Intro
Let me tell you something, folks. When it comes to distinguishing a successful PE (Private Equity) investment from a bad one, it’s not as easy as it looks. It takes a sharp mind, it takes skill. And you know what? The first step in cracking this code is defining and using the right benchmark for comparison. That’s the secret sauce, believe me.
Now, let me break it down for you. Benchmarking, technically speaking, is all about measuring investment success. How? By pitting the performance of a particular investment against a suitable alternative. It’s like a competition, my friends. Who’s going to come out on top? That’s what we’re trying to figure out.
But here’s the kicker. We have the power, the ability to compare our funds with both public and private alternatives in the private capital arena. It’s like having the best of both worlds, folks. Public market benchmarking, in general, is especially relevant for those big shots, those asset allocators who’ve put their money into a specific PE program. They want to make sure their funds are being put to good use. It’s all about keeping tabs, folks.
But wait, there’s more. Private market benchmarking is where the real action is. We’re talking about those investment managers, the ones who have the keen eye to handpick those underlying fund investments. They’re the real deal, folks. And for them, private market benchmarking is like a golden ticket. It’s about reevaluating the (relative) performance of the fund managers they’ve chosen. It’s about making sure they made the right moves. It’s a game of strategy, my friends.
But here’s the thing. There’s a real shortage of theoretical literature on private benchmarking for PE funds. Can you believe it? It’s like a big, huge gap in our knowledge. But you know what? We’re not ones to shy away from a challenge. No way. In fact, we’re dedicating a whole paper to private benchmarking, forget about the public side. We’re going all-in, folks. We’re going to dig deep, find the answers, and set the record straight.
So, folks, when it comes to distinguishing successful PE investments from the bad ones, it starts with benchmarking. It’s a real art, a real science. And we’re at the forefront, leading the charge. Get ready for a wild ride as we dive headfirst into the world of private benchmarking, where we’ll unravel the mysteries and pave the way for a new era of investment greatness. It’s gonna be huge, folks. Believe me!
Is Quartile Ranking Dead?
Let me tell you, folks, there’s a cutting-edge private market benchmarking methodology that’s all the rage. They call it “quartile ranking,” and let me tell you, it’s state-of-the-art. The practitioners, the ones in the know, they swear by it.
So here’s how it works, very simple, two steps. First, you identify a bunch of private equity funds that have similar characteristics, and they become your peer group. You want to compare apples to apples, right? Second, you look at the key performance indicators of all these peer group funds, and you count the winners and the losers. It’s like a numbers game, folks. And that’s how you calculate “the quartile of our fund.” It’s as simple as that.
But here’s the thing. Sometimes, these peer groups, they’re seen as too subjective or too small. It’s like they’re playing with a limited deck, folks. And you know what’s even worse? Transparency and disclosure, the very things that should be important, they’re often disregarded. It’s like they don’t want to show their cards. Not good, folks, not good at all.
Now, let me tell you about something that Harris and his team found out back in 2012. They showed that even small variations in the methods used can lead to half of all funds claiming they’re in the “top quartile.” Can you believe it? It’s like they’re playing with the numbers, folks. It’s not as reliable as it seems.
So, here we are, ready to tackle the big questions. Is quartile benchmarking a dead end? Are we stuck with flawed methods? Or, and this is a big one, can we unleash the power of more quantitative and data-science-driven methods? It’s like opening Pandora’s box, folks, and we’re about to find out.
Our study, that’s right, our study is diving headfirst into these questions. We’re not afraid to challenge the status quo. We’re going to explore whether quartile benchmarking is a road to nowhere or if we can revolutionize it with better methods. It’s like bringing in the big guns, folks. It’s going to be tremendous, believe me.
So stay tuned, because we’re about to shake things up. The era of more robust and data-driven benchmarking is upon us, and we’re leading the charge. Get ready for a wild ride, folks, because we’re about to make benchmarking great again!
Findings
- Folks, let me tell you something about private benchmarking. The data, it’s scarce. I mean, we’re talking about a real shortage here, especially when it comes to the fund level and even worse, the deal level. It’s like searching for a needle in a haystack, folks.
- Now, here’s the thing. We can’t just sit back and do nothing. We need to enhance the data, give it a little boost. Of course, we don’t want to go overboard with it, but sometimes it’s necessary, especially when we’re dealing with funds from older vintages. We need to benchmark them, folks, and that requires some data enhancement. It’s like adding some extra oomph to get the job done.
- Now, when it comes to the fund level, we can use these fancy parametric models. They’re like statistical wizards, my friends. They help us obtain those much-needed results, even in data-scarce environments. It’s like finding a way when there seems to be no way. That’s what we do, folks.
- But here’s where things get interesting. On the portfolio level, those simulation approaches, they can be a bit problematic. You know why? It’s because they assume independence between funds. Sounds fancy, right? But here’s the issue. When you have a large portfolio, two things happen. First, the variability around the mean becomes teeny-tiny. It’s like you’re dealing with minuscule differences. And second, it uncovers this bias between the peer group and the investable universe. It’s like there’s a mismatch, folks.
- So what’s the alternative, you ask? Well, the natural and, dare I say, easier alternative is maximum diversified approaches. They make more sense, folks. They’re like the logical choice when it comes to benchmarking on the portfolio level. It’s like going with the flow, going with what works.
So there you have it, folks. Private benchmarking, it’s a tough nut to crack. But we’re not afraid of a challenge. We enhance the data when needed, we use those parametric models on the fund level, and we go with maximum diversified approaches on the portfolio level. It’s all about finding the right path, the right approach. And that’s what we’re all about, folks.
Conclusion
Let me tell you something, folks. When it comes to peer group benchmarking, there’s a lot of ambiguity. It’s like trying to navigate through a maze. So, here’s what I suggest. We need to implement a simpler and more sophisticated version of these benchmarking steps. It’s like covering all our bases, folks.
What does that entail, you ask? Well, first things first, we need to perform all the analyses using both the raw data and an enhanced peer group dataset. We want to leave no stone unturned, folks. On the fund level, we’re going to use the empirical distribution, but we’re not stopping there. We’re also going to bring in several parametric models for percentile and quartile ranking. We’re going all in, my friends.
Now, let’s talk about the portfolio level. We’re going to calculate the maximum diversified benchmark, because that just makes sense. We want to see the big picture, folks. But we’re not stopping there. We’re also going to check if the historical simulation’s empirical distribution looks reasonable. It’s like a reality check, making sure everything adds up.
But here’s the thing, folks. We need to analyze these methods and see if they give us the same results or different ones. We want to understand why that is. It’s like unraveling a mystery, finding the truth.
And let me tell you something. When it comes to choosing the most appropriate method, it’s a subjective choice. That’s right, folks. It’s like a personal preference, a judgment call. We can’t underestimate that. The objectivity and validity of private benchmarking results, they shouldn’t be overestimated. It’s like keeping it real, folks.
So there you have it. We’re going to implement a simpler and more sophisticated approach to peer group benchmarking. We’ll use the raw data and an enhanced peer group dataset. On the fund level, we’re bringing in the empirical distribution and parametric models. On the portfolio level, we’ll calculate the maximum diversified benchmark and check the historical simulation’s empirical distribution. We’re going to analyze, compare, and remember that the choice of method is subjective. It’s like a puzzle, folks, and we’re putting the pieces together. This is how we do!
Christian Tausch, Markus J. Rieder, Philipp Abel
The article is published in the Journal of Alternative Investments, (Summer 2023, 26 (1) 96-111).