set of beliefs and then being really, really good at communicating those beliefs to the stakeholders.

Yeah, it’s huge. You guys know it being in the business. We’ve learned overtime. We’ve always been like direct to consumer in the sense that that we never had like distribution or whatever. So we always just put out research, because that’s what we know how to do. So we’ve always been lucky to attract kind of these weirdo people that are into this stuff. But overtime, we started to attract kind of normal people who just find it cool or flashy, and those people were like, “Oh! Wow! This is what the real world is like and they’re always asking every month like, “Why did you get beat by the S&P or whatever?” So it’s just – The real world, you need to have someone there that can help on keeping people solid, stick in the seats and sticking with the mission basically.

So speaking of flashy, what’s your view on the hedge fund world and alternatives?

So, my view on any investment is it boils down to what’s the process? What’s the cost? What’s the tax efficiency? Etc. So hedge funds in general and hedge fund strategies I think are great. Obviously, they’ve all stunk it up on like a return basis. But I think the idea of market neutral, manage futures, like all these like super niche boutique-y true diversifiers are great if you can access them in a way that’s affordable, tax efficient and understandable. The real challenge I think that’s always been with the hedge fund business and like the fancysmanchy deal is that conceptually it makes a lot of sense. It’s in the theory. Pull a bunch of super diversified, unique exposures together. They all kind of cancel each other out on the risk and you get like this nice expected return. The problem is, that’s the theory, but once we incorporate the brain damage of understanding all these stuff, the crazy fees, the tax inefficiency, blah-blah-blah. They’ve had a bad run. Until the world or someone figures that out, it’s – And this is where I kind of agree with DFA to some extent. Is it really worth the brain damage of it? So I think it could be, but that’s been a big challenge in the hedge fund industry for the last future or the last history of how it’s always been.

We kind of touched on this when we were talking about the reliability of outcomes. But if I look at like the quantitative value index, for example, it’s got 41 holdings. At what point or do you worry at all about idiosyncratic risk?

Yeah. Definitely, we worry about it, and there’s kind of two level of thinking. There’s at the individual portfolio level and then there’s at the level at like the higher portfolio where a user would presumably incorporated this in to their broader diversified portfolio if they’re doing like an efficient frontier type thinking. But at the portfolio level, you guys know all the diversification studies. A lot of the idiosyncratic risk starts to bleed out very quickly once you – Buying one stock is probably crazy. Buy five is pretty crazy. 10, crazy. 20, 30, 40. You’ve kind of capture or you’ve eliminated a lot of like the baseline idiosyncratic risk at that point. Then from 40 to 100, to 200 to 500, sure, you get marginal idiosyncratic risk benefits. But it’s an expense of higher expected returns. So that’s where there’s this tradeoff of like, “Hey, do we want to do our factor model on one stock?” Probably not. Even though it may have amazing expected return, but maybe like 40, 50 to 100, that’s a range where we’ve eliminated a lot of like if the CEO dies type problems. But we still going to have an amped up where you still got super in values case like cheap characteristic and presumably higher expected return. That’s why we’ve kind of chosen those ranges around 40 to 50 basically.

Is there a level of assets that an individual should have where it makes sense to add more active risk to their portfolio?

This is a great question, and what I found through experience is it’s not about level of assets. It’s about level of education, willingness to learn. So I sit on – I didn’t mention anything here. But I sit on boards of insanely rich people and some of them are like the most sophisticated people in the planet earth. Others, they don’t get it. Maybe they don’t come at the world from like an investment standpoint. They may come at it from like a manufacturing business standpoint where everything’s like, “What’s happening in a second? What’s the efficiency?” Their mentality just makes them terrible investors, which means, “Hey! You should probably buy a Wanguard fund. And every day, you can look at the Vanguard fund, match the S&P 500. Done. That’s great.” I’ve noticed that the level of money you have a lot of times doesn’t correlate perfectly with how good you are at dealing with and managing and understanding active risk type bets. So I’d say it’s more about like the individual’s willingness to be educated and their understanding of the tradeoffs without the risk versus how much dough they have.

That makes sense. That’s good insight. We talked earlier about the risk story for factor premiums, and you mentioned that you partially believe that. But the other part of your belief is that there’s a big behavioral component. Now, we talk about this in the podcast quite a bit where if factors are risked-based persistence, you can be fairly confident that they’re going to be persistent. But if you’re taking the angle that there’s a big behavioral component, how do you think about persistence?

Yes. So it’s a great question. So there’s obviously this huge debate in academic literature where people are trying to say, “Well, you don’t have excess return, because it’s riskier.” And when they say risks, they mean like fundamental, like macroeconomic equilibrium risk and they don’t really consider something like career risk, which is a behaviorally-driven thing, because you don’t want to do crazy stuff because your clients put their money out. That’s not really like a fundamental risk, because it should be arbitraged away, right? So I just think of risk in general in the financial markets. It’s just pain. If you want to map the pain the fundamental risk, great. That’s awesome. That’s probably persistent. But you can also map the pain to the cost to arbitrage. You got to be insane to hold this portfolio and take on monster career risk. You got to do weird stuff that people don’t like. This is also costly, but in a different way that may not be related to how the returns correlate with your consumption patterns or whatever. So I just say, “Hey, as long as there’s identifiable pain and that pain is something that out of sample we can continue to expect, I would say that that’s fairly probably going to be persistent. On the risk side, let’s just break this into like the fundamental risk and what they call like behavior risk. On the fundamental risk, that’s pain. Taste could change. People might consider what – They don’t like, for example, like there’s a lot of arguments now that the pain of owning idiosyncratic risk has gone down a lot, because it’s so easy to diversity it globally and own every asset class on the world. So maybe if there were some sort of premium in there for taking on idiosyncratic risk, that’s come down. So now just risk-based models where they used to earn really high expected returns, because diversification is so cheap. Maybe that premiums kind of come down overtime. So that’d be one area to think like, “Hey, out of sample, I want to understand risk. How it’s priced.” You could think about, “Well, is risk pricing changing overtime?” On the behavioral side, usually that is – There’s a situation where there are some issue in the marketplace with behavior. People overact or underreact or they performance change, what have you. That would obviously be eliminated immediately if it was costless to arbitrage. But the issue is there’s a lot of times limits of arbitrage, because, for example, exploiting value, as proud DFA holders and an investor myself, we all know it sucks to be value investor when the market goes up 20% a year and you’re not doing anything. So in order to exploit some of these premiums, even if they may be caused by like a behavioral issue, if it’s costly to arbitrage it out and presume it’s going to continue to be costly to arbitrage this out, these sort of premiums will probably also persist. So that’s how I kind of think out of sample, like if I earned excess returns, it’s probably an element of risk and then behavioral issues that are to arbitrage to the extent that arbitrage is still costly and to the extent people still hate risky stuff. I can predict from an economic standpoint that I’ll probably earn some sort of excess return. But the bottom line is it’s going to be painful. If it’s not painful, then it probably won’t exist out of sample. So that’s kind of the framework that I think about it.

That’s a great explanation. I can definitely see the argument for the behavioral side with limits to arbitrage.

Yeah. Then just to extend that – So one of another reason why we’ve gone into more concentrated factors is actually related to kind of a projected bet about the equilibriums of the future, because factors have become so much more accessible, computing powers everywhere. At the margin, do you think you’re going to get the edge buying the 500 cheapest stocks with a tilt? Probably not. At the margin, that’s probably been arbitraged out because it’s easier to risk manage. A lot of the behavioral and like tracking error capabilities have minimized the cost of being a “value person”. But I still believe that if you’re willing to go into the most extreme, so maybe the whole equilibrium is that every factor earns less excess return than it has in the past. But at the margin, the most pain is going to be in these crazy whack job type focus factor portfolios that we’re doing. So I feel more confident that that’s more sustainable out of sample than doing this kind of closet index factor constraint, because I do think at the margin, everything gets arbitraged overtime. So, anyways –

What’s your take? You kind of just talked about it. Maybe there’s at the margin not a whole lot of premium left in the value risk premium. Maybe there is in concentrated bets. But do you think the value premium is dead? Is it coming back?

No. I don’t think it’s dead, and I’ll tell you. Why does the value premium work in the first place? At least the theory? So, the risk base there is, “Hey, you don’t want a bunch of these crappy companies that – They’re crappy. They have shitty economic situations. So they should be cheap, because you need to be compensated for owning the non-Amazons of the world. So that’s theory one, and there’s probably some truth to that for sure. That’s one harder to asses out of sample. But the other one, which I think is kind of related, is the behavioral theory for value in particular is like it’s an overreaction to bad news, right? Value stocks, yeah, they’re crappy companies. We all know that. These are not Googles. But what happens is the fundamentals come out. Yeah, this is a really bad company. But overtime, it turns out to be not as bad as expected, and then there’s a reversion in estimates about the future of this company and then you make your little spread, risk adjuster, what have you. So what happens, if you look over the last 5 to 10 years, the problem with value, and this is particular problem with book to market value, is the fundamentals when you bought were crappy. The problem is the realization of the fundamentals were actually crappy. In some cases, even crappier than expected. It wasn’t a case of people threw the baby out with the bath water. It’s that they actually underreacted to just how bad it was going to be. I think just value had a fundamental bad role, and that happens. That’s kind of the risk part. Then the other component is if you look at – Like a lot of times, what you’ll do is you look at like earning supervises. So value stock, obviously it’s priced as value. Everyone hates it. But then they come out with earnings and they’re like, “Oh my God! It’s way better than expected,” and you generally get like a boost there, because this is news to the market, because we need to change sentiment about this stock. What you’ve seen is kind of like these earnings surprises and the reactions to them on value stocks that have actually beaten had actually been a lot more muted. One argument is that maybe it’s just because there’s so much passive out there and there’re not enough fundamental guys and there’s just so much sentiment, like Amazon’s going to destroy everyone. Everyone else is a loser. Even if a value firm comes out with fundamentals that beat expectations. They just don’t get that same kind of jump that they used to. So that would be an argument like maybe the behavioral component has been arbitraged away, or it’s just the shortterm in-sample situation and this has happened in the past where fundamentals were actually worse than expected in price, and people were wining at just for whatever reason, you haven’t gotten the same sort of boost. Now I would not bet that that would be the same case going forward, because I still believe in human issues and human overreaction to the bad news in like these value names. Because who wants to own Macy’s or whatever? It’s just like retail or energy. I still believe that sentiment drives markets and they’re still an overreaction to bad news in the short run that on average ends up being better than expected. I still believe in the value premium, because I think human behavior are still on average going to miss it, and I think the risk is still there. You’re going to buy crappy companies. Well, you should get compensated, but this in-sample, it was a bad run, because fundamentals were just really, really bad. I don’t know why I would expect that in the future. I think value is still alive. I just think it’s going to continue to be painful.

What a fascinating answer. So, market cap indexing is exploding around the world and certainly in the United States and you’re way ahead of Canada in terms of broad market, market cap indexing. Knowing what you know about markets and all the studying you’ve done, do you think it makes sense for anyone, or what would you feedback be to anyone who’s investing only in market cap weighted indexing?

So what I’d say is market cap weighted indexing is actually – It’s kind of like going back to our hedge fund discussion. It’s an amazing solution, because it’s so cheap, so tax efficient and hard to screw up to bad. A lot of times in financial markets, if people have bad advice or people are always trying to exploit them, just doing simple things, like owning market, not paying too much fees. Not trading a lot. That gest you 99% of the weight there. So for a lot of people, great solution. Now, what’s interesting is it’s also somewhat constraining, because you’re essentially buying mega cap beta factor if you look at it through a factor lens, and that’s fine, because you got it so cheap and efficiently, but you’ve also eliminated a whole world of diversification opportunities. You could get value, momentum, quality, whatever it is. There’s all these other ways where you can make that portfolio more robust and have more access to more risk exposures. Why wouldn’t you take advantage of that opportunity to the extent you could also get those cheap, tax efficient and create a behavioral setup where you can stick with them. So the long-winded answer to say for some people it’s probably a great solution. For other people, there’s probably more opportunities to add additional risk factors on there to the extent that they’re educated about it. They can access it affordably and all these other things.

So you’ve written – I’ve read both of your books on quantitative value and momentum. If you had to choose one factor to put all your family’s money, bet all your family’s money on, what would it be?

I would honestly probably go like global deep value, just because it’s easier to manage. It doesn’t require like tons of trading and it’s – I don’t know. I’m more confident in deep value and global just to keep it diversified and not just U.S.-centric. In momentum, it’s not that I’m not confident in it. It’s just – I don’t know if my wife would like to watch her momentum portfolio every day. It’s not intuitive. It’s just behaviorally a lot more challenging. So, I would go with just global deep value, because I know my wife would be like, “Oh, yeah! I get it. Buy cheap stuff and hold it for a long haul. That makes sense.” I feel like behaviorally she would sit in her seat and she would also take advantage of capturing beta, capturing value premium and capture global diversification.

Interesting. So global deep value in a diversified, not a concentrated portfolio.

I would keep it somewhat concentrated, but kind of like with [inaudible 00:50:10] you end up U.S. international. You’re probably like 100, 150 stocks. I don’t want to keep it somewhat concentrated just so I could actually ensure that I’m still exploiting or capturing the value premium. Because obviously if you have 100 stock global portfolio, you’re going to catch the beta there noisily versus, say, buying whatever – Buy everything in the world. So I want to capture that, but then I want to make sure I’m actually capturing the value factor in a meaningful way.

This is my last question for you, Wes, and it’s one that we always ask. I think you’re super impressive, and for lots of different ways, I think your background is extremely almost eclectic. From your education to the fact you put your education on hold to serving the Marines. As I understand it, when you’re overseas, you learned Arabic, which made you very valuable when you served in Iraq. On top of that, you’re a long-distance runner, and I see online you do all kinds of crazy athletic endeavors. So given this whole set, and I know there’s a whole lot more to your story. But given the set of experiences, how do you define happiness and success in your life.

There’s kinds of two elements. One is just – I’m kind of old school in the sense I just like being with my family. So we have our – I’ve set up my whole life where like we run our office and our business like out of my house. It’s kind of like a compound just so every night I got to hang out with my family every morning. I got to hang out with my family, and I got three little kids. So I just watch them grow up. So that’s really important to me. For me, that’s being successful, like setting up a job where I actually got to hang out with my family every day as supposed to working hundred hour weeks and my kids don’t even know who I am. Then the second one is just – I mean, I’m just kind of tribal by nature. So, like, I love the Marines, because you’re hanging out basically with your friends each day and everyone’s on like a common mission. So I try to recreate that as best as I could in like civilian world. So that’s kind of what I do now at Alpha Architect. All my partners, it’s an owner-operator firm. So we’re kind of all in it to win it. We’re all friends, like both professionally and personally and we all have this kind of common mission to empowering investor’s education. So that kind of replicated my need for tribalism and hanging out with a team and being focused on a common mission. So I’m good, man! I don’t really have any other complaints. In my head, I’m successful. So that’s all I need. Just be with my family and hang out with my friends and drive towards a common goal.

Books From Today’s Episode:

Quantitative Momentum: A Practitioner’s Guide to Building a Momentum-Based Stock Selection System — https://amzn.to/2XZWFoH

Quantitative Value, + Web Site: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors — https://amzn.to/2U6GuVh

Links From Today’s Episode:

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Alpha Architect — https://alphaarchitect.com/

Wesley Gray on LinkedIn — https://www.linkedin.com/in/alphaarchitect

Wes Gray on Twitter — https://twitter.com/alphaarchitect?ref_src