The Exchanges

Q How did that lead you to being involved in the business?

A So it was just dumb luck. So I was quite a bad student in high school and then quite a good student in college. And I studied philosophy, and when I graduated, like most philosophy majors, I had no clue really what I wanted to do. It just so happened that right at that exact time, literally that month, my dad and his team were leaving Bear Stearns Asset Management to form what's now O'Shaughnessy Asset Management. Technically, I joined OSAM as an intern and really just thinking it would be smart to get the opportunity to watch a business get set up and help out where I could. I mean, I was literally looking for office space and putting chairs together and cutting my hands on the chairs. And so there was no agenda other than get the experience of watching a business get set up and established real big business. And very quickly I fell in love with the research side of things. And I sort of moved from an unofficial unpaid intern to a junior level research analyst. So it was really just really good timing for me and that's it. And the rest is sort of history. I had planned on only staying for a few years and it just worked out that I so enjoyed the business and the research piece that I've been here ever since.

AI assessment note: “Technically, I joined OSAM as an intern and really just thinking it would be smart”

Q So as you go to construct your portfolios, how do you use the factors in a different way to create this portfolio that looks very different?

A So we don't have sort of a master multi-factor formula. I will literally describe the process. So we start with a universe. Let's say it's US large cap stocks. We then use these factors that I've talked about, quality, valuation, momentum, et cetera, to remove from consideration the worst decile of each of these groups individually. So what you're left with is like two thirds to a half of the universe after you do this. And again, to highlight why we do that, Most of the really interesting return, in this case, negative, bad return that you want to avoid, is concentrated in the tails, in the worst decile. So we remove that stuff. Then, depending on the strategy, we only are willing to buy stocks within the best decile of what we call ranking factors. So we talked about those earlier, basically value, momentum, and shareholder yield. So already, you can see that everything in the middle of the distribution, from Then most quants do. So most quants are interested in the entire distribution. We really are focused on the tails. The byproduct of that is low overlap with other people, much more concentrated portfolios. I use that word carefully because relative to like a stock picker, we are not concentrated, but relative to most quants, we own far fewer stocks.

AI assessment note: “So we don't have sort of a master multi-factor formula. I will literally describe the process.”

Q I want to turn to something that I know you spent some time on, which is the podcast. How did you get started with invest like the best?

A So it's a really kind of boring, simple story, which was I was having lunch with Jeff Graham and Jeff had just come out with his book, dear chairman. And I loved the book and that's how I met Jeff. So I had back then I had this habit when I read a book that I liked, I tried to email the author and get lunch or something. So I'd done that with Jeff and And he said, or I said at the lunch, wouldn't this be cool if we recorded this and shared this with people? And so we did that just as an experiment. And like, I'll never forget the 571 people the first week listened to this for whatever reason. And I just really enjoyed it. And I thought, I said to our mutual producer, Matthew Passy, I said, I think I'm going to do seven of these and like, see how it goes. And then maybe I'll do another seven, like six months down the line or something, but it probably seems like it's going to be a lot of work. So, you know, I don't have time to do this. And then I did one with Jason Zweig and one with Michael Mobison and kind of went down the list of people that I knew in the industry, and I just had a blast doing it. It's, as you know, it's incredibly fun to sit and talk to smart, thoughtful people about what they've learned in their careers and their lives, and it's just sort of been easy to maintain ever since then. There's an endless supply of interesting people, and I'm sort of interested i…

AI assessment note: “I was having lunch with Jeff Graham and Jeff had just come out with his book”

Q I'm curious to ask, have you thought about quantitative methods or the application of quantitative investing in private markets?

A If I'm right, that If quant will eat it, it will. I think that it's starting. You see some firms that are, if not pure quant, heavily quant driven in their private investing approach. I did a podcast with a guy named Ryan called back from a firm called circle up, which is the one that pops most immediately to mind, but it's basically a quant process, evaluating consumer skews for, you know, momentum and sales and literally even like colors on packaging, you know, the things that resonate that lead to revenue growth. That's their label, right? They want to see revenue growth in the private markets. So I think it's very, very, very early days, and it's beginning to happen. But again, the same problems, and then some additional ones in private market supply. If you're going to build a concentrated portfolio of a handful of positions, you can't do quant. It can't be pure quant. The stats don't work. You need to build a pretty diversified portfolio for a pure quant approach to make sense. Now, might like Angel List or a company like that be in a really interesting position to do something like this? I think so. I don't believe that they do that today, but that's the kind of firm that has an, the N of the universe is big enough that they could start to apply some really thoughtful Quan approaches. So I think what you'll see first is that Quan is starting to be a tool in the toolkit o…

AI assessment note: “You see some firms that are, if not pure quant, heavily quant driven”

Q And when you're thinking about how people should analyze quant managers, what are some of the key things that you would use to assess other quant managers?

A So maybe it's a good excuse to talk about this concept of a research graveyard. I think this is a great first question for quants, which is to ask them to detail their failed projects in as much detail as possible with as much breadth as possible. I think that a really good quant You know them when you meet them, when you're in this business has just tried a lot of stuff and failed a lot of different ways. And that the best way to know that someone's going to be an effective and efficient researcher going forward into the future is that they know what not to do. And I think the only way to do that is to build up that experience of failure through time. We call it a graveyard. So like, I'll give you one example. I always joke that this like has a mausoleum in our graveyard. You know, it's one of our big Big and persistent failures. It's a very compelling idea that I would love to be able to time when I'm a value investor, when I'm a momentum investor, when I'm a quality investor, and shift around based on prevailing market conditions. Factor timing would be the term here, and I desperately want this idea to work. I just think it makes sense that there are some environments more friendly than others for these different factors, and a quant strategy which rotated between factors versus having a fixed allocation sounds really neat and interesting. We have burned an insane amount of…

AI assessment note: “to ask them to detail their failed projects in as much detail as possible”

Q I usually ask some question about reading, and the question I want to ask you is that with all the reading and media that you consume, how do you fit it into your day?

A I don't really know. I mean, I guess it's just kind of constant, meaning if I am trying to think through a problem, I'll often just set aside work time to read everything I can on that problem. So software, we talked about earlier, I've read an insane amount about software in the last three or four months, and that's sort of at any time of the day. I get up early and I read in the morning, I read at night, I read on planes, I read on trains, wherever my downtime is. I listen to podcasts when I run. Sometimes I want them in the car. I just kind of fill the empty spaces with the stuff I like to learn about.

AI assessment note: “I get up early and I read in the morning, I read at night”

Q What was striking about what you learned from them and watching them at the same time?

A So the work that they do on the data science side, Fascinates us. So this is a great example of how data can be thoughtfully used in a more traditional fundamental discretionary process. So they're building company models around, let's say upstream oil and gas businesses. NAV is a, is a really interesting data point for those businesses. What is the acreage that these companies own worth, right? And there's incredible amount of data in the energy world, wellhead data, pipeline data, et cetera, et cetera. And with deep domain expertise of how to normalize, standardize, Cross compare, you know, a well and the depletion in a well or all these kind of really hard things with data. They then pipe that data into very traditional company models to hopefully have a better assessment of what the acreage in this case is literally worth. And then basically compare that to what the market thinks it's worth. And then the gap is your alpha. And so watching them work with a really tricky, hyper specific data set has been very inspiring for us. Typically quants don't do deep industry sector specific Data sets outside of like financials, which has been like a common problem for quants. Quants typically are looking for something you can compare cross-sectionally across the entire population of stocks. And what this has inspired in us is more of a willingness of diving into particular parts of th…

AI assessment note: “watching them work with a really tricky, hyper specific data set has been very inspiring”

Q What life lesson have you learned that you wish you knew earlier in your life?

A This actually relates to reading and stuff we talk about all the time, which is, I wish I had earlier on been forced or tried to build something and then sell it. I think There is nothing more clarifying than that simple exercise, and you can do all the reading in the world, and I still love to read, and I still recommend people read a ton, but as I've grown older, I actually recommend people probably read less and less as they get older, and do more and more, and I wish I had started that a little bit earlier, because I read so much, and I spent so much time just like collecting information, and back to my learning loop, right, like I was doing all learning, No building and no sharing and building and sharing are what sharpen the sword and make you realize how incredibly hard it is to build something that people actually want or give a crap about. So I wish early on that I had done more of that so that I had an appreciation in say my early twenties rather than my late twenties about how hard that is, but also how fun it is and how much more rewarding it is than just collecting information.

AI assessment note: “I wish I had earlier on been forced or tried to build something and then sell it.”

Q In a couple of your podcast episodes, you mentioned the names of some people that you've invested with through your family office and some of the activities you've done. You mentioned Brent Beshore at Adventures. You mentioned The Graveyard. You wrote a piece about analyzing quantitative managers. So how do you think about investing in other managers?

A So the podcast for me Is and will always be a very pure curiosity machine. I don't really have clear ulterior motives where like I am trying to do thing X, Y, or Z. Maybe the one slight caveat to that or exception to that is we are really interested in making high return investments as a firm, as a family, me individually, and I don't believe that a hundred percent of the best returns are going to come from our quant strategies forever into the future. I have the vast majority of my personal money, and the partners of the firm have a vast majority of their money in our own public equity strategies, but I think it would be foolish to think that that's the only and best place to put money. So several years ago, we began making smaller investments externally, and some of the classic ways, you know, just really simple, straight direct deals, early and later stage, typically with people that we knew exceptionally well. So This is not our trade, so we need to have deep, deep trust with the people that we get involved with. What the podcast made me realize was, wow, there's some really interesting strategies out there that are completely different and unachievable based on our skill set and our toolkit. Perhaps we should get into business formally with some of these teams and again, earn a high return on our flexibility. So because we're not managing anyone else's money in this, at le…

AI assessment note: “we need to have deep, deep trust with the people that we get involved with”

Q This last piece in the share in quantitative lessons, how do you balance putting out something that you discover that could have alpha and getting critical feedback that could improve what you're doing versus the risk that the alpha you've discovered will go away as soon as you shared it?

A Yeah, so, I mean, this gets to kind of Movison's great framework of the sources of excess return, and if you believe in this simple framework, which is basically behavioral, informational, analytical, and Trading or structural. The share component is especially effective with something that is rooted in investor behavior. So I would argue all the factors that we use here have a behavioral component, or at least a compelling behavioral explanation for why they work. People have known about value investing forever. People have known about momentum investing forever, and yet they have continued to work. Not value so much in the last seven years, but the other ones have done very well Decades after their original and very prolific publication. So that says something, right? That says that the source of the excess return is not knowing about the thing. It's doing the thing. When that is the case, we're very comfortable putting something like that out into the world. I was talking to Cliff, asked us about this, and he had a great little story where he said there was like one time where they were having an internal fight about sharing, and they decided not to do it. And then somebody else published it, you know, a year later or something. He's like, God damn it. We should have just shared the thing and then accrued the benefits as a business. And so I think that that, when there's beh…

AI assessment note: “when there's behavior behind the reason the thing works, it's great to share”

Q All right, Patrick, let's turn to some closing questions. And of course we'll end with yours. What's your favorite hobby or activity outside of work and family?

A So we do this together sometimes. So you, you know, and it's basically just time in the woods. So Whether that's running or hiking or sitting by the river or whatever, just outside in general, but I live near a very large, like thousand acre state park. And basically any spare time I get, I either go in there myself to run or bring a friend or a couple of friends in to hike. And I do that a lot. It's if you added up the hours that I'm in there, it's significant. So that would certainly be my number one. And then I think of this kind of as work. A lot of this stuff blends together for me, but You know, I still am a voracious consumer of content, books, videos, you know, conversation, whatever. I'm just really interested in learning about different stuff, so I try to fill my spare time up with that as well.

AI assessment note: “basically just time in the woods. So Whether that's running or hiking”

Q As you distilled a lot of the lessons about businesses, and mostly from your work, but again, the podcast, and then also doing it, how have you applied some of what you've seen to OSAM as a business now that you're leading it?

A I would say it's the most fun thing I've gotten out of the podcast is lessons from the worlds of technology, I guess we'll call it generally speaking. So it's not just VC and startup, but big public technology companies like Microsoft and Google and Amazon and asking whether or not we can apply those principles to a very simple old school asset management business. And the answer, at least in my view, unequivocally so far as yes, and nobody else is doing it. Or at least they're not doing it holistically as part of how they think about running their business. So we'll take a few examples. So the platform business model is one that I'm fascinated by. And it's the simplest explanation of this would be like a two sided marketplace like Uber or Airbnb. So you've got sort of this, we'll call it a platform that sits in between a demand side, you know, people that want to ride or somewhere to stay and a supply side, people that are willing to drive or give up their home or their apartment. And Airbnb and Uber sit in the middle and facilitate those transactions. They reduce search costs. They provide tools to allow the consummation of those transactions. They provide rules and guidelines. They're, they're sort of this kind of middleman, almost like a marketplace. So the question is, could you apply some concept like that to investing? Which doesn't really seem like it would work, right?…

AI assessment note: “asking whether or not we can apply those principles to a very simple old school asset management business”

Q Does that happen simultaneously or is there a lag?

A It's hard to say, right? It's hard to parse the causality. All we know is that of the highest momentum decile stocks, just in very, very simple terms, those stocks in the next year have super normally high operational growth. Now, what's really interesting about price momentum is the holding period that you need and the turnover you need to make this strategy work. So I'm always really suspect when someone says like, at this point in the cycle, cause I just don't, I don't think we really understand cycles that well. And certainly we don't know where we are in this cycle almost ever. It's something that's hard to forecast, but it does appear like value in the data. Has a very long payoff window. So if you buy in value today, you, on average, earn a little bit of alpha for a really long time. Most of it's early on, but you continue to earn marginal monthly alpha for up to 10 years. In the case of momentum, it looks very, very different. So you earn all of your momentum, all of your alpha, rather, in the first one year holding period, and then you actually need to get the hell out, because it reverts. And typically, depending on where you're looking at the data, within two years or so, it's actually Completely inverted and now you're earning negative returns. So there does seem to be something about momentum that it's sort of like the last stage of these companies in some sort of …

AI assessment note: “It's hard to say, right? It's hard to parse the causality.”

Q So this one may be repetitive from your opening comments, but if it is, I'll just edit it out. So let's try it anyway. What teaching from your parents has most stayed with you?

A We've mentioned a couple of these ideas. I think I'm lucky in the sense that a lot of ways, but I have very good family soil is what I would call it. Not just my immediate family, but my broader family. So kind of starts on both sides, but most notably maybe on my dad's side of the family with a guy who had the coolest name ever. His name was Ignatius Aloysius O'Shaughnessy, IA for short. So IA was born a hundred years before me in 1885 and was a incredible entrepreneur. Uh, huge oil wildcatter in the Midwest. And in today's terms, had he not given all of his money away, basically would have been, you know, a billionaire, extremely successful. There is this entrepreneurial mythology in my O'Shaughnessy side of the family, which is remarkable down through the generations. So I guess I'm third, fourth generation in every branch of the family, which is a massive Irish Catholic family. There are these crazy stories of entrepreneurial success Some still in the oil business, lots in completely different businesses. There's this deep sense of calculated risk taking and sort of an entrepreneurial mindset and an action first mindset that certainly I got from both my parents, but it has its roots deeper in this kind of really cool, broader family tradition. So calculated risk taking would be something that, that I think I'm pretty good at. That is directly the result of Being told that w…

AI assessment note: “So calculated risk taking would be something that, that I think I'm pretty good at.”

Q And how'd you go from a background as a quant to, you know, exploring things like venture capital and crypto with your guests?

A I mean, I think kind of the process everywhere is the same. We call it here like the learning loop, right? And I'd love to talk about this in detail because I think it's useful for anybody. So on our website, if you go to the website, the first thing you see in big, big blue letters is this four words, learn, build, share, repeat. So think about that as a loop and it's a learning loop, right? So I think The research we're doing, the reading that I love to do independently, the conversations I've had with VCs, like all this stuff is basically just the same loop over and over again. So what does that mean? It means collecting raw information. That's sort of the learn piece, build a map of something, understand who's doing what, what's important. I love business and investing. Those happen to be my fields. I'm really interested in how value is created, how businesses capture that value in a defensible way through time, and then how you can Find that that's mispriced, right? So in my world, it's value that's mispriced that matters. I guess really in all investing, it's value that's mispriced that matters. So those are the questions that animate me. And I think it's just as interesting to talk to a VC about these same ideas as it is for me to do a study in the public market. I've done a lot of that myself. I'm more interested in other areas of the business and investing world to try…

AI assessment note: “I think it's just as interesting to talk to a VC about these same ideas”

Q a little more interested in the kind of the subtleties of the investment strategy and how they make that work with a team, with CEOs, with operating partners, how the whole thing gets made. And then what do they think about the environment, right? We know they can borrow really cheaply, but they're paying a lot for companies. And, you know, what is that going to imply for the future?

A How do you as an allocator with your own and when you're doing it for others, others capital, and probably also those that you respect most that are allocating right now through this very interesting environment, how do you make heads or tails of or navigate this insane performance divergence? Originally, I would have said it was between growth and value, but now it's even a divergence between what I'll call speculative growth and growth. There's this chart that came out about non-profitable growth companies versus the NASDAQ, and they're crushing the NASDAQ. For the last almost a year now to an insane tune. It kind of goes against all the Swenson training, all the Buffett training, all the things that I think a lot of professional allocators came up on are being violated in front of us. How would you recommend people out there, especially at the allocators, think about that challenge in twenty-twenty-one?

AI assessment note: “How would you recommend people out there, especially at the allocators, think about that”