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Educational Alpha
S3: Conversation with Andrew Akers, Lead Quantitative Research Analyst, PitchBook
00:40:33
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- [00:05]
Bill Kelly
Welcome to Educational Alpha. I'm Bill Kelly, your host, bringing you on the ground conversations with business leaders, educators and industry colleagues from around the globe. Educational Alpha is sponsored by iCapital, the financial technology company with a mission to power the world's alternative investment marketplace. Part innovator, part educator, and part navigator of the alternatives industry, iCapital offers intuitive, scalable digital solutions that have transformed how private market and hedge fund investments are bought and sold. With iCapital, financial advisors, wealth managers and asset managers around the world now have access to everything they need to deliver the return and diversification potential of alternatives to high net worth investors. To learn more, visit icapital.com in this.
- [01:01]
Podcast Introduction
Episode, Bill welcomes Andrew Akers Leap Quantitative research analyst at PitchBook to dive deep into private equity and private market replication strategies. Andrew shares his journey from traditional finance to quantitative analysis, discussing the key findings from his seminal paper on private equity replication. The conversation examines the nuances of equity risk premia, operational alpha and the challenges of building a replicable index for private markets. They also explore the role of machine learning in analyzing patterns within financial data and the growing importance of private markets for high net worth investors.
- [01:38]
Bill Kelly
Andrew Akers welcome to Educational Alpha.
- [01:40]
Andrew Akers
Thank you Bill. Thank you for having me.
- [01:42]
Bill Kelly
I've been looking forward to this conversation. You wrote what I consider to be a seminal piece on taking the private out of private Equity under the PitchBook Monaco, which is your current employer. And we're going to spend most of our time talking about that. But before we get there, maybe a little bit on your background and how you got to PitchBook. And I know Pitchbook is now part of Morningstar. I'm not sure when you joined pre or post that acquisition, so maybe a bit on your experience.
- [02:08]
Andrew Akers
Yeah, I started probably a more traditional finance background, so right out of college spent a year working for Vanguard on the retail side and then I moved into the institutional consulting world where I kind of got a lot of my background for strategic asset allocation portfolio management. So I did some performance reporting, started out there and then worked my way into strategic research looking at capital market assumptions and how institutional investors build portfolios. And then I finished up on the portfolio management side of things in their OCAO discretionary side of the business. From there, decided to make a change to be a little bit more quantitatively inclined and add that to my skillset. And then PitchBook was a great opportunity there, kind of combining the data side with the market side as well. So I'm currently the lead quantitative research analyst at PitchBook where I spend time Doing kind of written research on private markets as well as building in analytical tools to help a wide variety of private market participants.
- [03:20]
Bill Kelly
Well, a great path forward in Vanguard. We may get back to that at some point too because I've often used Vanguard is an example of exposure to end investors and we think about diversification. Getting that pure beta as cheaply as possible is how Vanguard was built. And if we get into the wonderful world of alternatives, it is more, at least theoretically, maybe less so in reality. An alpha based game and the ability to replicate that exposure is more difficult. And if we're going to do it and maybe do it either partially right or fully right, it's going to come through better analytical and quantitative tools. And I think you're on to something here. Although I don't know if this buyout replication portfolio index was meant to replace private equity, but I have some thoughts on that as well. But before we get into the process and what brought this whole paper and viewpoint forward, maybe a couple of definitions and you said something in here which I find remarkably true and maybe understated quite often is what is equity risk premia and is it different public versus private? And I think you can get into a deeper discussion about operational alpha in the private markets. But in essence, is it fair to say equity risk premia doesn't really have a conscience and doesn't know if it's public or private?
- [04:38]
Andrew Akers
Yeah, I would 100% agree with that on a theoretical basis. If you just go back to like the very basics and what is equity? How do you value equity? How do you value a lot of different financial assets? It's simply the discounted or the present value of future cash flows. And whether you're a private business or a public business, there's some differentiations there, there's different regulations. But at the end of the day, you're trying to use that capital most efficiently. You're trying to maximize your future cash flows while minimizing a bunch of different risks and optimizing for that. And at the end of the day that's risky, right? Those cash flows are risky. They may or may not come to fruition. But that core concept is the same whether a company is publicly traded or privately traded.
- [05:31]
Bill Kelly
I think that's going in thesis that I think most if not everybody could agree with. And then on top of that, and your research proved this out, which I don't think you needed to get to the last page to figure that out, is if you can buy something very inexpensive relative to the market as a whole, hold it for a long time. You usually do well. And that's the concept of value investing. And there was certainly a bias in terms of your approach there too. But then when you think about a public company versus a private company and certainly the buyout space or a couple of key differences, and you can get into some of these, certainly the sector weightings are different and that gets into maybe the index a little bit. But there is operational alpha because you have a concentrated shareholder base. And then the deployment of leverage can be a little bit of a wind at your back, which it has been for a large period of time, maybe less so today. But talk about some of those elements which maybe takes a risk premia that's equivalent, but then it's different as you layer in some of these other aspects.
- [06:28]
Andrew Akers
Yeah, I think the elevator pitch for buyouts, specifically in private markets does hinge on this idea of operational alpha. And so the general buyout playbook really hasn't changed since the kind of the strategy gained popularity and began in the 1980s. You find a company that you can purchase so much you feel like is well below its fair value, you lever that company up and then you make changes. So maybe you bring in a new management team, maybe you spin off a separate department, all sorts of different things. And the pitch that I think bio managers made was that's a differentiated source of return, that's uncorrelated with markets. What we do with the management team doesn't necessarily have anything to do with what's happening in broader markets. And so that was kind of the key. But what we've seen from the data, even in the past 10 years, is the biomarket as a whole. That hasn't necessarily been the case. What we've really seen has been driving buyout returns has been multiple expansion, revenue growth, which is what we've seen broadly throughout markets. The last 10, 15 years have been a really good time for corporates. But the one area where they often try to pinpoint is on the margin side of things. That's where these operational alpha improvements generally show up, is increasing margins. And we've seen that just hasn't necessarily been the case for the median buyout deal in the data that we have. And so certainly you have managers, just like in public markets, you're going to have those managers that do outperform and outperform in this operational alpha sense. But as an investor, if you're gaining broad exposure, that's not necessarily been a reliable source of differentiated return.
- [08:40]
Bill Kelly
I want to spend some time, Andrew, going through some of your assumptions and how you built this buyout replication portfolio. But I have this penchant always going to the final chapter and seeing how things turned out and working my way backward. And I think what was roughly a 10 year period, 2014 to maybe circa 2020, you compared the return of this index to the Russell 2000, which I think is an appropriate benchmark. And there's differences which we can get into. And you chose three ways to decompose the return. Sector selection, leverage and security selection. And the biggest winner was the security selection. IT outperformed by 830 basis points, but security selection was 700. And it looked like the sector selection was 150 basis points and maybe a slight loss on the leverage side. So I think it underscores how different your portfolio is from the Russell 2000 and I think that's part of what you were trying to accomplish. And we're going to get into this about whether or not we're talking about a way to replicate PE returns for the investor versus having a better way of measuring buyout returns relative to what most people look at as a public market equivalent. So any observation on the end result before we get into the more important pieces as to how it was built?
- [09:59]
Andrew Akers
Yeah, you touched on that return decomposition and kind of the security selection component is the one that stands out. So really all that is, I think we'll probably get more into how we're determining that security selection. But as you said, jumping to the end, there's kind of a systematic way in which buyout managers select securities that has kind of been pervasive. Like I mentioned, they are buying cheap companies, potentially companies that underperform in terms of stock price, but not necessarily fundamentals. There's a focus on free cash flow. And essentially if you replicate that type of security selection by looking through financials and stock price data that's had a premium or at least in the last 10 years. And I'm not the first person to come to that realization. There is a paper out of the Harvard Business School, Eric Strafford did a similar exercise, different data set and also found some similar findings. That the security selection for biomanagers may be more systematic than I think people realize or another way to put that, it might be less active than people.
- [11:15]
Bill Kelly
Realize and in observation. And then we can get into the underlying building blocks. But when it comes to backtested results, a skeptical client will say I've never had a manager present to me back tested results that didn't do well or they didn't like and There are always going to be some assumptions made. It seemed like yours more black and white as opposed to making a lot of assumptions in the gray area. Am I right on that? Or did you have to maybe make some assumptions just based on sort of more gut instinct?
- [11:43]
Andrew Akers
I mean, that's a good point. The back test is one of the most difficult things, I think, in finance because you need something to point to, right? You're not just going to invest in a new strategy solely based on an idea. But they're full of limitations, particularly with time. Our back test only goes back 10 years, which admittedly, that's not long enough. I think some people would argue even if you went back, you know, 50 years, that might not be long enough. So there is that caveat. I'd say we try the best we could to make it as systematic and repeatable and take away any decisions, subjective decisions that we could. And at the end of the day, you do, I think with any type of new product like this, you do kind of have to buy in fundamentally to the idea in a more qualitative sense, which probably is kind of sacrilegious for me to say as a quantitative analyst. But there are things that you just can't completely solve for. And trying to kind of understand the idea. And does the fundamental nature of what we're trying to do, does that make sense?
- [12:56]
Bill Kelly
Okay, I take that at face value. So then getting into some of the building blocks, Andrew, which I think is a more interesting part of this discussion, and you and I have seen some of these same surveys. I think Hamilton Lane has put one up not that long ago that says 87% of the companies in North America with more than 2, I think 200 million or 100 million, I forget what the cutoff was. More than 87% are private versus public. So I think people might think that every portfolio company in a PE fund comes from the private markets. While largely true, not exclusively true. And I think your results in this, again, is, I think more of a black and white observation, is that 40 to 60 of these target companies come from the public markets every 18 months. So there is. Every 18 months, you look back, there's a sample of companies that did go public. So you do have some data to pull from, and why were they attractive? And then could you drop some kind of an algorithm over this to replicate why they were taken public in the first place? So this gets into, as I asked you before, your quantitative street cred, because it is dealing with neural networks, machine learning. But maybe more for me than the audience, if you can break that down in language that I can mostly understand because I don't have the quantitative chops of you.
- [14:12]
Andrew Akers
So the basic idea that if any machine learning model, and you can start with the basics, same idea of just a simple regression. You have some data, that's your input, you feed it into a model, a neural network or regression. Really a simple way to think of it is a function, right? A function of X and X is your input data. And then that data goes through the function and outputs whatever you've trained it to understand. So in this case, we're outputting the probability of a certain publicly traded company gets taken private within the next 18 months. There's a lot of things obviously that happen in between, and neural network is a bit complicated. But at the end of the day, we're looking for patterns in data. And so in this particular context, we're really relying a lot on the quarterly financial statements, which is really why we hone in on the take private transactions, because like you mentioned, the majority are going to be private to private deals. And at PitchBook we do track those deals, but the transparency into those deals is often very limited. You're not going to get company financial statements because they don't have to file those, which is one of the reasons that sometimes they prefer those. Those can be simpler transactions. But we do have this nice sample of companies where we have quarterly financial statements going back for the history of the company, and we have a private equity manager that goes on to purchase that company. And so really what we're looking for is patterns in the financial data as well as the stock price data, saying, what do these private equity firms look for? And are there repeatable tendencies in that? And what we found is that there are, because we do have some ability to predict or discriminate against companies that go on to be taken private. And so it's not random selection. There are certain characteristics that these firms are looking for which then kind of leads into the okay, if there's certain repeatable tenants tendencies that biomanagers look for, can we build that into kind of a systematic framework to copy that?
- [16:37]
Bill Kelly
And then a couple of adjustments you have to make to that, which I'll lead you in that in a second. But all things being equal, if these companies never get taken private out of your universe, you're starting with a much better value quotient. And if I'm looking at the paper right now, it's something just shy of 14 times cash flow in your BRP versus a market that's over two times that at almost 32 times. So you're starting out at a better valuation point and that should fall out of the bottom. But then if you take a step back, there are a couple of adjustments you have to make, which I totally understand is the sector bias in the private markets. And they don't like highly regulated businesses. So banks and insurance companies, no, thank you. And they're overweighted in tech. So if you look at the underlying index, you do have to tweak that and then maybe to make your answer a bit longer, you got to employ leverage there as well. So talk about the sector adjustment and how you replicate leverage in the brp.
- [17:37]
Andrew Akers
Yeah, the sector adjustment is kind of the first thing that we looked at when we were trying to build this replication strategy. And there's just like you mentioned, there's such stark differences between the typical benchmark and I think from my time in the consulting world, that typical benchmark, for better or worse, is the Russell 2000. And it has roughly 23%, a little over 20% in financials, mostly commercial banks, which just isn't like you mentioned, isn't even on the menu for private equity managers. And what we saw from PitchBook Data, where we kind of bring in our value add on the data side, is that we've tracked where PE managers are investing more broadly, not just in the take privates, but in the private deals as well. And we found that they are actually started making a bet more on Tech in 2012, which turned out, I think to be a very, very good bet. But we want to be able to capture that right off the bat before we go into that security selection mode. We actually have the data on where these managers are investing in terms of sectors. So it's kind of an easy first step on a replication strategy to mirror that sector selection as well, which has turned into a positive alpha bet. Over looking at our back tests. Right. Overweight tech, underweight financials, it was a good period to do that. And so again, that is a source of alpha, but one that is replicable.
- [19:09]
Bill Kelly
And then leverage and leverage.
- [19:12]
Andrew Akers
Yep, that's another big differentiator. If you look at the average company in the Russell 2000X financials, you're looking at a financial leverage ratio, total assets to equity of around, depending on the period, 1.3 to 1.5 times. So there is some embedded leverage there. If you look at the buyout transactions, it's more like two to two and a half times financial leverage. Again, depending on the period it's come down More recently, as interest rates have gone up, as interest rates go back down, at least moderately, we've already seen that tick up on the margin. And again, just another huge return driver. If you think about over the last 10, 15 years, which I think we'll probably look back and say it was almost an ideal environment for the buyout strategy, that's just been another tailwind. You lever up with interest rates that are low, you get multiple expansion, you get a strong economy for the most part, and that's going to be another tailwind. And that's not alpha and that is taking on more risk. Right. Which may be a prudent decision, but certainly not something that I think when you look at private equity returns and then just compare them to a standard benchmark, that's not an apples to apples comparison and not something that you can attribute to manager skill.
- [20:38]
Bill Kelly
So jumping around a little bit. And I'm going to touch upon this, I think in another way too, Andrew, but Morningstar is in the index business. I'm not sure where those business leans over or intersect with what PitchBook is up to. But put aside the whole discussion on leverage. If you just had this machine learning algorithms and looked for these, take private companies in the public markets and you came up with your target shopping list. As I said earlier, if nothing else, I've got probably more of a tech play than financial services. So maybe concentrated in an industry where I'm maybe looking for more exposure. Although you have a lot of that exposure in the s and P500. But even putting the leverage aside, could that be an index? That could be interesting for me as an investor if I was focused on value investing.
- [21:26]
Andrew Akers
I mean, it's the first step to replication. Just you get very different exposures than you would just buying an off the shelf index strategy. You could take that further. One of the main drawbacks of the Russell 2000, what people lament a lot is there's just a lot of, as you say, non performing companies. Just look at negative earnings earning companies. In the Russell 2000, it makes up probably more of the index than you would like as an investor. Obviously value may or may not reflect that, but kind of taking this a different way, going back to the security selection model, and you mentioned the value tilts on a free cash flow basis. But another thing we found with what managers, private equity managers are looking for is, is those income earners, those strong free cash flow generators. So there is also a tilt more on the quality side, I would say, in this strategy, which makes sense because again, going back to that buyout model. If you're levering up a company that two and a half times, you better have some cash flow to cover those interest payments.
- [22:36]
Bill Kelly
I agree, and maybe I'll stick with this theme and I'll come back to PME separately in a moment, but maybe a slight infomercial. You, myself, your colleague Hillary Week and Dan Harms from Third Wire, maybe right before, right after this podcast, are doing a webinar on a very similar subject and it's going to look at some of your research and talk about wealth approaching the private capital. Not just private equity, private capital markets. And as we mentioned before, if that's where value creation is happening, if it's where yield is happening, yes, by all means. But how? And performance dispersion is so wide, as I said earlier, if PitchBook or Vanguard or somebody could come out with a true replicating index, it would be an absolute home run for the end investor. And I think one of the things I think people don't pay enough attention to, and it came up in a prep email back and forth among some of us earlier this morning, is that not all LPs are created the same. And there are some challenges in the institutional space, but the Abu Dhabi Investment Authority is up there with the most sophisticated. But pick any big pension plan, they have the resource to go in and take a closer look at the underlying portfolio companies. They can get involved through an elpac. They have ILPA on their side, they have the LP LED secondary market. So there's a whole different level of sophistication and they kind of know what they're buying. I worry about wealth in that we're treating these LPs as if they're just a garden variety LP from yesteryear in the institutional space. And their makeup is very, very different. And I do fear that the access is the easy part. Getting a differentiated return can be quite difficult. So I know not the intent and maybe be careful what you wish for, but is the work that you've done thus far, is there a little bit of a light at the end of this tunnel to say maybe we could create a public market replication that maybe doesn't give you everything because you're not going to get the operational alpha, but it can get you pretty close and ultimately give you a return that's differentiated from what you get very, very cheaply in the public equity markets?
- [24:45]
Andrew Akers
I think the short answer is yes, and at the very least it's a start. And I think as we go down this road, and I do think Private markets are at a bit of an inflection point right now where this is just going to pick up steam, where the institutional market may grow along with the asset market, but net new dollars headed in from that channel are going to slow. And so everyone's eyes have now turned to the private wealth channel. We had a paper out earlier, or I guess last year called the Evergreen Evolution, looking at that and estimates of the high net worth channel around globally $450 trillion with very little currently allocated to private markets. If you start doing some of that math, just a little allocation to private markets, you start to get to a relatively big number. Perhaps that current estimate of private market AUM at around 15 trillion globally, it could be that there's about as much of that locked in in the high net worth channel. And so you don't have to look any further to kind of the corporate strategy of these publicly traded gps, your Blackstones, kkrs, Paul's of the world and how they're investing in that business. And it's clearly on their radar of kind of the next frontier of private markets and where they can generate assets. And I think the question is, do we currently have the products and the implementations to fit those needs? And you're completely right. The big institutions are a completely different animal than likely your high net worth, who's running through an advisor. They're not familiar with how to build these portfolios. They're not even able to access a lot of what you mentioned on the institutional side secondary market. And so I think the ideas such as this, which kind of simplify the implementation and not just simplify the implementation, but allow a product with much lower fee structure is a big component of that. You mentioned can we build an index for the private markets? Well, I think a really important characteristic of index strategies has been on the fee side. Right? You're harvesting beta. So there really shouldn't be any management fees for that. It should be more on the operational cost to implement, which could certainly be higher on this side. But I think that's the big hurdle right now is do we actually have the products and implementations as these companies push into the high net worth channels to actually satisfy those investor needs and create more of a symbiotic relationship that it's good for those companies in their aum, but also beneficial for investor portfolios which those interests, as you know, aren't always aligned.
- [27:37]
Bill Kelly
Oh, I agree. And if you accomplish that, Alfred Noble will walk out of his grave and pin the Nobel Peace Prize for Economics right on your chest. Andrew So I hope for that day. So I think the best use case is measuring performance and maybe we can conclude with that. But before we get there, you did learn a few things about volatility drawdown and sharpe ratios. Could you just make a comment on observations there? And for the cliff asness fans, maybe volatility laundering was proven out in some of the work that you did?
- [28:07]
Andrew Akers
Yeah, definitely. We've done a lot of work on this. Actually, one of my first papers I released that pitchbook looked more specifically at the volatility smoothing. We say volatility smoothing, I'll leave a cliff to call it volatility laundering. But the idea that private market strategies, particularly private equity, is a diversifier, is a low risk strategy that is an alternative, I think is just flat out wrong. I think the alternative moniker has basically been applied to everything other than your traditional public equity and your Bloomberg Barclays aggregate index on the bond side. But as you mentioned at the top right, equity is equity and they're going to share a lot of the same risks. So one of the things that we have the advantage of in building this replication strategy is we have daily pricing and so we can look at, okay, what happens when you sector adjust, add leverage, and then kind of look at the same type of security selection criteria, what does that do to the risk side of things? And we can measure that in more traditional sense in terms of volatility and drawdowns. What we found is mainly through leverage that the replication portfolio has annualized volatility north of 30% and it's had a drawdown of greater than 50% that occurred in the depths of COVID in March 2020. And this just generally aligns with, I think, how we think about the strategy. You're investing in the bottom of the distribution of the small cap. So you could call it a micro cap strategy. You're leveraging that up and that's going to be risky. You're getting a lot of equity risk mainly and a few others. And that's, I think something again, on the private wealth side, the risk modeling of these strategies is extremely difficult. And just the acknowledgment up front that buyouts in particular, you're investing in a leveraged small cap equity strategy that belongs in your equity bucket of your portfolio and not necessarily a diversifying or an alternative bucket because your primary driver of risk there is going to be that equity risk premium.
- [30:30]
Bill Kelly
And I'm just looking back, Andrew, I thought I saw it here. And you may know this off the top of your head if you Don't. That's fine. But you mentioned annualized volume. The BRP of 31 and drawdown was 54. How did that compare to the index, the Russell 2000, do you know, was it in line with that or more or less?
- [30:47]
Andrew Akers
The standard index is going to be less and so it's north of 20% on the volume and I think 35. Just kind of ballparking off the top of my head. 35% on the drawdown primarily where I think we're picking up that extra risk is on the leverage side. So obviously you leverage that up, that's going to be a one for one on your volume increase. And so what we found is it's not necessarily that the strategy itself is inherently more risky than your typical small cap benchmark. It's the fact that after you lever it up, that's just the nature of leverage that you're going to be adding risk and return potential.
- [31:27]
Bill Kelly
Yeah. And then I don't know how much a sector like it or tech and the volatility there versus banks and insurance companies, they probably are less volatile. All things being equal, I don't know how much that comes into play.
- [31:40]
Andrew Akers
Yeah. With the shifts we didn't find that there was a huge risk differentiation on the sector side of things.
- [31:47]
Bill Kelly
So last one, and this may take a little bit of time, which is totally fine is your intent when you started out, which I think was a better way to measure success or not skill or luck of your underlying gp. And I think we covered this on a prior webinar we did with Dan and then we're going to talk about this more as well. But how do you measure success in the private market? It's very, very difficult to do. And time weighted return does not come into play at all. And you have to look at various tools and they all have their limitations and it's a bit of an acronym Alphabet soup, irr, moic. We now have the public market equivalent pme. And there is no silver bullet. You got to kind of take each one of these things and recognize what the benefits are versus what the limitations might be. But all things being equal, as you said before, if I'm going in with a PME and that might be the best way, and I think Kaya talks about this in our curriculum as well. Maybe the best way to look at it, but not the only way. But if you're starting out with an index that looks nothing like the underlying portfolio companies, you're already starting with at least one hand tied behind your back. So what were Some of your thoughts about using this primarily as a pme and I'm sure there were others beyond just the sector weightings.
- [33:08]
Andrew Akers
Yeah, and I think this could probably be its own podcast or maybe podcast series of how you measure performance in private markets. But we too at PitchBook feel that the public market equivalent is the single best measure. If you had to pick one of closed in private market performance, and essentially just quick definition is if you had instead invested the cash flows of a closed end private fund in a market index instead of the fund, how would that relative performance look like? So it takes into account those timing of cash flows which you need to. Because IRRs are obviously have some extreme limitations. And the problem with PMEs though is that they make several assumptions. One, it's not really an assumption, but you have to select an index to use. And what we see oftentimes is just a really poor index selection. Namely they go in and clients will take the S&P 500. And it couldn't be further from the portfolios that buyout managers are holding. It's still equity, right? But you're investing in your Nvidias, your Apples of the world. They just really aren't comparable. The second key limitation of PMES is that it assumes a beta of one to your underlying index, which kind of is related to the first point. You have to choose an index in which your risks are matched or else you're just not going to get a great comparison. And so the idea that we've kind of already gone through those steps very explicitly to match risks, match security selection, sector selection of private markets really lends this as a very good choice to use as a benchmark in PME calculations because we've already done a lot of that work kind of matching betas and matching different risk exposures. And when we look at the large buyout space, the buyout funds over the last few vintages, where we had the data for the replication portfolio is we found that generally they perform in line with this portfolio. If you looked at more of a standard small cap benchmark, you would come to the conclusion that there was significant outperformance in those vintages. Looking at again 2014 through the 2017 buyout closed in vintages. So just again, kind of a night and day conclusion of you can use the right measure, but if you use the wrong inputs, you're still going to potentially get to the wrong answer. And so we think that putting aside the ability for this to be an investable product and something that you would include in portfolios is it gives you a much better sense of whether your actual private managers have outperformed or not.
- [36:02]
Bill Kelly
And a very important point, and maybe to underscore it, when I first started work getting out of university, I was in New York City and very affable mayor. Mayor Koch was the mayor of New York City, very visible guy. I would see him quite often because he'd be always around the city. And if he saw you or saw a crowd, how am I doing? How am I doing? And you could measure the potholes or crime or the subways running on time. And they had a benchmark. But I think part of the challenge is if pick your private capital CEO goes running around New York City, how am I doing? I don't know. So having a better way of measuring that is critically important, particularly as the end investor gets less sophisticated. Maybe the last point on this, Andrew, is where we are in the private equity space now is that Fundex plus one is coming out, not all my capital and the current fund has been called and there's certainly a cash imbalance for a lot of these LPs and they want to participate in the next, but they don't have cash coming back to them. So as you think about all of this dry powder uninvested capital that's still sitting the LP coffers, it's still contractually belonging to the fund itself. But could this ever be, this being the brp, could this be a decent parking place for uninvested capital?
- [37:20]
Andrew Akers
I think it could. The challenge that I think you're going to find with that type of use case, and we've had some conversations with clients that are interested in it, is the timing and the volatility component. If you're going to kind of park money that you've contractually obligated to be drawn down into a closed end fund, I don't think you're going to be particularly happy with the volatility or the drawdown characteristics of the strategy. So I think you'd have to make some tweaks in that use case. What we see, maybe you don't add the leverage component. Maybe you're just looking more for that beta of one type equity risk premium, but more correlated with the actual strategy itself. But then again, it's still equity. There's potential for drawdown depending on the timing. We have seen some strategies in the space implement some hedging procedures, which I don't like from a long term perspective because that's a lot like buying insurance. And sometimes the insurance doesn't really pay, it'll bleed you dry over the long term. But perhaps in a use case such as this, where you have the money with obligations to pay it down, that you might include some hedging in the short term, you might give up some of that return potential. It might make sense there. So I do think there's probably something there, but potentially you'd have to craft that to the individual use case where you might take a max loss type of situation. But I think it is becoming probably a better conversation as like you mentioned, it's it's been taken a while to get money back, put money back in and how do you manage around those liquidity constraints or liquidity obligations in your portfolio where you're not just want to be sitting in cash? So I think there's a conversation to be had there.
- [39:10]
Bill Kelly
Yeah, I agree. And I think out of this phase in the private equity space, and it is a phase, it too shall pass. But I think we're going to learn some things, new products, new opportunities. I think we've learned that the secondary market was way too small to service a $20 trillion capital base. And I think that's going to continue grow as well. So I think that we are going to see some interesting outputs of this, not the least of which is if the goal is to be fully invested in a drawdown vehicle, how can we get that money to work sooner rather than later? So very good conversation, Andrew. Great paper. I'll put a link to it in the notes when we put this podcast out. And, and I'm not sure if this is before or after our broader discussion, which I think is happening on the February 5th. February 5th. Okay. And I put something out on LinkedIn yesterday on that. We'll make sure we promote that as well. I think it's going to be very interesting discussion as well. So thanks for your intervention here and your thought process and taking us through it. I appreciate it and more to come, I'm sure. So Andrew, thank you.
- [40:10]
Andrew Akers
Yeah, thank you, Bill. Enjoyed it.
- [40:14]
Bill Kelly
Thank you for listening to Educational Alpha. I'm your host, Bill Kelly. Learn more about the Chi association and subscribe to the show at kaya.org that's C-A I A dot org. See you next time.