The Exchanges

Q What was it that led you to go from private equity to public equity?

A Private equity, it's a patience game. You do a lot of work, three months potentially on a single company. You learn a lot, but often things don't happen. There's a lot of binary outcomes at the end of three months of work. As soon as I was in public markets, I think the fact that you get multiple opportunities to use your knowledge, either long or short, and that steady flow of opportunity set, in a sense, And your ability to monetize your thinking both ways was extremely appealing. And really you're pitting yourself against other minds, similar to chess. I think there was something more like chess about it that appealed. Whereas in private equity, you have to create it, which is fine. It's entrepreneurial, much more process driven, but hopefully you do a deal a year.

AI assessment note: “steady flow of opportunity set, in a sense, And your ability to monetize your thinking”

Q What was your path from going through university into finance?

A Met somebody in my university time that we discussed a lot. What do we want to do with our lives? Where do we want to go? That person is Rilof Beuter, who runs Sequoia globally. He went on his path, and I went the London private equity, long short equity path. But I think we were motivating each other, or at least discussing what's interesting, what's challenging, where's the opportunity set. Coming full circle back to chess, by the time I was in my late teens, it was obvious that there was some limit to how far I could progress in the chess world. Maybe you can get to the top 50 if you live and breathe it, but that's as good as I would have been able to achieve. Whereas I'd gone to private equity in the late nineties. It was a cottage industry. It's hard to imagine today, 25 years later, that private equity was literally teams of five or 10 people doing handcrafted deals, and the fund sizes were all one to five, and the firms were nascent in terms of just where they were in their development. And I think long, short equity It's similar. At least in Europe, it's still somewhat unproven. Fundamental, long, short, single manager. There's just not many of us left in a way. A lot more opportunity to not be number 50 at best, but rather excel and get to the top.

AI assessment note: “I went the London private equity, long short equity path.”

Q Before we dive into how you do that, I'd love to chat about fundamental long-short equity, which over the last decade, maybe longer now, hasn't really delivered en masse what investors had expected. You've seen fund flows, index funds, all this kind of stuff. What's your sense of how the long short equity model should work?

A I think there are different models that can work and that platforms have clearly exhibited success in one type of model. I think on the flip side, when you look at the single manager construct, I think there's been hidden flaws in what most single managers gravitate to that isn't entirely obvious. And over time, The lack of success there, it's been relatively consistent. It comes down to portfolio construction and short alpha. And I think a little bit, the simple way to do the math is, if you're running a one 50 gross long short fund, and you've got good long alpha, but no short alpha, and you end up with one 50 50, what I mean is one 50 gross, 50 net exposure, that means you're a hundred long, so you're a long manager on that side of the balance sheet. And on the short side, you're running a 50 short book, which produces no alpha, which means you're basically short an index. So you're running a one 50 gross product, which is effectively a long product with a short 50 index. Over time, why is that worth anything? And that very elementary math is at the heart of the problem. Too little gross, too little short alpha, and you end up being a diluted good long manager. Again, I think when markets are healthy, your lungs are working, that product seems okay, but have some flat years in the markets and some challenging years where you're just short the index and the product doesn't wo…

AI assessment note: “Too little gross, too little short alpha, and you end up being a diluted good long manager.”

Q I'd love to turn to how you take all of these philosophies about people and portfolio and put it into practice at Gladstone. Let's walk through some of that investment process. So first, how did you decide what your investment universe would be?

A My background is financials and TMT. So those have tended to be the big areas where we focused on. We've extended that to consumer, which is touching TMT. So those Today are the biggest three areas, and then over time you can do smatterings of things as add-ons, some industrials, some healthcare, but really deciding what your core areas of expertise are, and getting better at those, and building the organization around those, building a culture of excellence around those. So that takes an awareness of what am I going to do and what am I not going to do, what sectors, what market caps I'm willing to invest in. My background's always geographically been London, so European equities has always been an obvious investment place for us. The U.S. is such a big, deep market, and we try and use what we see in Europe or our understanding of the world and apply that to the U.S. And then I grew up in South Africa, so that tends to be a market I play in, and I lived in Australia, so that tends to be a country we do things. And beyond those, it's de minimis. So stick geographically, To what you understand, and stick from a sectoral perspective to what you understand, and that's led to a risk framework where we've, much like the platforms, we're measuring exposures by country, aggregations of certain countries, by sectors, by sub-sectors, by factors, so there's very much a risk system framewo…

AI assessment note: “stick geographically, To what you understand, and stick from a sectoral perspective to what you understand”

Q What are some of the other parts of your framework on the short side?

A As I alluded to, external environments are really understanding where we are, and I asked the team to think in ten-year timeframes. If you look at the company, what environment it's in today, how does that compare versus the last 10 years? So I'll give an example. As we speak, regional banks in the US, if you look at the environment they're operating in today versus any point in the last 10 years, it's the worst environment they've had. The regulators obviously tightening up, following SVB, Rates have unstuck the low deposit rate culture that's helped the banks over the last at least 40 years. So now that interest rates have moved up and customers actually expect to get a return on their deposits, that's meant the cost of funding has gone up. That hasn't been present, never mind the last 10 years, hasn't been present for the last 40 years. It's been great that there's been such low loan losses, there's been very little credit risk in the US system over the last decade. But over the last 50 years, that's not true. At some point, you can't run a bank on the basis that you're gonna have 30 basis points of loan loss provisions forever. It's not true. When that changes and how that changes, it's gonna happen. It's just a question of when. They've over-invested in their cost basis, and they're behind on tech. So there's just a bunch of environmental factors that mean the entire regio…

AI assessment note: “external environments are really understanding where we are, and I asked the team to think”

Q What's an example of, in the investment realm, what it takes to be able to excel in that way that you feel sometimes people don't get to?

A Changing your mind, being intellectually flexible. I think a lot of people get into, I'm right, and the idea that just toggling other potential outcomes, possibilities, approaches, There's an inertia, there's a stubbornness. I think that's quite hard for people to navigate. If you think about it, in any professional sport, there are times when the individual, the team are not performing, and they probably need to change something, and somebody needs to drive that change. Just because they worked before doesn't mean they're going to work in the future. So being able to be dynamic enough, and open enough, intellectually open enough, Emotionally open enough. Maybe this has just changed. Let's just look at the facts anew. I'd say data is something that people struggle with. Embracing data seems to be a challenge, at least in the long, short equity space. I'm sure I'll hear a lot of people say, no, that's not true, because they scrape data. But it's as simple as, okay, you keep trying to short this sector, and there's no alpha doing that, and you've been trying to do that for three years. It just shows that, is this a good idea? And it's like money ball. And if you can actually dissect performance into pieces of And really analyze not just your longs and your shorts, but let's disaggregate that down into sectors. Have you managed to prove that you can generate alpha in this sector r…

AI assessment note: “Changing your mind, being intellectually flexible.”

Q What are some of the tools or frameworks you use to assess management?

A Look into their background. We look for alignment of their skill set with the job that they've been hired to do. And you'd be surprised how many really good companies just hire the wrong people to run them. You've got to develop an eye for that. That's just the wrong person. They're not going to be able to do that job. It's coaching people to develop a sense of what does it take to run a business, and is that CV the right one? It's rare to see lateral hires, hiring people from totally different industries, coming in to run businesses. The Adidas CEO was previously at a chemicals company. That's not a natural evolution in my mind. He was highly regarded in the chemicals industry. He had no experience that we could tell in the consumer industry. And some of these things are happening more regularly and frequently than we realize. It took Microsoft three goes to replace Bill Gates. They got it right, eventually. Getting under the hood and observing whether somebody's actually making the right decisions, capital allocation decisions, M&A decisions, and tracking what they say versus what actually happens and disaggregating them. So you've got to have a pretty sharp inquiring mind to keep listening out for gaps in what people say and then what they do.

AI assessment note: “We look for alignment of their skill set with the job that they've been hired”

Q How do you organize the investment team to cover three main sectors, four geographic regions, which is a lot of places, a lot of companies. What have you found optimal works?

A We've organized the team by industry. So, I work with three sub-teams, as you point out, and we have three meetings a week, one with each of those teams, that runs through research and analysis, new ideas, existing ideas, once a week. We're talking every day, but as a formal how we've organized ourselves, and making sure that people have got a target date to present work, update, imposing some discipline and rigor and structure to everything. I found that's worked very well. And allows a small enough forum for everybody involved in that subsector to talk and engage and express their views. What we've moved away from is the entire organization sits down to talk about stocks. The entire organization on the investment side will sit down to talk about lessons learned, share what happened when I was looking at this industrial in Germany versus I was looking at this insurance company in Bermuda versus I was looking at this tech company in Palo Alto. Great forum for that, and always surprising how similar people's experiences are. We're all somehow on the same journey when it comes to public market investing, and getting people to actually realize that, and be open about it, and oh, this wasn't unique to me, and what can we learn from that, and can I improve that? Oh, you did that? I probably should have done that. Just getting people to be open about what worked, what didn't work,

AI assessment note: “We've organized the team by industry. So, I work with three sub-teams”

Q What's an example of that learning across different people on your investment team that may be in different sectors?

A Take the financial sector. I think there's naturally an underweight of management assessment. The expertise lends itself to a lot of three letter acronyms and lends itself to detail and numbers and a lot of information. And people often just don't lift their heads and say, who's running that company? Do they know how to run this company? We were disclosed on a short in Europe where the CEO had no experience running anything, and that CEO subsequently got let go overnight, but getting the team to focus on, how's this person ended up running this company? That simple question, as opposed to all the facts and figures about how the business is doing this quarter versus next quarter, et cetera. And I think important for different sectors to hear how in TMT, it all comes down to the CEO or the management team. And TMT people naturally know that. But TMT people tend to not spend a whole lot of time on the balance sheet, because that's just not the fun part of being in TMT. Companies are companies. And good companies that are getting better tend to embark on very similar journeys over time. And I think bad companies that are getting worse tend to embark on the same types of fact patterns occurring. The underlying principles tend to be the same.

AI assessment note: “important for different sectors to hear how in TMT, it all comes down to the CEO”

Q In your conversations with your investors, what's your sense of what the allocator's perspective is on long short equity investing?

A I think there seems to be more skepticism, and I think that's healthy, because I don't think it's possible to have a thousand great long short managers. It's just, it's just too hard, and it's too specific a skill set. So that skepticism is probably healthy. I sense disappointment in the community with the overall returns from long short. I think once they get over that, they'll just realize it's quite a bespoke business. How many great venture firms are there? Maybe 10. How many venture firms are around today as a consequence of the last TMT boom? Probably 300. So three hundred's gonna peel back. It has to. And it may be a decade long process. And I think long short is a bit the same, that just like venture is a very particular skill set, And it takes very strong willed particular people to get good at it. It's not just throwing money at interesting startups. That realization that long short is the same thing. It's actually quite a boutique bespoke business. And it probably shouldn't be that many proper long short single managers out there when all said and done.

AI assessment note: “I think there seems to be more skepticism, and I think that's healthy”

Q trading, compliance, and more. In the AI era, asset and wealth management firms moving to Ridgeline gain a decided advantage. That's why customers call it miraculous, game-changing, and an awakening. If that's not how you would describe your investment management tech, request a demo at ridgeline.ai. And now back to the show. How do you go about creating a process to assess those people dynamics in companies and in markets?

A We've got a short framework. There's 10 boxes in the short framework and divides broadly into internal dynamics to a company and external environmental factors. We probably spend the most amount of time talking about management because I do believe that it's probably the one dimension that people spend the least amount of time Thinking about measuring understanding. We'll have people come in who do executive analysis. How do they even think about understanding executives? What do they look for in the history and their buyers, and how do they assess them? And that's more of the art side of this business. Calculating whether next quarter's earnings per share is going to be at plus or minus one percent is the science part. We should be talking more about Management strengths and whether that behavior that we think the market will reward or not, and actually creates fundamental value. And less time talking about whether the next earnings per share is up or down one percent.

AI assessment note: “We've got a short framework. There's 10 boxes in the short framework”

Q And how about on the long side? What are the targets that you like to look for?

A Good business models. And that seems like an obvious thing to say. But if I just say something contrary to that, has Amazon proven that it has a good business model yet on the consumer side? Three percent margins. It's a good business proposition for customers. That's very different to it's a good business model for shelves. So really being focused on business models that actually are just great business models. Certain businesses over time have proven to just be better businesses because the model is better. That's, again, super high on my list of, do I want to be an investor in something? The external environment. Is this a good time to be invested in this company? Or I can wait and maybe this is just the wrong environment for this business. So identifying good businesses and then understanding when it's a good time to actually own that business. And then I could go through the list of moats and good management, but if I boiled it down to Two most important things in my mind. It's a good environment and a good business model.

AI assessment note: “It's a good environment and a good business model.”

Q What are some of the things you've learned from looking at all that data that you didn't know ahead of time?

A It's as simple as net decisions at the fund level tend not to work. To you want to do mid-cap long investing, any hair on that asset tends to not produce outcomes that are good. When shorts get below a billion dollars, and we've probably had 15 stocks in our history that have gone from large to below a billion dollars, and when they get to that type of market cap, their behavior changes dramatically. It's a survival call option. The data can tell you all kinds of things. Actually, I heard a great expression for those small shorts. Somebody called them squirrels. They keep making these noises. And a lot of people, yes, you can analyze companies and you can analyze what's a good company and what's not a good company. But can you create a portfolio that actually works with that information? And in long, short equity, I've seen a lot of people who, they're good at the analysis, they're good at saying this is a good company and that's a bad one. But populating a portfolio that actually produces outcomes, That's a totally different skill set, essentially, because that's about sizing and risk management and exposures. And then if you go all the way to the factor neutral world, it seems that three percent alpha works for everybody. That's fine. We're shooting for 20% alpha. Completely different order of magnitude.

AI assessment note: “It's as simple as net decisions at the fund level tend not to work.”

Q As you've set up those contracts with the people on your team, how do you bring that down into practice of what the alignment is? There's different compensations Structures that people use as one portfolio? Is it individual ideas, a contribution to the portfolio? What have you actually done that's worked?

A Alpha measurement. Recognizing when it's their idea versus somebody else's idea, the firm's idea. Measuring. Just measure as much as you possibly can, and alpha being that North Star. Back to all good and well, these were money-making ideas, but there was no alpha. Or it was an okay short, but not a great short. Or it was a decent short, but we actually added at the wrong times, and therefore there was no alpha. And so a little bit getting people to be more precise about how much alpha they're measuring, and really measure themselves. Build the habits internally, and also with the team, to want to be measured, to enjoy being measured, to check their own performance. It's a performance industry.

AI assessment note: “Alpha measurement. Recognizing when it's their idea versus somebody else's idea”