From AQR Quant to Founder & CIO with Brian Hurst
55:54 Share

89% of business leaders say AI is a top priority, according to research by Boston Consulting Group. The right choice is crucial, which is why teams at Fortune 500 companies use Grammarly. With top-tier security credentials and 15 years of experience in responsible AI, Grammarly is how companies like yours increase productivity while keeping data protected and private.

See why 70,000 teams trust Grammarly at grammarly.com slash enterprise.

Join Bloomberg in Atlanta or via live stream on February 11th for The Future Investor, Finding the Opportunities. This 2025 event series will examine how companies are investing in their businesses to create efficiencies, innovate their products and services, and improve the customer experience. This series is proudly sponsored by Invesco QQQ. Register at BloombergLive.com slash Future Investor Atlanta.

Bloomberg Audio Studios. Podcasts. Radio. News. This is Masters in Business with Barry Ritholtz on Bloomberg Radio.

This week on the podcast, yet another extra special guest. Brian Hurst is founder, CEO, and CIO of Clear Alpha. They are a multi-manager, multi-strategy hedge fund that has put up some pretty impressive numbers. His background is really fascinating. Cliff Asness plucked him out of the ether to be one of his first hires at the quantitative research group at Goldman Sachs.

He was the first non-founding partner at AQR, the hedge fund that ASNA set up. And Brian worked there for a couple of decades before launching Clear Alpha.

He has a fascinating perspective on where alpha comes from, as well as the entire hedge fund industry. Few people have seen it from the unique perspective he has. And I think he understands the challenges of creating alpha, where it comes from, and managing the risk and looking for ways to develop non-correlated alpha that is both sustainable and

and manageable from a behavioral perspective. I thought this conversation was absolutely fascinating, and I think you will also, with no further ado, my interview with Clear Alpha's Brian Hurst. Thank you, Barry. Appreciate it.

Good to have you back here. Last time you were on a panel, we were talking about the rise of some emerging managers, including yourself. But let's go back to the beginning of your career. Wharton School at the University of Pennsylvania. You graduate with a bachelor's in economics. Was quantitative finance always the career plan? That's a great question.

I think when I went to school, I didn't even know quantitative finance was a thing. And frankly, at that point in time, it really wasn't much of a thing. I was taken by my dad. He was an accountant and CFO of a commercial real estate company. He would take me to the office. And I was really fascinated by business. I really wanted to get into that. I was into computers. I learned how to teach myself how to program and things like that. But I wanted to get into business. And I said, Dad, I want to get into real estate.

And my dad gave me some really good advice. He said, Brian, if you think about finance as an org chart...

real estate is like one of the divisions. And if you start in real estate, it's hard to move up and go to other divisions and try other things out. You should really learn corporate finance and you can always switch to real estate if you wanted to. And corporate finance is kind of the top of the umbrella or the org chart. And I said, okay, well, what's corporate finance? And where do I go to learn that? And he's like, well, you should go to Wharton. And then I said, well, what's Wharton? That's how it started. That's hilarious. You finish up at Pennsylvania and you begin your career at DLJ. Yeah.

What sort of work were you doing, and what were your classmates doing? This is the early 90s, you started DLJ? Yeah, I did DLJ. It was interesting. That was my summer year between junior and senior at Wharton, and they kept me on throughout my senior year to finish up an interesting project, which is basically all

automating the job of the investment analyst. We're doing all the company work, getting all the 10-Ks, 10-Qs, all the information. At the time, there was a new company starting up. I know I'm on Bloomberg, but it was called FactSet at the time. Sure, of course. And there was a salesperson walking around trying to get anyone to talk to them because this is a brand new company.

And I was a summer analyst and I was like, I've got time, I'll talk to you. And he showed me, first of all, two things. He showed me this thing called Microsoft Excel. At the time everybody was using Lotus 1-2-3. And he showed me basically how you can type in a ticker and it pulls in all of the financial information right into this spreadsheet for you before the internet, but what was kind of the internet at the time.

I was like, wow, this is amazing. I was like, this could save me hours and hours of work. And so I went to the MD at the time and I said, hey, I think I can automate most of what the analysts are doing. He said, you're a summer intern. We're not paying you much. Go at it. And that's what I did. So I started off in that. But I mainly learned that I didn't want to do investment banking because it didn't hit on my core skill set, which was like.

engineering back down quantitative techniques and tools. That sounds really interesting. It's amazing to have that sort of experience as an intern. How did you land at Goldman Sachs?

Like everything in life that works out well, that's a lot of hard work, but mostly luck. Because of the DLJ experience, that was a good thing to have on my resume. Cliff Asness, founder of AQR Capital, managing partner there, at the time, I think it was late 20s, he was finishing up his PhD at the University of Chicago and was working for Goldman Sachs Asset Management.

he got the mandate to launch a new quantitative research group. And so he wanted to hire someone who had both the finance background and the computer science background. I had

I started with a couple of friends, a software business in high school. And at Penn, one of the things I did with my roommate was we started up a hardware business, kind of like Michael Dell, building and selling computers to faculty and students on campus. So I had the computer science background. Cliff had gone undergrad at Penn at Wharton also. So he knew that we'd taken the same kind of courses. We spoke the same language from that perspective.

and I had that technology background. So I was his first hirer as he was building out that new team. What my other colleagues did back then, you had basically three choices coming out of Wharton. It was accounting, investment banking, and consulting.

There was really no jobs for asset management, but those are the courses I love the most at Penn and really wanted to pursue that. So it was a great opportunity. So you spend three years or so at Goldman with Cliff. By that point, he had been there for a while and decided, hey, I think I have a little more freedom and opportunity if I launch a fund on our own.

You were there day one. You left with him, right? Tell us a little bit about what it was like standing up AQR with Asness. It was great. We started off, just a little background there, as a research group within GSAM. So think cost center and just putting some time frames around this. This is 1994, which is one of the toughest years in Goldman's history, even going back to the Great Depression. It was the kind of year where, to me and a partner, you had to put in money.

Wow. Was it that bad a year? I don't remember '94 as a terrible market year. That was the year where the Fed had the surprise significant rate hike in Feb. I was actually on the floor- I think bonds took a whack, but equities also wobbled a bit. Is that right? It wobbled a bit, but yeah, it was really a bad year for fixed income and the firm had a lot of risk in fixed income, I presume, which led to the tough year.

So we were a research group, cost center. And then left and right, people were disappearing week by week as they were cutting down headcount. And so quickly we realized we've got to start generating some revenue if we want to stay alive. And Cliff went to them and said, hey, we've built some interesting models. We think we're good at picking stocks and futures and things like that. We think we can trade on this and make some money.

he convinced the partnership to give us some money so it's basically a prop trading effort for a little while uh it did very well they kept adding money to it and then we opened it up and turned it into a fund it was really goldman's first real hedge fund uh coming out of gcm

That funded very well, which really opened the door for us to be able to leave and start up and raise money as an independent hedge fund. What were the specific strategies Cliff was running at GSAM with the partner's money? It was a multi-strategy approach, but it was all quantitative. And when I say quantitative, that means a lot of things to

to different people. I think about every good investment process is really a process and whether people would label as quantitative or not is really how automated it is. And so by quantitative, I mean like really automated downloading public data for the most part,

pumping it through some systems, and that causes you to want to buy and sell different instruments around the world. But you're still creating, or Cliff at the time was creating models, and the models would give him a ranked list of, hey, the top 10 stocks on this list of 1,000 are really, or

are things you want to look at either getting long or short based on whatever that model is. That's right. So that you'd have many different signals, and we're trading many different asset classes. And so it's like you're saying, all those signals, you would give different weights, different signals, and those would add up to, you like these things, you don't like these things. We would trade global equities in a bunch of different countries, but market neutral, so long as much as you are short. So you're not taking a bet on, is the market going to go up or down? You're really taking a bet on, this group of stocks is going to outperform this other group of stocks.

by looking at a bunch of different characteristics. We did that for stocks. We did that for currencies, for commodities, you name it. It was tradable and we had data. We wanted to be trading it. And that's really what the genesis of that fund was. How long were you guys doing that before you realized, hey, this is really going to be a successful model? And then how much longer was it before, maybe we should do this out from under the compliance regulations of a broker-dealer?

We started that as a fund really in 1995. It had been trading prop for a little time with Goldman's money. And we made money almost every month, basically. It traded as a fund. And I think we left them in terms of a timing perspective. You know, this started in 1995. We left early 1998. So it was only a couple years and change that we were trading this within GSAM before leaving to start up AQR.

So let's talk a little bit about AQR. You're there from inception, from day one. What was that transition like from, you know, I imagine at Goldman Sachs you have access to lots of support, lots of tools, lots of data, lots of everything. What's it like starting over again from scratch in a standalone hedge fund? I'll tell you a funny story. So I got into a few different battles with the administration folks at Goldman Sachs as management.

If you remember, in college, I had a computer business where we'd buy parts, build computers, and sell them. And so I knew how to build my own computers. Goldman Sachs at the time, the standard computer that everybody had was what was called an 8086. This was like the first...

real PC that IBM had out there and you know they were good but they weren't the most advanced available machines. Basically I went to the administration I said look we need the most advanced machines because we're trying to run a lot of computationally intensive models and this machine we have now is very slow it's taking very long to run our models.

You can buy the latest machine at half the price of what Goldman was paying and get twice the performance. What I didn't realize at the time is that when you're trying to run an organization that large and complex- They want everything standardized. You can't support it unless everything's standardized. And so there was a reason for it, which I didn't understand at the time. But you guys can support your own hardware. That's not that hard.

Cliff eventually persuaded them to let us get the new machines. But one of the big changes, as you talk about leaving a place, you have lots of resources and whatnot at large organizations, but you have limited resources at every place, no matter how big you are. There's always trade-offs that you're making. When you start as a new firm...

One thing that was a big change is that at Goldman, we had to support lots of other groups. We were providing research advice, investment advice, talk to clients, help them raise money in other products. When we launched our own hedge fund, all that mattered was making money in that hedge fund. So helping that focus was important, and we were able to buy the latest computers at half the cost. I'm going to bet that you did something a little beefier than those IBM 8086s.

Yeah, I was overclocking the machines. I was doing all the pulling all the ways to get things to go as fast as possible. Really, really interesting. So at AQR, you juggled a lot of responsibilities. You were a portfolio manager, researcher, head of trading and apparently tech geek putting machines together. What was it like juggling all these different responsibilities?

There's a couple things I'll say about that. So one thing just from a personal perspective, my wife and I, we have five children together. And that's a lot to deal with. My wife is amazing. And there's no way I would be able to do all the stuff I do at work if it weren't for her being amazing and handling everything at home. So that's the first thing in terms of how I get so many things done at work.

I'm also, from a personality perspective, I get bored very quickly. I like learning and doing a lot of different things. I like being able to jump around. So to me, that's just fun. The consequence is sleep. I don't sleep very much. What do you mean not very much? Uh...

And you know that only gets worse as you get older, right? We usually get to sleep around 1 a.m. Oh, really? And I'm waking up, you know, 6, 6.30, something like that. All right, so five hours. That's not terrible. It's not too terrible. I've lived on six hours most of my life. And you get older, that shrinks. I thought you were referencing the five kids because it's like, hey, when you have five kids, you learn how to juggle a lot of different things at once because something is always on fire. There's always something going on. That's for sure. What was it like working with Cliff back in the days?

It was fun. I think Cliff's great at a lot of different things, but one was he hired well. He was able to attract really talented people, and then he just let them do what they do. So he's not a micromanager. He just lets them run with it. And so that was a very fortunate thing for me, right place, right time, in terms of being able to get a lot of responsibility early on.

And that's how I was able to not just be a researcher building models and creating new strategies that I'd run by Cliff and he would say, okay, you're doing this dumb or doing that dumb and you're going to improve this. But also doing all the trading by myself for the firm for the first several years. And then eventually saying, Hey, Cliff, you know, I need some help here. We need to hire, you know, someone to run technology other than me. We need to, you know, hire more traders than just me so that I can actually sleep. So,

That's how he ran it, and it was a lot of fun. I mean, you mentioned it earlier on. I mean, Cliff's hilarious. He's a funny guy, and it's rare to find someone who is a quant...

who can communicate as eloquently as he can, and at the same time has such a devilish sense of humor. Like, that's an unusual trifecta right there. And it's part of what makes him fantastic as an individual, but also fantastic to work with and work for. It made the place fun, even in the tough times. Yeah.

And so that's a big reason why I think a lot of people stuck through lots of the ups and downs that any organization has. 89% of business leaders say AI is a top priority, according to research by Boston Consulting Group. But with AI tools popping up everywhere, how do you separate the helpful from the hype?

The right choice is crucial, which is why teams at Fortune 500 companies use Grammarly. With over 15 years of experience building responsible, secure AI, Grammarly isn't just another AI communication assistant. It's how companies like yours increase productivity while keeping data protected and private. Designed to fit the needs of business, Grammarly is backed by a user-first privacy policy and industry-leading security credentials.

This means you won't have to worry about the safety of your company information. Grammarly also emphasizes responsible AI so your company can avoid harmful bias. See why 70,000 teams and 30 million people trust Grammarly at grammarly.com slash enterprise. Grammarly.

Enterprise Ready AI. Trust isn't just earned, it's demanded. Whether you're a startup founder navigating your first audit or a seasoned security professional scaling your GRC program, proving your commitment to security has never been more critical or more complex. That's where Vanta comes in.

Businesses use Vanta to establish trust by automating compliance needs over 35 frameworks, like SOC 2 and ISO 27001, centralize security workflows, complete questionnaires up to five times faster, and proactively manage vendor risk.

Vanta can help you start or scale your security program by connecting you with auditors and experts to conduct your audit and set up your security program quickly. Plus, with automation and AI throughout the platform, Vanta gives you time back so you can focus on building your company. Join over 9,000 global companies like Atlassian, Quora, and Factory who use Vanta to manage risk and prove security in real time. For a limited time, our audience gets $1,000 off Vanta

Advantage.com slash special. That's V-A-N-T-A dot com slash special for $1,000 off. Let's talk a little bit about the AQR experience. The firm seems very, I almost want to say academic-ish.

They publish a lot of white papers. They do a lot of research. They have very specific opinions on different topics that seem to come up in the world of finance. How much of this intellectual firepower is part think tank and how much of it is just, hey, if you're going to have an investment perspective, you need to have the intellectual underpinnings to justify it?

So I think one thing that makes Acura very powerful is its ability to attract top talent, specifically on the academic side. The smart people want to hang out with other smart people.

There is definitely a network effect that happens there. And I would say part of the compensation you're getting indirectly by being in an organization like that is getting exposure to all these great minds that you can learn from, you can bounce ideas off of. So is it a think tank? Yeah, I think it is a think tank from that perspective. But at the end of the day, it's a business and they're there to make money, make money for their investors. So I think there is a lot of focus on that as well. So-

The publications, you see a lot of white papers, and sure, I would say it rhymes with a lot of things they do, but they obviously keep a lot of the special sauce unpublished and use that within their funds. But they're still writing about broad strokes. So let's talk about a white paper that you wrote titled The Evolution of Alpha. Tell us, how has Alpha evolved over time?

over the past few decades? Sure. This is a white paper I wrote from my Clear Alpha CIO, CEO hat. And it really talks about the history of the hedge fund industry, why different models of delivering alpha, starting with, let's say, single strategy hedge funds, fund of funds, multi-strategy funds, and now multi-

strategy multi-manager or multi-PM funds. And that's the latest evolution. And then we talk about what we think might be the next step, part of which we think we will drive.

So that's the point of the paper. And there's reasons why you went from different models from one to the next. And it has to do with a variety of things. I'd encourage you to read the paper. It's on our website. So let's follow that up. What were the drivers of the shift from a single manager to multiple managers to multi-strategy to multi-manager multi-strategy? What was the key driver of that?

Starting back, this is around 2000, let's say. Obviously, hedge funds existed before that, but that's really the point at which at least a meaningful amount of institutional investors actually started having investments in hedge funds as like a normal course of business. That was the year, obviously, that the market sold off a lot. There was the Enron fiasco and whatnot.

A lot of Wall Street was let go, so a lot of talent was being let go. And much of that talent was investment analysts, research analysts that covered stocks, knew stocks deeply, knew the management of those companies deeply. So if you're an investment analyst at a Wall Street bank, you go off and...

hang up a shingle, start a single strategy hedge fund where you're picking stocks. You had an argument for why you'd have an edge because you knew these managers and these stocks deeply. And that's really was like a Cambrian explosion of hedge funds at that moment in time. And even to this day, I think in terms of like sheer number count, the vast majority of hedge funds are really stock picking hedge funds, long, short. 11,000 hedge funds out there today. Yeah. Long, short discretionary equity, stock picking hedge funds. That

models survive for a little while, but as investors were investing in these individual kind of single strategy, single style hedge funds, what they realized is that any one single approach is not very consistent. It's going to go through its good periods and its bad periods. And it was hard to hang on to what I would call or be exposed to what the line item risk is. And when you have these quarterly reviews of what's going on in the portfolio, invariably the discussion is, let's talk about the things that are down the most.

And that leads to firing managers when they're down, usually just after an environment that was just bad for their approach before it rebounds and does well in the next year. So that model, while it still exists today, is tough from an investment to stick with.

Then you switch to fund-to-funds. And institutional investors, you know, one-stop shop, buy into a fund-to-fund, you can get exposure to many different strategies and styles in one vehicle. That's what came out of that, and it was to address this inconsistency. So fund-to-funds were more consistent than a single strategy fund, but I would say the consequence or the issue really is both for fund-to-funds and really for portfolios of hedge funds that investors have.

It's cash inefficient. It's capital inefficient because most hedge funds have a lot of cash on their balance sheet. Typical hedge fund, it varies, but depending on the type of style and strategy, will have between 40% and 90% of the money you give them just sitting in cash. Really? That's a giant number. Half is a giant number. I thought you were going to go in a different direction. I have a friend who's an allocator at a big...

And he calls the fund to funds fund to fees because you're paying layers on top of layers of fees. And it definitely acts as as a long term drag. But I never would have guessed that 50 plus percent of.

assets handed to hedge funds or in cash in any one time. I always assumed it was the opposite, that, all right, they're like the 130-30 funds or whichever variation you're looking at. I always assume that they're leveraged up, and even if they're long short, all that money is put to work. You're saying that's not the case. Well-

Technically, they will put the money to work in the sense of it's not pure cash sitting there, but really there's a lot of borrowing power. You'll have assets that you're holding. There's a tremendous amount of borrowing power you can borrow against those assets that you hold to then create a more efficient portfolio. And that's where kind of multi-strategy funds evolved. So multi-strategy funds gave you the benefit of many different strategies and styles, yet put into the same vehicle, all these positions held in the same vehicle to get much more cash efficiency, capital efficiency, higher return on capital.

plus the consistency. So I'm assuming if you're using a multi-manager, multi-strategy approach, anyone...

strategy at any given time is either going to be doing well or poorly, but the overall performance of a multistrat will offset that. So it's not like, hey, this guy has a bad quarter because what they do is out of favor and the clients pull out their cash just before the recovery. Is there a tendency to leave money with a multistrat, multimanager approach for longer? And so you don't have those sort of...

Bad quarter, bad month, whatever it is, because this just isn't working now, but it'll start working eventually. Is that the underlying thinking? That's really the approach. In fact, a lot of successful single manager businesses evolve to the multi-strategy approach because they recognize that.

that lack of consistency for a single approach, a single investing style was a threat to their own business. And so expanding into other strategies and styles is how a lot of these more successful single strategy funds evolved. So it sounds like if you're running either a multi-manager or a multi-strategy or both,

everything needs to be very non-correlated. You don't want everything down at the same time. How do you approach picking various strategies that are not correlated? That's a great question. I think it's helpful.

I don't like the gambling angle, but I think it's a helpful analogy because most people are used to the analogy. If you think about the casino, people go to the casino knowing that if they play the games long enough, they're going to lose their money. I think most people think that.

The multi-strategy hedge fund is really like the house, where each table or each game in the casino, in their house, has a slight edge. And if they make sure that there's not going to be massive losses at different tables on the same night, same weekend, same month, over time, they will just...

just statistically, accrue profits in a more consistent manner. So that is a big focus. And if you think about what risk managers would do at a casino, it's the same thing. They're going to make sure that these tables, these games, are not going to be making or losing money at the same time. So let's talk about some of these diversified, non-correlated strategies. I'm assuming some include momentum, long, short, any other sort of approaches that

people would really readily understand? - Sure. When I think about most hedge fund strategies, the ones that people know about, the ones that there are, if you look at hedge fund indices, there's a category for it. - Right.

So it could be long-short stock picking. It could be merger arbitrage. It could be index-rebalance arbitrage or basis trading. There's a variety, and there's like dozens of these kind of well-known, well-understated... Activists. Activists, exactly. These are all out there. They're well-known. When you look at each one of those, you can break it down between kind of cheap passive beta. So let's take an example. Long-short discretionary stock picking. Most of these hedge funds, the way they're implemented is...

the managers net long the stock market. And so some portion of their returns, it's actually a pretty significant portion is just being going to be driven by whether the stock market's up or down. Just pure beta. Pure beta. And that's, that's a, I think about the scarce resources, your risk budget and how do you want to allocate that risk budget? If you're allocating a lot of your risk budget to just pure beta and

That might work for the manager, but for an investor, that doesn't make a lot of sense because I can go and get pure beta. I can buy an index fund for single-digit basis points at this point. It's effectively free.

These multi-strategy funds, in order to reduce the correlation across their managers, they don't want to have all these managers long, pure beta. That's a common risk that will cause them to make and lose money at the same time. And so when you're running a multi-strategy fund, it's really about looking at these common risks. Beta is the simplest example. It could be sector exposure. It could be factor exposure, like momentum you mentioned earlier. And there's a lot of other...

Less well known, but known in the industry, risks that take place. People talk about crowding. There's reasons why crowding happens. So being able to be aware of those and look for signs of that and trying to mitigate those commonalities across your different strategies is a really key component to managing risk for these multi-strategy funds.

There's so many different ways to go with this. So you're implying with these crowded funds that there's a way to identify when you're in a crowded fund. I recall the quant quake a couple of years back where all these big quant shops –

post-GFC really seemed like they were having the same sort of exposure and the same sort of problems. How can you identify an event like that before it takes your fund down 10%, 20%? That's a great question. And I would say a more recent example might be COVID, March of 2020. So I talked about a couple of different common risks. One is beta. Another one might be factors. A simple other one is just

there's a well-known strategy. Let's say merge arbitrage. There are plenty of funds that are running merge arbitrage as one of their strategies within the fund. Simply because a lot of people are doing something that, in a sense, when there is some other exogenous event that causes people to de-risk, it actually makes it bad to be in well-known, well-understood trading strategies. So that...

You know ahead of time that this is something that is crowded. You know that there are other players that are doing the same kind of trades as you going in.

That's really interesting. And just to put some meat on the bones, multi-strategy, multi-manager, multi-model funds have really gained prominence lately. Names like Citadel, .72, Millennium, lots of other larger funds have very much adopted this approach. Fair statement? That's very fair. I do think it's the best way to deliver alpha.

So you're reducing correlation, you're reducing risk, you're increasing the odds of outperformance. How broad are firms like Citadel or Millennium that they don't run into that crowded trade risk? You would think given their size and their tens of billions of dollars, a crowded trade becomes increasingly more likely, right? Right. And there's a reason for why that's the case.

There are literally thousands of different types of ways to make money in the markets. Thousands.

but there's only dozens of ways of making money in the markets that have lots of capacity. It means you can put a lot of dollars in general, a lot of dollars to scale up. And if you're going to be a very large fund, you by definition have to put more and more of your money into the well-known large trading strategies. And so they have to be particularly attuned to the fact that they are large and their competitors are also large and then they're the same kind of trades. So what is at risk? And when one of these shops opens,

sells out or reduces risks in one of these common strategies, it's going to affect the other ones. It's hard to avoid that, but they are fairly well diversified across many different types of strategies. So that's why you see still very consistent returns, but there is this exogenous risk element of having being big in the crowd. The way you avoid that is by being smaller, focusing on smaller strategies that are a little bit different.

Huh, really interesting. So you mentioned earlier, early days of hedge funds, the fund-to-funds were popular. It feels like they're kind of going away. You certainly hear much less about them these days. Is that a fair assessment? Just because you don't hear about stuff doesn't mean it's disappeared, but I certainly read much less about fund-to-funds. They are in the news much less, have multi-manager, multi-strat, multi-model

Broad funds replace the concept of fund-to-funds? I think it's an evolution. It doesn't mean that the fund-to-funds model is going away entirely. There's certain managers out there who have commingled vehicles that only, you know, they won't run an SMA for you. They won't trade their strategy into your account. Fund-to-funds can access that. So there's a reason for that.

And, you know, they're nice one-stop shops and they can maybe a little more transparent. But there are, you talked about this earlier, the fees being an issue. And it's really about the fee as a percentage of the dollars of P&L being earned. There was an academic paper recently published that did a really interesting study over 10 years of looking at institutional hedge fund portfolios.

What it showed is that for every dollar of P&L being generated by these hedge fund strategies, at the end of the day, the institutional investor took home about 37 cents.

Really? Which is, I think, a shocking number for a lot of people. Right, right. So you're saying almost two-thirds of the money never, either it's fees or costs or some other factor, but only a little more than a third ends up with the actual investor. That's right. And it's really interesting. It breaks down the...

The sources of all these things, part of it is fees and double layers of fees and things like that. A big part of it is the behavioral nature, which I think is driven by governance of investing organizations where- Filled with humans. Yes. Strategy's down. What's been down? Let's get out of that. Let's get into the thing that's been up recently. That costs about a third of your alpha. That doesn't surprise me at all. Even though you expect big endowments and foundations and hedge funds to be smarter than that,

Fill them with people and you're going to get those behavioral problems, aren't you? Yeah. Well, there's agency issues in between. And I think investors are well aware of these. So that causes part of it too. But a big thing, and I think that kind of the multi-manager, multi-strategy approach is

that a fund of funds can't is you get a lot of netting benefits both from, you know, one manager's long Apple, another manager's short Apple. Right. In a fund of funds approach where you're investing in two different funds, well, A, they don't know that. And B, the manager's long Apple, they're paying a financing spread to go leverage long Apple and the manager's short is paying a financing spread to go short Apple. A lot of costs built in. You're paying a lot of extra costs there. Just to be net flat. Just to be net flat.

So if those two managers instead traded those positions into the same vehicle, you're getting that efficiency. And that's worth, you know, on the order of like 23% per year, just that alone. The enhanced risk management you can get by having daily position transparency and all the trades of all the different PMs are doing, being able to hedge out all these beta risks, factor risks, sector risks, things like that, allows you to be much more efficient with how you deploy that capital.

And so you see that these multi-manager funds tend to be a little more invested than a hedge fund portfolio typically could be. And that creates a lot of efficiencies. And so when you look at the returns that they're generating, it's closer to like 50-50, where for every dollar that's generated of P&L, 50 cents is going to the investor. So it's a much more efficient delivery mechanism of alpha.

89% of business leaders say AI is a top priority, according to research by Boston Consulting Group. But with AI tools popping up everywhere, how do you separate the helpful from the hype? The right choice is crucial, which is why teams at Fortune 500 companies

Use Grammarly. With over 15 years of experience building responsible, secure AI, Grammarly isn't just another AI communication assistant. It's how companies like yours increase productivity while keeping data protected and private. Designed to fit the needs of business, Grammarly is backed by a user-first privacy policy and industry-leading security credentials.

This means you won't have to worry about the safety of your company information. Grammarly also emphasizes responsible AI so your company can avoid harmful bias. See why 70,000 teams and 30 million people trust Grammarly at grammarly.com slash enterprise. Grammarly, enterprise-ready AI.

Join Bloomberg in Atlanta or via live stream on February 11th for The Future Investor, Finding the Opportunities. This 2025 event series will examine how companies are investing in their businesses to create efficiencies, innovate their products and services, and improve the customer experience. This series is proudly sponsored by Invesco QQQ. Register at BloombergLive.com slash Future Investor Atlanta.

So we were talking earlier, and I mentioned off air that the funny element of individual investors tending to underperform their own investments. I know you've done some research on that. Tell us a little bit about what you see. Yeah, this is really something that's very important to me when I think about the industry and what are the big problems that are facing the industry? What's really...

causing investors not to get as much money in their retirement accounts as we possibly could get there. One of them is this behavioral issue, which I think also ties to incentives and governance and agency issues within investing organizations. Morningstar does a study that they call Mind the Gap, and they do it on a regular basis. Some of your listeners might have heard this, and it's definitely worth reading. I'll quote some numbers off the top of my head. I might be remembering it incorrectly, but what it does is it's measuring the

time-weighted returns of funds, which is the returns that the funds report. These are the returns that if you invested a dollar at the beginning and you held it all the way through, the returns you would have gotten if you never went into or went out of that fund. Then they compare that to the asset-weighted returns, right? And that is going to, the asset-weighted returns are, you know, accounting for the fact that, you know, the fund does well, everybody gets excited, money comes in.

and then it maybe does not as well after that. And so the larger assets earn less return. And so the asset weighted return minus the time weighted return is a really good way of measuring what's the actual impact of this behavioral element of investing, which is a really critical part of investing. And the gap refers to the behavior gap, which is the difference between what the fund generates and what the actual investors are getting. Yeah. Please continue. And so what you find is that for investors

60-40 balanced funds, which typically are in retirement accounts where people maybe aren't looking at them every single day. They get statements once a quarter that are delayed. Set and forget. It's kind of a set and forget. That gap is on the order of 60 basis points. Relatively small.

Relatively small, but it costs 60 basis points a year for the average investor for those simple funds. Now, for alternative funds, when they look at those, that gap is 170 basis points a year. Okay, that's starting to add up. That really, I mean, if you think about that compounding over a decade, that is a massive hit to wealth.

Why is there such a big gap for alternatives and not as much of a gap for the 60-40? I think it has a lot to do with investor understanding of what those products are and therefore the confidence. People invest in alternatives. They don't necessarily understand them. And so you're setting yourself up for failure a little bit there because when it has bad performance, you don't understand what it does. You're more likely to redeem. That

That makes a lot of sense. So to me, investor education, really understanding what they're investing is, is a critical component to being a successful investor. Really interesting. So you talk a lot about idea meritocracy. It's on your site. You've written about it. Explain a little bit. What is idea meritocracy? This is a really important part, and it's a part of our culture at Clear Alpha.

The idea is to get all ideas surfaced so that the organization can make the best decisions. What prevents good ideas from surfacing? One is that people may not know that a question is even being asked. So many organizations are run fairly siloed, different groups. And a lot of that happens, especially at large organizations. It's hard for everybody to be constantly communicating with one another.

So just not even knowing a question exists. So the way we address that is that we use Microsoft Teams at the office and most people are in various channels and we're seeing questions going on all the time. I really discourage people from asking me a one-on-one question. I will usually redirect a question someone asked me to here's the broad company, here's the question that was asked, here's the answer. So then immediately the entire

company learns what this topic was. And very often that says, oh, someone else, I have another idea about that that I want to now share. So getting accessibility for people to deliver. But the most important about idea meritocracy is really from a leadership standpoint. People have to feel safe bringing up ideas that they're not going to get yelled at. There's no bad questions there.

There's only people not asking questions. That's what's bad. And the only way for people to feel safe about that is that they need to see me as the leader and my other partners as the leaders

to be willing to take in feedback, be challenged even publicly, and say, you know what, that's a really good idea. Let's go with that. And so just having them feel that safe environment so that people can always ask and bring questions up. Huh. That's really interesting. Also, you've discussed generating less common ideas. Earlier, we were talking about crowded trades.

How do you generate less common ideas? How do you find non-correlated sources of return when you're in a hyper-competitive marketplace? Great question. So I'll use an example here. There's a common strategy that people might be familiar with. It's called merger arbitrage. And basically, company A is going to buy company B, whether it's for cash consideration or a stock-for-stock type transaction.

And, you know, merger arbitrageurs look at that and they might go, you know, long the company that's being acquired, short the company that's being acquired, and then make money if that deal ultimately closes. That's a very common, well-known strategy.

That would be the common version of implementing this strategy. A less common version to implement is you try to find ones that you like more than others. So you might think they all or like the vast majority are going to close, but some you might like better than others. And so you could go long half of them and short half of them. So you're not exposed to this common element of merger arbitrage deals closing. You're neutral to those. So if

a large pod shop, one of these large multi-managers, if they decided to get out of merger arbitrage and they're selling all these positions down, half your portfolio will get helped and half your portfolio will get hurt. But you're less exposed to that crowding risk, that common, what I would say, risk factor that these other common strategies have. So that's a niche version of how we might implement that kind of a strategy. You mentioned niche. I never heard the phrase prior to reading something you had written called niche alpha. Yeah.

Tell us a little bit what niche alpha is. That's a great question. The simple answer is you're unlikely to have any or much of it in your hedge fund portfolio. That's how I would describe it. And so it's looking for people that are either implementing common strategies in a very different way that

makes them less susceptible or more immune to people getting out of that strategy. Or people have a completely different idea of how to make money that I haven't heard of before. And I've interviewed hundreds, if not thousands of portfolio managers and worked with, developed many strategies of my own. So it's trying to find things that people aren't doing. Yeah.

Is there, given what we know about the efficient market hypothesis, and Gene Fama was Cliff Asness' doctoral advisor, is that what, or MBA advisor? Cliff was Gene's TA. Yeah, so given how mostly efficient the market is, are there really niches left that have not been discovered? How many more opportunities are out there that we don't know about? That taps into something we talked about earlier, right?

which is there are thousands of ways to make money in the markets. There's only dozens of ways to make money in big dollar size in the markets. At scale. So these smaller ideas, is that where the mostly kind of eventually efficient market hasn't quite reached yet? Well, what I think about is...

The amount of dollars you can make, this is a ratio I think about, the amount of dollars you can make divided by the complexity or how much brain damage you have to inflict upon yourself to actually implement the strategy. A lot of these small strategies, they're complex and difficult to do. They might require some kind of new technique that is difficult or rare to implement. And the actual P&L that you can generate, profit loss you can generate, is small relative to that effort.

Small in terms of percentage returns or small in terms of, hey, there's only $100 million to arbitrage away with this. And once that is mined, that's it. It's done. It's about dollars of P&L you can extract from the markets per year. Percentage returns can be very high for these strategies, but I'll give you a sense. Most other large shops, they're going to look for strategies that can generate at least $100 million of P&L to make it worth their while to invest. We're looking at strategies that are generating $10, $20, $30, $40 million per year.

That's really kind of intriguing. So what sort of demand is there for

lower capacity strategies. I mean, so you guys are less than half a billion dollars. You're not an enormous fund. Are there more hedge funds looking to swim in these ponds? Or is this something that, hey, once you cross a certain size, you just have to leave behind and stay with those larger capacity scalable strategies? Yeah, I think this is a general thing for all investors, not just other hedge funds.

Everybody wants to be in the interesting things. They want to be in the lower capacity things. They know that they're less crowded. The difficulty and really what I think kind of our business model is, is you're paying for us to go out and search the world and source them because it's expensive. It's expensive exercise to do. People might not have the expertise or the background to underwrite these types of strategies. It just takes a lot of work. And at the end of the day, alpha is either about being smarter or working harder and

The being smarter can work in the short term, but eventually that does get out of the way. Eventually someone smart enough comes by. The working harder to me is the thing that actually stays. - Huh, that's really interesting. You would think if the incentive was there, enough people would just eventually grind away in that space. I mean... - The incentive's there, it's just not enough to be worth the time. And so if you are a very large investor, very large organization,

You do have to prioritize. You still have limited resources and time to look for things. So you're going to have, you know, thresholds. I'm not going to invest at least, you know, at this amount of dollars.

And that's where we step in is kind of fill that gap. So you're very much a student of what's going on in the hedge fund world. What are you seeing in terms of strategies driving costs down and the question of where fees are? They've certainly pulled back from the days of 2 and 20. What's happening in terms of efficiency and cost? There's a bunch of things to talk about there. So first thing I would say is the higher capacity strategies that have become well known, I think that those...

costs are going down because there's a lot of people who can implement those strategies. And so you think just simple supply and demand, lots of portfolio managers, you can do them. And so then it's just a competition of who's going to be able to do it most efficiently. Then there's a unique alpha. I think that's harder. And actually the cost of that has gone up over time. It's not gone down. The cost it takes to compete in the space has increased over time. And so there's a bifurcation that's been going on. We think that

There's still a lot of efficiencies you can carve out of the system that exists now that we're attacking. A lot through technology, a lot through ways of working that can just make the organization more efficient and deliver more net returns to investors. So we've seen some motion towards fees for alpha, not beta. Some people call it pivot fees. There's like a lot of different names for this. I haven't heard much about that recently. What are your thoughts on where hedge fund fees are going in the future?

I'll answer that with a different story that will draw an analogy here. With the rise of indexing, which has been happening for decades now, and thank God for indexing. It's a fantastic invention that has helped a lot of investors. The original thought was, well, as the market goes more and more indexing, and I don't know what the number is, it's probably 70% is indexed of the invested dollars. Then it makes the markets, you know, it's easier to make money because there's less people trying to compete for that. Mm-hmm.

But that's not what actually happens. What actually happens is it's become more and more difficult to make money because the talent pool is of higher quality now than it used to be that's searching for that alpha. And just like sports...

When there's a zero-sum game, and it's very small differences between the number one person and the number five person, what you see is the rewards and the compensation tends to be a power law, meaning that the very few get paid a lot. And I see for pure alpha, where there's real competition, that the investment talent will actually get paid more and more over time. It'll get more and more difficult to be that person.

Whereas for the common stuff, the well-known things that have higher capacity, I think you're going to see fees keep going down on that side. Michael Mobison calls that the paradox of skill, that the more skillful the players are, whether it's sports, investing, business, the more of a role luck plays, which is really kind of fascinating. You've also written about portable alpha. Discuss portable alpha. What is that and how can we get some?

So I think Portable Alpha is a great way for investors to get exposure to alternative return streams.

What Portable Alpha is, is mixing a beta like S&P 500 exposure with an alpha stream and really just plopping that alpha stream on top of the S&P 500 returns. So it lets investors get exposure to S&P, which most investors already have, but now exposure to a different type of return stream. Usually people, historically at least, have tried to be the S&P by picking a manager who's trying to pick stocks, overweighting stocks that they like versus the index and underweighting stocks they don't like.

But that comes with a lot of constraints. One is the manager can only overweight and underweight stocks in the index. They can't trade other asset classes. They can't, you know, utilize any kind of sophisticated investment techniques to try to beat that benchmark.

Portable Alpha gets rid of all of those constraints. And so what you typically see is Portable Alpha programs are much better at consistently beating traditional active programs. I like the phrase Corey Hofstein uses for that, return stacking. Is that same concept as Portable Alpha? That's right. Yeah, really, really interesting. Before I get to my favorite questions that I ask all

Well, my guest, I just have to throw you a little bit of a curveball. So you're a member of the Yale New Haven Children's Hospital Council. Tell us a little bit about what you do with that. Sure. So just how we got involved, my wife and I, we have five kids, three of which had severe peanut allergies. And we were very concerned about that. You know, that's become...

a rising epidemic within society over time. And we wanted to see if we could solve that, invest in basically research to try to solve this problem. So we worked with both Yale and our local hospital to, can we fund a research effort and a clinical effort to basically collect data? Because a lot of the research really needs data. So we worked with them, and that's how we got originally involved with Yale as an organization.

And then they have this council that's focused on children's health issues. And what it is, it's a collection of individuals who are interested in this topic. We meet typically quarterly. They'll have some of their top researchers from Yale come in and talk about whatever research they're working on and their clinical experiences with children as patients.

And that usually generates ideas. Okay, how can we make this more effective? How can we get more funds directed toward this activity? All right, we only have you for a couple of minutes. Let's jump to my favorite questions that we ask all of our guests. Starting with, what are you streaming these days? What's keeping you entertained? Either Netflix, podcasts, Amazon, whatever.

My wife and I, after going through the litany of all the kids and their issues each day, it's usually very late. And so we don't get to watch as much TV as you probably would like. There's a lot of great content out there. Lately, we're watching Lioness on Paramount, which is... I just finished season one a few weeks ago and taking a break before season two, but it's fantastic. It's fantastic. Yeah, we've really enjoyed it so far. But...

But I would say... Are you up to season two yet? No, we're three or four episodes in to season one. Brace yourself. You have quite a ride. Okay, great. But in terms of favorite shows, one of my favorites was the remake of Battlestar Galactica, which was a show when I was growing up as a kid. With terrible special effects in the old one. Yes. And the new one is great, right? That's right. And there's a scene that's actually relevant to our conversation a little bit today where

The leader of the Cylons, which is like the robots, is talking with a human. This is one of the fighter pilots. And they're watching a video of one of the battles. And the humans win this battle. But then the Cylon says, this is how we're going to beat you. And the human's like, what do you mean? Because they just watched like one of the humans kill one of the robot fighter pilots.

And she says, well, every time that we make a mistake and we lose a battle, every single other Cylon learns from that. And so inevitably, we will learn every way that we can avoid dying and we will take you over. And that has a lot to do with how we approach the business on the investing side.

Always learn from your mistakes, get the communication out there, and constantly improve. If you improve by a few percent a year, that really compounds over time. Well, what does it matter if the AI Cylon's eventually going to kill all of us? It won't make any difference. Alpha is only here until the Cylons beat us in a space battle. Yeah. We view it...

That's way off in the distance. We like intelligence augmentation versus artificial intelligence, IA instead of AI, using these tools to be more effective. That makes a lot of sense. Let's talk about your mentors who helped to shape your career.

Well, I would say of all the ones I could think of, Cliff would be the top mentor. And Cliff wasn't the kind of guy who would put his arm around you and say, hey, this is how you do X, Y, and Z, and you should do this differently. He did have several conversations with me like that. Most of his mentorship was through his actions. Cliff's extremely principled, very ethical, and it's

It's a very fortunate thing to be able to be in business with someone like that where you can be successful at business but do it in a very ethical, principled way that's always doing right by the client. And that's something – it's one of the biggest things I've taken away from working with him. Let's talk about books. What are some of your favorites and what are you reading right now? I like –

specifically financial history. The one I'm reading right now is called The World for Sale. It's actually written by a couple of journalists that cover the commodities industry. And it's really about the physical commodity traders and the whole history of that, which is kind of interesting. I love biographies. One in particular I liked was the Michael Dell one, Play Nice But Win, where it's kind of chronologically his whole story. I really connected with

the building computers in his dorm and selling them. Obviously he was much more successful at that than I was. Really interesting. Any chance you read McCullough's Wright Brothers? I have not. Really fascinating. I like, it's unusual to read something that you think about

"Oh, I know that history." And then it's like, "No, you have no idea what's going on in that history." And he's just a great writer. Really, really, really interesting. Our final two questions, what sort of advice would you give to a recent college grad interested in a career in either quantitative or investment finance? I don't know if the advice would be specific to those things, but talk less and listen more is what I would say.

There's a curve. I forget the name of the curve, but you start thinking you know a lot. Dunning-Kruger. Yeah, Dunning-Kruger. That's what it is. That is such a true effect. I thought I knew everything. And if I just listened to those around me who knew a lot more, people are trying to help you more than you realize as a young person. And I should have just listened to more advice. I would have been more successful much more earlier if I had.

So here's the funny thing about the Dunning-Kruger curve, and this comes straight from David Dunning.

They did not create the Dunning-Kruger curve. It kind of came from just pop psychology and social media. And then when they went back and tested it, I think the paper was like 99 or 2004, something like that. When they went back and tested it, it turned out that the Dunning-Kruger curve turned out to be a realistic measurable effect.

And it's Mount Stupid, the Valley of Despair, and the Slope of Enlightenment are just sort of the pop terms of it. But it's really, really funny. And our final question, what do you know about the world of investing today you wish you knew back in the early 90s that would have been helpful to you over those decades?

There's a lot of smart people out there. As smart as you might be, there's a lot to learn from everybody else. Everybody has some insight, some perspective that you don't have. Don't presume how...

that you know what people are thinking. So ask questions and listen. Sounds like good advice for everybody. We have been speaking with Brian Hurst. He's the founder and CIO of Clear Alpha. If you enjoy this conversation, well, be sure and check out any of the 530 we've done over the past 10 years.

You can find those at iTunes, Spotify, YouTube, Bloomberg, wherever you find your favorite podcasts. Be sure and check out my latest podcast, At The Money, short 10-minute conversations with experts about topics that affect your money. Spending it, earning it, and most importantly, investing it. At The Money, wherever you find your favorite podcasts.

I would be remiss if I did not thank the crack team that helps us put these conversations together each week. Sarah Livesey is my audio engineer. Sage Bauman is the head of podcasts. Sean Russo is my researcher. Anna Luke is my producer. I'm Barry Ritholtz. You've been listening to Masters in Business on Bloomberg Radio.

Join Bloomberg in Atlanta or via live stream on February 11th for The Future Investor, Finding the Opportunities. This 2025 event series will examine how companies are investing in their businesses to create efficiencies, innovate their products and services, and improve the customer experience. This series is proudly sponsored by Invesco QQQ. Register at BloombergLive.com slash Future Investor Atlanta.