Invest Like the Best with Patrick O'Shaughnessy
00:38:23 · Patrick O'Shaughnessy
[00:38:23] S0: I love it. And I love that movie. So now knowing the reference, it's great. Going back to the Evernote acquisition, you mentioned you paid 50% more than maybe the next highest bidder. How do you know the right price to pay? Like how do you think about pricing assets? As you buy bigger and bigger ones, we can talk about Vimeo, we can talk about AOL. These bites of the apple are gonna get bigger and bigger. Price matters of course, you have the ability to do a lot after buying it, so maybe that allows you to pay a higher price. But still, I'm sure you wanna pay a good, fair, responsible price. How do you think about it?
00:38:51 · Luca Ferrari
[00:38:51] S2: One is how do you determine your returns as a function of price? The second one is how do we stay disciplined so we will really not pay more than we believe is right based on our expected returns, opportunity cost, what else we could be doing with that capital and at what returns. So the first one is a matter of sophistication and the second one is a matter of psychology really, think, discipline, patience. And the third one is how well do we negotiate? How efficient are we at positioning the ultimate price on that curve? The fastest but stupidest approach would be to immediately offer the very most you can pay. The opposite of offering a ridiculous low price is probably equally stupid. So we want to find the right balance. Interestingly, we actually are much closer to the former. We believe it's better to have a reputation for someone who offers a very fair price immediately but who's not going to be very willing to negotiate much. So the first one, how do you determine that return as a function of price curve? You've got to be very sophisticated at knowing what you're doing, having first party data for benchmarking, asking the right questions, having good models, but the output of the model is only as good as the assumptions you put in it. And we certainly have very sophisticated cohorted models and whatnot, but main advantages in being able to run these businesses a lot better. We don't win because we're good at predictions. We win because we can run them better so we can offer a good price. But it's certainly marginally important to making good predictions. And so you want to have twelve years of experience running many businesses from the trenches in the details. Private equity, I think teaches you a lot more. So you understand why things went a certain way with certain business. You're wiser when you set the assumptions for your next acquisition in a way that I think if you stay on the financial layer or kind oh, talk to management every week, you think you understand, I don't think you really do. There is a cost to pay, it takes time. But then on the bright side, you're basically smarter at predicting the future when you find yourself in a similar situation again. So assumption setting is critical for us.
00:40:45 · Luca Ferrari
We have many assumptions, each has a probability distribution and we debate assumptions extensively without ever looking at what the model will spit out as a consequence. That's forbidden. Because we think that if you see the P and L, basically the business plan as you do it, there are all sorts of biases. It doesn't look good enough and you're like, Oh, maybe this is conservative. Let me push We do not look at the output, only at the inputs. We debate, analyze, dig for more data. Once we're happy, that's the best we can do. At this stage, we run a Monte Carlo simulation and then we look at the distribution of IRR, NPV and that's the truth. Nobody can say, Now that I see it, I think maybe we were a bit pessimistic with the assumptions. No, too late. This not the truth. This is what will guide our negotiation. So that's phase one. And then you make an offer. As I said, we try to make an offer that's sometimes the maximum we'll to pay or close to it because we think although we could probably get a better deal in the moment if we started lower, then we don't want to establish a reputation for people you can push around and get more out of. We're more like the Warren Buffett model of, I'll give you what I think is actually a very good offer and I'm okay if I hear no, but don't think you can get 25% more out of me just asking. And then you've got to be disciplined when they ask for more than you're willing to pay. You absolutely need to not have fallen in love with that particular business and say, okay, you know what? That's not to be, we'll move on to the next one. What's your
00:42:05 · Patrick O'Shaughnessy, Luca Ferrari
[00:42:05] S0: walk away rate? Like for every AOL, Vimeo, Evernote that you buy, how many did you want to buy that you ultimately didn't? [00:42:11] S2: We need to define this because we look at actually thousands of businesses each year. We don't make an offer to thousands of businesses. I'd say we probably make an offer to twice as many as we buy. You know what? We have never lost a bid before. There has never been a business we made an offer for and someone else got it. Those we didn't buy were ultimately the seller just chose not to sell to anybody. So that tells me our offers are typically super competitive. It also tells me we're probably not very good at negotiating because I think some level of failure rate would indicate a more optimal strategy. I guess you you fail, you learn, you get better. [00:42:42] S0: Can we talk about the history of the financing of the business? Because like you said, you've done very little direct equity capital raising before you've done some debt. Has most of this just been build up of free cash flow from earlier businesses until you have enough to buy the next thing and then just rinse and repeat?
00:42:58 · Luca Ferrari
[00:42:58] S2: Yeah. In short, the more sophisticated version is completely true what you just said for the first five years. Then we started using debt, Pretty basic debt from commercial banks. Not very high leverage ratios, 3.5x EBITDA on a good day, generally lower trailing EBITDA in the last twelve months. That helped accelerate. Before we couldn't use that because you need to have an established track record before they take you through your code. Since then it's been essentially that and reinvested earnings from that. We have raised a bunch of equity but mostly to fuel secondary transactions because if you're in business for a long time, you start to get into good scale, people say, Okay, I've invested in this company. And I'm talking really just team members because from the beginning we enabled people, we paid just cash, no variable pay of any kind. And people can choose though to receive some of their cash pay in equity at a discount. It's very unusual by the way. In time people have accumulated positions and that equity is worth nothing if there's never any liquidity. So we started organising secondary transactions every eighteen months, one year, two years. We've had maybe five, four probably since 2019. And so mostly equity has been raised to finance those transactions, but occasionally the first capital increase of any significance was in 2022, I think. So yes, have dilution from capital increases, very modest. Off the top of my head, I'd say maybe 10%. We could also not have done any of those at all. We still did them because we figured in a couple of cases it helped us get over the hump to close a deal we couldn't without that little extra. With that we were maxed out. But also we figured if we bring in a little bit more in terms of high quality international investors, that would be helpful. Credibility, firepower if we need to go after a huge acquisition quickly, just optionality. But we're generally very cautious when it comes to dilution. I think if you really believe in what you're doing, it should be painful to increase your capital base.
00:44:49 · Patrick O'Shaughnessy
[00:44:49] S0: I don't know if it was Evernote or some other one. We've talked about Evernote, so maybe pick a different one. I'm curious for another acquisition, whether it's WeTransfer or Commute or anything else, the AI photo sharing one, Remini, which I was just looking at out before we started this morning. Are there other acquisitions that have taught you personally the most about your own process, about doing this well that stand out in memory?