November 10, 2014
Interviewed by: David Snow
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Does More Data Lead to Smarter Investing?

A panel of experts from AlpInvest, Siguler Guff & Co., and Clayton, Dubilier & Rice discuss whether harnessing private equity portfolio data can enable better investment decisions.

A panel of experts from AlpInvest, Siguler Guff & Co., and Clayton, Dubilier & Rice discuss whether harnessing private equity portfolio data can enable better investment decisions.

Does More Data Lead to Smarter Investing?
Data and Excellence in PE Investing

David Snow, Privcap: Today, we’re joined by Thomas Franco of Clayton, Dubilier and Rice; Jay Koh of Siguler Guff; and Eduard Lemle of AlpInvest Partners. Gentlemen, welcome to Privcap. Thanks for being here.

Eduard Lemle, AlpInvest Partners: Thanks for having us.

Jay Koh, Siguler Guff & Co.: Thank you.

Snow: We’re talking about the importance of data in private equity, and how better access to data, more data, and a better ability to analyze data can perhaps make you a better investor. I’d love to hear from all of you to what extent you think that’s the case. Eduard, your own investors are requesting much more information and they want it in a better form, with more ability to slice and dice. Of course, AlpInvest as an investor in other peoples’ funds and directly in companies, has its own platform for analyzing data. It’s one thing to get data but it’s another thing to actually benefit because you have it. What are some approaches you can use to use data to make yourself a better investor in the private equity asset class?

Lemle: Thanks for the question. You already structured it very well to differentiate the two broad groups of use cases. One is to look at an entire portfolio and hear the points; risk management and portfolio’s construction are most important. The second one is, as selectors of invested funds as AlpInvest, how do we make better investment decisions in which fund to choose versus another fund? I would say, for the first one, risk management here it’s really the ability to spot trends in revenue and EBITDA early on as a warning sign. Or debt multiples, how do they develop over time in different geographies and where do we have first signs of a downturn? Or, could we even run stresstest scenarios with good systems that support that type of analysis?

Snow: For example, would you see that there would be worrying revenue declines in certain sectors within the private equity portfolio and that would be one form of risk management that you could uncover because of the use of data?

Lemle: Yes, we would be able to then spot patterns and say, “A certain country X in healthcare across the board has grown very strongly last year, and, the most recent period, it’s actually slowing down.” Then, you can see how this compares to public equity or publicly available statistics on the similar sector.

Snow: Jay, you’re also a firm that not only invests in other people’s funds and directly in situations, but you have your own investors. So, you’re kind of in the middle of this flow of information. Can you summarize some ways you use the information from the underlying portfolio companies to become a better investor yourself?

Koh: We pay a lot of attention to entry multiples when we’re looking at different markets and whether we think there is an overflow of capital. We try to track where we are in the cycle, depending on roughly where we are getting into deals. Over half of the capital we have in our management is in distressed and credit strategies. We pay a lot of attention to the credit environment to understand what the likely risk factors might be for portfolios that have substantially more leverage in them. That typically isn’t the case in the emerging markets, but we play a lot of attention to that as well, in the situation where we’re looking at new fund managers, which is the most difficult. Where you’re got very little data to underwrite, we pay a lot of attention to how they underwrite individual transactions and the pipeline of transactions they’re looking at compared to the database of transactions we already have. So, if they’re looking at healthcare, how do the healthcare companies they’re looking at perform versus the companies we already know and have invested in? We’ve done tripleplay transactions through funds and directly in Russia, Latin America and other geographies. So, we understand how those things ought to perform and whether or not a GP is looking at them in a way that gives them that international perspective.

Our ability to assess the true underlying value of different companies from a performance basis using the wealth of data we have at the transactional level to make better decisions about pricing or about whether to actively step into the shoes of a GP, or to work with other LPs to do that. The last piece we think about, as a firm that also invests and not only manages, is we pay an increasing amount of attention to fees, expenses and ESG as data points from the underlying managers.

Snow: Do you sense that especially the investment advisors out there, groups like AlpInvest and Siguler Guff, are attempting to distinguish themselves by being able to use the data they gather to help their own investors understand not only individual managers better but the world in general? Is that becoming an important differentiator for these investment services?

Tom Franco, Clayton, Dubilier & Rice: The asset class is not well understood outside of the club of people who are involved in it day to day. And it seems to me that there would be an enormous opportunity for Siguler Guff or AlpInvest or any of these other platforms to provide some of this macro data to policymakers and academics who are looking at the industry and trying to understand how it really works and what its economic contribution is. For example, many dimensions—employment, productivity, innovation, all of those things—should get more visibility and you guys were in the perfect position to do that.

Koh: The problem with data is that it’s forensic; it’s backwardlooking. You have a potential for a Thanksgiving turkey problem where you feed your bird and feed your bird, and then, on Thanksgiving, you cut the bird’s head off. Not everything you’ve seen before is likely to continue forever, right? Using that perspective and not being blinded by it has been something that is a critical aspect of why our investors have been interested in working with us.

Snow: Can any of you give concrete examples of, on the risk side, risks you have seen or uncovered because you were able to compare information across your portfolio? [Risks that] either allowed you to make a better decision, or at least it forced you to pay closer attention to an issue you did not previously know was there?

Lemle: I would say the best use of portfoliowide information on the granular or portfolio company level is, for example, tracking metrics like debt over EBITDA, to see if things are getting to unhealthy levels. As a next step, you could do risk or stresstest scenarios like assuming that anything that’s over seven times EBITDA levered and is not covenant light might get into distress if things go sour over the next two years. That’s a more systematic portfoliowide perspective.

Koh: We do a lot of healthcare investing globally, so we’ve done probably $600 to $700 million of healthcare investing, $200 to $300 million direct or co-invest in emerging markets alone. So, when we look at the comparative performance of different similar companies in our portfolio, you understand what the key performance indicators are. When we were looking at co-invest transactions recently in the healthcare area, we had a pretty aggressive turnaround plan presented to us by a local GP that thought they could rightsize the operations of the company within a year or a year and a half. We sent the head of our India office, who has run one of the largest pharmaceutical manufacturing companies in India and has a lot of experience with these healthcare companies from a background at McKenzie to go look at the asset and compare it against the performance we’ve seen in other similarly situation-ed healthcare companies in the same sector, sub-sector, in different geographies. And that made us considerably more concerned about the duration it would take to actually turn around that asset, and then, changed our risk analysis around the transaction. We ultimately decided to move forward with the transaction but we underwrote it at a different level, and our expectations for how to monitor that transaction will be different going forward. The only other point I want to make here back on this fees and expense piece of it is we’re doing a data analysis again and a review of the fees and expenses of the underlying managers we work with. And, in some cases, we’ve had a couple situations where there have been some irregularities, and that’s not that surprising given the context. We’re now starting to discover that the entire market is in an evolving market in the private equity landscape.

Franco: I’d like to just challenge the assumption behind the question, data struggles with context. Investments are not discrete events; they are embedded in sequences and contexts. And you have to really understand the narrative, multiple effects and multiple contexts. So, I would just wave a cautionary flag.

Lemle: I would like to echo that entirely. From the LP organization’s perspective evaluating a fund, the data is just a starting point. As I said before, the data we’ve been provided by GPs for, let’s say, entry multiples or all their nitty-gritty for the diligence phase has already been nearly as detailed 10 years ago. And GPs have been very good. What’s key is for LPs to have a sizable and highly skilled team to exercise the judgment on what’s between the lines and not just follow the pure data.

How can iLEVEL’s platform help the private equity asset class grow?

Hank Boggio, iLEVEL Solutions:

At iLEVEL, we believe that by providing a limited partner or an institutional investor with deeper access to data, they’ll have the ability to have more confidence in their data, to effectively be able to monitor and measure the performance of their portfolio, ultimately increasing the asset class allocation and getting better returns in total.

Talk about iLEVEL’s origins within the Blackstone Group.

Boggio: Today, over 100 GPs use our technology. What Blackstone realized several years ago was that inefficiency of having access to data that was spread across the organization, prone to errors—the difficulty in trying to aggregate that. [These were] very timeconsuming processes. They have expensive resources on the investment teams that were spending upwards of 80% of their time chasing data as opposed to doing what they’re paid to do, which is essentially analyze that information, assess performance and ultimately source new deals. So, having it centrally stored really gave them the ability then to effectively and efficiently use that data for a variety of reporting purposes.

Yeah. If you look at allocations today from an LP’s perspective, they’re putting a small percentage, 6%, 7%, 8% or 10% perhaps into their overall portfolio. The reason that’s so limited is that they don’t have or haven’t had the ability to effectively look at that data to assess risk and exposure. Where do they have an issue in a portfolio?  Do they have a particular allocation that perhaps needs to be hedged against the public markets? So, while the asset class has always outperformed the public markets, or at least in most cases, has outperformed the public market asset class, they haven’t had the confidence in the data they have, or perhaps the lack of access to that data is the reason they’ve not been able to increase the allocation to the asset class.

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