In his recent interview with Tobias, Drew Dickson of Albert Bridge discussed Hunting Alpha In Europe. Here’s an excerpt from the interview:
Tobias: What’s your universe, in terms– so it’s Europe, but it’s anywhere in Europe, and then do you care about your geographic concentration or diversification or that’s just an irrelevant consideration?
Drew: Well, it may not be irrelevant, but I don’t care. We’re basically a Western European focused product. It depends on what ideas make their way through the process and through our universe into this focus list. We typically have a lot of exposure in the UK or Germany or France, sometimes Sweden, Norway, Finland, Spain, very little in Portugal or Austria, bit in Holland, bit in Italy, just depends on the ideas and the nature of them. But no, we’re not– arguably, it’s become less important after we all moved to the Euro in terms of FX risk, but from a geographic perspective, we monitor but we don’t have any constraints on it.
If it turns out that we have half the portfolio in one country, as long as we think we’re doing things that are idiosyncratic, and they’re not that related to each other, we’re totally fine with it. Similarly, I say the same thing about our sector exposures. We’ve got a lot of consumer stuff. We have a lot of industrial stuff. Very little in tech in Europe. We haven’t found things we thought they were– we had a few IT services exposures over the years where we thought people are missing out on things.
Tobias: What’s the limiting there? You just don’t think that they’re cheap enough. They’re just not underfollowed enough.
Drew: Well, there are some that are, but you don’t have these global dominating businesses in tech, like we have in the US. We had one, this is not a value stock at all, but we held it because this is a great example actually, Toby. It was Arm Holdings was the company– Basically, they’re the inside all these phones. The Arm architecture, it’s a globally dominated business. SoftBank bought it a few years ago. We lost one of our potential world dominating tech companies. Of course, we have SAP and the big IT services companies like Capgemini, Aptose and things.
We’ve had different positions in a lot of these things over the years. I should mention too, sometimes we’ve had names in the portfolio for six months. Usually, when we get six months into it, no, we screwed that up, that’s wrong. Sometimes, it’s because the thesis changed, and sometimes there can be something in the portfolio for years. But even then, it’s not a static size, it can be a 5% position and that can grow to 8% and fall to 4%.
This all very much driven by what is expected returns. Here’s what we’re worth if it goes up. Here’s what it’s worth, it’s if it goes down. Where’s this stock and how does that compare to other things in the portfolio? As the expected returns fall for certain things, we will shift that capital into other things that have higher expected returns. That drives a bit of turnover, which has a bad name. I think it can actually be a positive thing in a concentrated book if you’re doing it from without bias. Obviously, easier said than done, that’s part of the process for us, too.
Tobias: How are you hunting for what you’re looking for? How are you tracking them down, then, how are you going–? What’s the process for validating and making sure they fit for the portfolio?
Drew: We start with a defined universe of billion-dollar market cap companies in the sectors we focus on and the analysts, including myself, will be in charge of doing the work in those– [crosstalk]
Tobias: How big is that universe roughly?
Drew: It’s a 300-stock universe, basically, and you can have– call it five different sectors that we’re focusing mostly. Like I mentioned, we don’t look at the utilities or the telecoms, or the interest rate sensitive stuff, real estate. But in the stock-picking sectors, these are ones with the highest dispersion, it becomes a matter of– we call our process the GAME, Gather, Analyze, Model, Evaluate. At the beginning of the process, we’re just gathering information.
We’re going to conferences and we’re meeting with companies. You’re just trying to hear things that you didn’t expect to hear. If you don’t hear anything you didn’t expect to hear, you move on to the next sector or the next idea. But if you hear that stuff that, “Hey, I didn’t expect that,” if you see management change its tone from what they used to say six months ago or a year ago.
Well, let me dig a little deeper to this and then move into the A stage, you start to analyze all the information you’ve gathered. The most important thing in that A stage is to get rid of information, get rid of stuff, which is noisy, and people are going to think it’s news, or it doesn’t matter and try to really hone in on the stuff that does matter. In the M stage, we’re just modeling it. Okay, what’s this thing going to look like?
What’s down the road, given the restructuring that they’re doing or the products that they’re launching or the margins that they’re seeing or the demand that we’re hearing about? Then, in the E stage, the evaluation stage, we’re evaluating consensus expectations. Okay, Where is Mr. Market? What do they think about this? It’s hard, but we try not to know that stuff at first. We don’t talk to sell-side analysts until the very end to see what their views are. We don’t want to be biased ourselves. We just want to be fairly open and objective to this notion that company X, Y, Z is a buy or to sell or it’s a nothing.
Once we decided something in that E stage, then we’re trying to understand why. Okay, why does the market not see the things that we see? What are we getting wrong? What are they getting wrong? If we can then marry those things, then we have candidates for the focus list. Then the whole expected return thing is what then helps me downselect to a more concentrated portfolio of our very best ideas.
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