Risk-Adjusted Performance of Private Funds: What Do We Know?

[00:00:00] Speaker A: The data that we're using, the primary fund data, we think it's the best data that's available on private funds. It's from msci. It's their private capital data. It used to be the Burgess data set. And the reason we think it's the best is because it is the net of fee experience that LPs ultimately get. [00:00:16] Speaker B: Welcome to the Institutional Edge, a weekly podcast in partnership with Pensions Investments. I'm your host, Angelo Calvello. In each 30 minute episode, I interview asset owners, the investment professionals deploying capital, who share insights on carefully curated topics. Occasionally we feature brilliant minds from outside of our industry, driving the conversation forward. No fluff, no vendor pitches, no disguise marketing. Our goal is to challenge conventional thinking, elevate the conversation and help you make smarter investment decisions, but always with a little edginess along the way. Hi everyone, and welcome to another episode in our series on private markets. I'm Angelo Calvello, your host, and my guest today is Professor Greg Brown. I've followed Greg's research for years, and what sets it apart for me is that it's a rare combination of theoretical rigor and practical application. Greg's research yields insights that genuinely help LPs make better private market decisions. Today we're diving into Greg's recent paper, co authored with Professor Christian Lindblad and William Volkman. The title of the paper, risk Adjusted Performance of Private Funds. What do we know by way of background? Greg is the Van and K. Witherspoon Distinguished professor of Finance at the University of North Carolina's Keegan Flagler Business School. He's also the founder and research director of the Institute for Private Capital and a faculty director for the Hodges Scholars Program at unc. In a word, he's a leading expert in alternative investments, including hedge funds and private equity. We'll put links to Greg's CV and the topic paper in the show notes. Greg, it's great to have you on the show. [00:02:00] Speaker A: Yeah, thanks so much for having me. It's really fantastic to have the opportunity to share some of our recent work with you. [00:02:05] Speaker B: I appreciate that. Why don't we jump in and I'm going to start at the top. Tell me, what's the genesis of this paper? Why did you perform this research? And maybe what is the research question that you're really exploring here? [00:02:17] Speaker A: There's a couple motivations, I guess. One is people are just sort of always interested in what historical performance has been of different asset classes. And it's been notoriously difficult to study performance of private funds. Just because it's been hard to get a good data set and then even harder to figure out what the risk adjusted performance is because you can't use the standard toolbox. So there's been quite a few papers that have tried to do this, but some of them have come to differing results. And you don't know whether those results are because the sample periods are different, the underlying set of funds are different, or it's the methods that are different. And so what we really wanted to do was take a lot of different methods onto a common data set and sort of see what they said about risk adjusted returns for different types of private funds. [00:03:02] Speaker B: Let's go a little deeper now and start to deconstruct your research framework. What's the scope of inquiry, the types of private funds, the data sources, et cetera? [00:03:11] Speaker A: We try to have as comprehensive a view of the set of funds that we're looking at. So we're looking at venture capital funds, buyout funds, real estate funds, infrastructure funds and private credit funds. And then we further divide that universe into basically North American focused strategies. And outside North America, ideally we would go even finer than that and say, look at Europe and Asia. But the problem is European, you know, there's just not a lot of funds, you know, that are, you know, more than 20, you know, years old. So you can't say as much and you can't estimate some of the models that we're estimating without a reasonably large data set. So we kind of just do a us versus non US geographic split. The data that we're using, the primary fund data, we think it's the best data that's available on private funds. It's from msci, it's their private capital data. It used to be the Burgess data set. And the reason we think it's the best is because it's full LP experience. The actual cash flow to the penny, to the day that LPs get, that comes from how Burgess started out as a service provider, sort of a back office accounting solution for large institutional LPs. So they have a very long history of very precise cash flows across what we think is about 90% of the institutional fund universe for private closed end funds. So it's not 100% comprehensive, but nearly comprehensive. So we trust it more than data sets that say, come from public records requests or are self reported by gps, things like that. So importantly, it is the net of fee experience that LPs ultimately get. [00:04:49] Speaker B: What's the time period you're covering with this data set? [00:04:53] Speaker A: It's a little bit dependent on the asset class. So we start in 1988 for equities because there's a sufficient sample that goes back that far. Real estate infrastructure, private credit, they start a little bit later. The constraint that we have is we need to have at least five funds in the kind of bucket that we're studying. And so, for example, we'll have a little bit longer history for all real estate funds together than we would have for us versus not US real estate funds, because it takes a little bit longer to 5 to get to 5 funds in the non US real estate bucket, for example. So that that's really a constraint that's beyond our control because the kind of confidentiality requirements around reporting using the MSCI data sort of require that we have five funds in a particular bucket. So, you know, again, like looking at private credit, like we'll have a pretty long history for private credit overall. But if we were to drill down into say senior lending, then it gets shorter because there's just fewer senior lending funds 20 years ago. [00:05:55] Speaker B: Right. So you got your universe. You feel confident it's a good data set. I think the next step would be what performance metrics did you use? Now that you've got your data set? [00:06:05] Speaker A: We try to be as comprehensive as possible. We're actually continuing to work on this project and add in additional metrics. So we sort of do things that range from very simple to extremely complex. So we report the simple metrics that people always expect to see, like IRRs and money multiples. We also calculate modified IRRs. So kind of get rid of that reinvestment assumption from IRR and use a standard discount rate to mitigate that bias that comes from the reinvestment assumption of irr. Those are not really risk adjusted returns, though. We're most interested in risk adjusted returns. So we have a suite of different models that we look at the two sort of simple risk adjustment models. One is the Kaplan insurer public market equivalent. And so that's like a money multiple, but it's instead of just a raw nominal multiple, you're actually adjusting for public market benchmark. And so you can think of it as a money multiple relative to what you would have gotten if you had invested the same money in say an index fund or something. And then you can annualize that. So that's just a multiple. You can annualize that with a method that's called direct alpha. And that gives you something that's sort of comparable to an alpha that you would get if you were to be looking at Alpha for a hedge fund or a mutual fund or something like that. So it's an annual excess return. So obviously anything that's positive is going to be better than the benchmark. Anything that's negative is going to be worse than the benchmark. So those are the simple ones. But we also have estimated some more complex models. There's been a series of academic papers that have come out with more sophisticated techniques for doing risk adjustment. So one of these is the generalized pme. Another one is what's called a nowcasted performance. So these use sort of complicated statistical models, usually on portfolios of funds because you need that extra data to estimate the models. And so then we can kind of compare across all these different models. Are they telling you the same thing in terms of what risk adjusted performance is or are the models giving you different answers even when the underlying data set's the same? [00:08:18] Speaker B: I understand that you've already mentioned embedded in those comments. My next question, how do you benchmark the performance? What benchmarks do you use? This is typically a problem in private markets. [00:08:29] Speaker A: Yeah, it is. And you know, I mean, you can think about benchmarking, I guess, in a couple different ways. You know, one is are you trying to benchmark against public markets? That's what we do in this paper is we say, you know, how have private funds performed against what's hopefully a as close as you can get apples to apples, you know, comparison between, you know, public markets and private markets. Now it's never going to be perfect. And so the question is, you know, how close can can you get? We use some broad market indices. So for example, if we're looking at US equities, we'd use a total US Stock market as a benchmark. But we've also created some custom indices where we actually match on the underlying industries and the underlying geographies of the portfolio holdings for private funds. And we can do that for equity and real estate and infrastructure. We can't really do that with private credit because we don't have a kind of granular enough view of what is in the private credit portfolios to do that. But we also look at a bunch of different off the shelf benchmarks. So for US equities, for example, we say, well, what do things look like if you use the S&P 500 or the Russell 2000 or the Russell 2000 value or the Russell 2000 growth? Just so people can get a sense of how sensitive the results are to different benchmarks. As academics like to use total market indices and that seems so for example, I teach my MBA students. How do you estimate the capm? Well, the market index you should use is a total stock market index. So that's sort of our default. And it turns out that's a reasonable thing to do in equities. You get sort of reasonably intuitive answers out of that. It turns out to probably not be the right thing to do when you get to private credit. So, for example, if you were to look at a broad bond market index like the Bloomberg Barclays Aggregate, that's got Treasuries and mortgage bonds and kind of corporate bonds in it, it has almost zero correlation with private credit. And so we just didn't feel like that was a good benchmark. We ended up having our preferred benchmark being a levered loan index for private credit. And it actually has a pretty high correlation with private credit. And we get to talking about private credit results. I can kind of give you some more highlights on that. So we sort of think about benchmarking to public markets. You could, of course, benchmark to the universe of private funds, too, if you wanted to know what the relative performance was of a particular fund relative to a broad basket of private funds. But that's kind of not what we do. There's certainly tools out there that let you do that, but we're more interested in sort of trying to understand what the broad kind of market risk is and how to adjust for that in our analysis. [00:11:15] Speaker B: I assume that the benchmarks also reflect geography as well, not just asset class. Yeah, I mean, that's a lot of work to pull all that together. I got to tell you, I'm exhausted just hearing about it. [00:11:26] Speaker A: Yeah, it was a lot of work. And I'll tell you, one of the lessons that we had was that there aren't great public benchmarks for some asset classes. I think equities is relatively easy, but as I already mentioned, like credit is a. Is a bit difficult. Infrastructure is quite difficult because the types of assets that are in private infrastructure funds are very different than publicly traded infrastructure. So, I mean, one of the things that we've kind of been advocating for, as we've talked about this, is for the index providers to start thinking about how they could create new indices that would be good benchmarks for private assets. [00:11:57] Speaker B: Well, I mean, you've given us the research topic. You've talked about the scope of inquiry, metrics, benchmarking. Hey, let's just get into the key findings, and I'd suggest we do it by asset class or focus. I mean, could we start with equity funds? [00:12:14] Speaker A: So As I mentioned, we broke equities into two groups. We did venture and we did buyout funds. And you can go finer than that. I mean, you could do early stage versus late stage venture and growth capital and all that. But again, we're trying to do things at a broad level. So we just did venture and buyout. Buyout has historically seemed to have very good risk adjusted returns. There's been a series of papers that evaluate what the beta is of buyout funds relative to public indices. And those numbers have been sort of converging near one. And in fact, with our more recent sample and a host of different models to estimate betas, we almost always end up with a buyout beta that is right around one, like very close to one. [00:13:02] Speaker B: Does that surprise you? [00:13:03] Speaker A: You know, I think it surprises a lot of people because folks assume that buyouts are going to be riskier than the market as a whole because they're levered up more than public companies. And we don't cover this in this paper, but there's other research papers that talk about why that might not be the case because you're selecting on certain types of companies that when you take them private or keep them private, that might have different risk characteristics than the broader sample. So I think, you know, maybe 10 years ago would have been very surprising because we didn't have some of the research we have now and people just sort of assumed that more leverage means more risk. But what we find is that for probably a variety of reasons, that risk is, is mitigated for buyout funds. And so even with a higher leverage, the risk level is about the same as the, as the market as a whole. That means that you don't have to sort of do a lot of additional discounting on the performance of private equity. And so we tend to find, depending on the sample period and the geography and stuff, that buyout funds have excess returns alphas that are in the 3% range, roughly on average. Of course, some vintage years it's higher and some it's lower. But the long run, history is outperformance of about 3% on a risk adjusted basis, which is very similar to what you get on an unadjusted basis just because the beta is about 1. Now venture is a different story. So venture has had historically better performance than buyout funds. Again, on average, there's a huge dispersion at the individual fund level for venture. But if you take all the venture funds and pool them together, that average performance has been pretty good and on average better than buyout funds. But here's the issue. That higher risk, a lot of that higher risk is actually systematic risk. It's beta. So when we do these models that estimate beta, we get much higher numbers, numbers that range from like 1.4 to 2.3 or so. And so what that means is that the alpha that you get is actually zero or maybe even negative in some samples from Venture, because even though you're getting a higher return, it's not enough compensation for the additional risk that you would have gotten historically from Venture. So I mean, I should reiterate that this says nothing about what's going to happen in the future. Right? And I mean, who knows what's going to happen in the future. All we can really say with any reliability is what's happened in the past. I would say I feel fairly confident that the risk profiles of funds will continue in the sense that Venture will continue to be riskier than buyouts. But it very well could be that Venture way outperforms buyouts in the future or they both underperform public markets or any, any combination of results. We're really only trying to get an accurate picture of what's happened over the last 30, 35 years. [00:15:49] Speaker B: How about the debt funds? What'd you find there? [00:15:52] Speaker A: Debt funds also look pretty good. So again, we measured performance relative to levered loan index. Taken as a whole, private credit is riskier than levered loans. That's probably not a surprise because they tend to be typically riskier companies, smaller loans. So we find a beta of about 1.3 of for private credit relative to levered loan index. But even with that kind of additional risk penalty, the it looks like levered loan, or excuse me, it looks like private credit is still providing maybe 2, 3, 400 basis points depending on what period you look at and what type of fund you look at. We find some sort of reassuring results when we look at subcomponents of private credit. So for example, senior debt is safer than distressed debt. And that's sort of the way you would expect things to line up just given the nature of the loans. But on average, they tend to have performed well. One other thing that's interesting, go back to the equity results I mentioned this too, is that when we do this careful analysis of benchmarking funds by geography, a very different picture emerges for the rest of the world. And this is true really across asset classes. People have just sort of assumed that private fund performance outside the US has been bad because we just sort of look at the returns and be like, oh wow, those are quite A bit lower than what we've seen in the US but it turns out that when you, when you benchmark foreign funds against foreign benchmarks, you know, so for example, you take US equities relative to the IFA index, that actually the outperformance in the rest of the world has been more than in the US So again, total returns are lower, but relative returns to foreign benchmarks have been higher. So we thought that was sort of an interesting result and something that we saw really across different asset classes is. [00:17:49] Speaker B: That one of the surprising conclusions you came upon. [00:17:52] Speaker A: Yeah, yeah. I mean, that one was surprising because very few studies have done that. People would look at say a U.S. sample and a U.S. benchmark and sort of ignore the rest of the world or they would look at a global sample and use a global benchmark or even a US benchmark. But there's really a few studies that have looked at just non US funds with just a non US benchmark. [00:18:14] Speaker B: So yeah, let's go to the other category, real assets, the real asset funds. What'd you find there? [00:18:21] Speaker A: Yeah, so it's a mixed bag. Real estate funds have really not performed well on both a kind of unadjusted basis and a risk adjusted basis. We don't find particularly interesting performance. I'd say sort of like on par with public markets at best. And we do find that, for example, real estate funds, which the funds we're looking at, tend to be more value add opportunistic funds because we're only looking at closed end funds, not sort of odyssey core real estate type funds. But sort of surprisingly, they seem to be a little bit less risky than public REITs, which we use as the benchmark. But even with that kind of additional little bit of tailwind from lower risk, they still haven't performed in a way that would make them particularly interesting relative to public REITs. That result is maybe not hugely surprising. We have seen some other papers over the last 10 years using similar data set that have found sort of middling performance for real estate funds. Maybe the risk assessment that they're a little bit less risky than REITs is new and interesting, but again, who knows? Like, real estate has been a weird asset class for the last 25 years. Right? I mean, you think about, I mean, there's been sort of like 200 year floods for the real estate industry in the last 25 years between the global financial crisis meltdown and then Covid really hitting office, which had been a cornerstone of, of commercial real estate, you know, hitting office properties really hard. So you Know again, the future might be very different, but the past has not been great. The flip side on real assets is that infrastructure seems to have done very well. You know, infrastructure is a newer asset class. Most of the assets in the funds that we examine are post gfc. So you know, it's, I would say I don't feel like the results are as reliable just because we have a shorter window to examine for infrastructure. But the results are quite good. We see consistent outperformance in the 2 to 3% range per year on a risk adjusted basis. I will say that another finding from this study is that some of these more complicated models are hard to estimate on smaller samples or international samples. And so we have to rely on sort of the, the simpler risk adjustment methods for some of these asset classes with shorter time periods or in looking in foreign markets. But that said, where we can do the estimates, it does look like the risk adjusted performance has been quite good for infrastructure. And we go back to this notion of what's the right benchmark when we think about a public benchmark for infrastructure. If you were to say, look at the MSCI infrastructure index, it's got a lot of utilities and telecoms and energy assets in it, which is a bit different than what you see in private infrastructure where you're much more likely to see industrial companies, smaller companies, less regulated companies in many instances. So we do worry a little bit that there's sort of an apples and oranges comparison there. But you know, we don't, we don't really have the ability to create a custom global infrastructure benchmark given the data that we have. We can, we can do it in the US because we have access to the kind of underlying set of equities that we could build an index with. But it's harder to do that internationally. [00:21:49] Speaker B: Can you pull it all together? What are your overall conclusions? [00:21:53] Speaker A: I think the overall conclusions are that you definitely get a clearer picture of what performance has been. Relative performance has been looking at a long time series and looking at risk models that are sort of, at this point, many of them are kind of off the shelf type risk models. So performance has, I would say, generally been good. There's exceptions to that, especially on a risk adjusted basis where sort of real estate and venture capital may not have given enough juice to make the private funds worth the squeeze. But in general, taken as a whole, private funds have provided what seems to be reasonably good outperformance and performance. It's probably worth the cost of setting up a program to invest in and private assets that said, we know there's a huge dispersion. Individual results will vary because, you know, in some cases there's questions around access in venture. For sure there's access questions. A lot of the most desirable funds are only available to certain investors. And so that might lead to further headwinds for some investors in venture, but makes venture a really logical choice for the folks that can actually get into those funds. So I think, you know, all this needs to be taken with a bit of how does this reflect on me as an individual investor or institute, an institutional investor that's, that's making decisions not about an asset class as a whole, but about individual funds. Because no, no, you can't, you can't index in private markets. Right. Nobody can own the, the index that is essentially kind of what we're trying to examine. So I think, you know, that generally positive takeaway is a key conclusion on the, the modeling front. I mean, basically what we find is that the simpler risk models, These Kaplan Shor PMEs and Direct Alphas, which are very easy to estimate, you can do them in a spreadsheet. I teach my MBA students how to calculate them. They give very similar results to these more complicated models that can be very hard to estimate, sometimes impossible to estimate if you don't have enough data. And so for most people it's probably not worth the brain damage of trying to do these more complicated models. You should do risk adjustment. You shouldn't just be looking at IRRs and multiples. But even the simpler risk adjustments, the PMEs and direct alphas, tend to give you sort of the same conclusions as looking at the more complicated models. [00:24:13] Speaker B: And that seems to be a takeaway for LPs. I mean, that is one of the takeaways. The other thing that's top of my mind is the whole issue of benchmarking, constructing the benchmarks. If they're doing this internally, I mean, that's a non trivial task, but it's a critical task if they're actually going to look at the risk adjusted returns. [00:24:32] Speaker A: Yeah, and, and I, hopefully there's going to be more advances. I mean, I wish we had sort of really clear prescriptive advice on, on how to pick a public benchmark for assets. But I think what we, what we identified is a need for, for more work in that area. And you know, I think we'll continue to think about it. But you know, we're, we're never going to be in the, in the kind of benchmark index creation business. So my hope is that folks like MSCI and S&P and LSE, you know, think more about how they could create benchmarks that people could use that would, you know, be as apples to apples as possible for private funds. [00:25:07] Speaker B: At the top of the episode here, I was talking about how you have these practical applications that are kind of constructed within this theoretical rigor. We've talked about looking at benchmarks, we've talked about looking at data. What's the guidance you'd give to LPs, you know, that falls out of this research. [00:25:26] Speaker A: I think that looking at a large historical data set is a valuable thing to do. In some cases. You want to do that to understand how those assets fit into your broader portfolio. So if you have a big complicated portfolio and you're trying to understand, you know, how much market risk you're taking, it's important for you to understand your venture portfolio's got a beta of 1.5 or 2 or something like that and build that in. So I think that is a very practical solution. And if you're trying to evaluate an individual manager to be able to say, okay, well, you're, you know, a European buyout manager, so I'm going to try to benchmark you as closely as I can to other European buyout managers versus all buyout managers or US buyout managers, because you're going to get a better sense of what the skill is of that manager by having, you know, a geographic and strategy specific reference point for them. And so, you know, you might miss a fantastic European buyout manager if you're comparing them to U.S. buyout funds because they generate a lot of alpha. But you're not going to see that if you're using the wrong benchmark. And maybe the next 20 years, European stocks are going to outperform U.S. stocks. And so you want to be allocating to that European manager. So I think there are sort of stories that come out in terms of how there are differences across fund type and geography and time period that are hopefully going to be informative for how folks can make decisions. And it depends on the, on the type of decision. These kind of broader portfolio allocation decisions, those are, I think, well informed by this type of research. We have less to say about, you know, how do you want to evaluate an individual manager, like going back to this European buyout manager. I mean, what you really might want to do there is look even deeper at that, that specific manager, get a, you know, a very, very apples to apples, say, okay, well, they were invested in these industries, create that custom benchmark that replicates their industries the timing of their actual investments and do that at the individual portfolio company level. We have less to say about that in this research, but those are things that we're thinking about as well. [00:27:29] Speaker B: That actually takes me to my final question for you. What are your plans for future research? You're hinting at that. You also mentioned earlier in your comments that there's ongoing research here within the context of this paper. Anything else top of mind that you want to dig into? [00:27:45] Speaker A: Sure. Well, I can tell you what else we hope to include in this paper. We're kind of working on it now and hopefully this fall we'll release a revised version. We're adding additional models. So right now we have sort of three models that are sort of these complicated state of the art models, but there's other ones out there. Again, it's a lot of work to estimate some of these just to give you a sense to, to estimate one of the most sophisticated, kind of what people call a Bayesian state space model on just US buyout funds takes four days of compute time on a high performance computer. So like these are, these are very complicated models. And just because they are complicated and time consuming, we didn't include them in the first draft. We just want to get something out there. But our hope is by the time we submit this to an academic journal that we have, you know, sort of the full suite of models that are considered sort of state of the art academic models. Interestingly, we haven't found anything that's different yet. Even when we, when we do these models, they all sort of kind of point to roughly the same conclusion. You get different specific numbers, of course, but the kind of general conclusion is about the same. So I mean, that's good, I guess in the sense that the models are all kind of doing their job, but just different levels of sophistication. So, so that's what's up for this paper. Other things we're working on right now though is a lot of stuff that's focused on portfolio company level analysis. I think the last 15 years or so we've made a ton of progress in terms of being able to better understand what's happening at the fund level. But now we're starting to get data sets of what the underlying portfolio companies are and financials around those companies. And so I expect over the next five years there's going to be a wave of papers that will have really interesting things to say, say about the actual portfolio composition of different private funds. I think that'll be across asset classes too. I mean Equity is the biggest still. But I expect we'll be able to do that sort of analysis for real estate and infrastructure and private credit funds as well. [00:29:48] Speaker B: Greg, where I come from, I hear it takes four days of compute. I mean, you're doing it on a local machine. It sounds like you're not using AWS or another hyperscaler, are you? [00:29:59] Speaker A: I mean, here's the funny thing is that the types of analysis that we're doing, it works best on a local machine because things don't get spread out. And so having a really high performance workstation that is dedicated to doing this sort of analysis is actually the most efficient way to do it. And the other constraint we have is that a lot of our analysis actually happens at msci. So we have high performance machines that are actually on the MSCI system. And so, you know, we're able to, to utilize those. But yeah, it's. I mean, it's a, it's a funny thing that really we have this limited amount of data, but the models that we use have to be quite complicated that sort of like do all this resampling and optimization to estimate parameters. And so it just takes a lot of, a lot of compute power to estimate some of these models. [00:30:50] Speaker B: I think that wraps it up for us. Man, you good with that? [00:30:52] Speaker A: Yeah, I thought that was a lot of fun. And I think we hit all the highlights. [00:30:55] Speaker B: I think so too. I think you did a great presentation. I would encourage our listeners to absolutely go and find that paper. It'll be in the show notes. Or you could easily search it and dig in yourself because, I mean, we did it justice. But I think when you go into your analysis, especially when you're looking at your performance metrics and your benchmarking and then the results, I mean, paper's 56 papers, 56 pages long. I mean, I haven't read anything that long since high school, probably so. But this is great. I really appreciate the effort here and you taking the time to share this with us. I tell you, keep doing the good research. I enjoy it. I know LPs enjoy it. And thank you. [00:31:38] Speaker A: Yeah, my pleasure. [00:31:39] Speaker B: Thanks for listening. Be sure to visit PNI's website for outstanding content and to hear previous episodes of the show. You can also find us on p and I's YouTube channel. Links are in the show notes. If you have any questions or comments on the episode or have suggestions for future topics and guests, we'd love to hear from you. My contact information is also in the show Notes. And if you haven't already done so we'd really appreciate an honest review on itunes. These reviews help us make sure we're delivering the content you need to be successful. To hear more insightful interviews with Allocators, be sure to subscribe to the show on the podcast app of your choice. Finally, a special thanks to the Northrup family for providing us with music from the Super Trio. We'll see you next time. Namaste. [00:32:32] Speaker A: The Information the information presented in this podcast is for educational and informational purposes only. The host, yes, and their affiliated organizations are not providing investment, legal, tax, or financial advice. All opinions expressed by the host and guest are solely their own and should not be construed as investment recommendations or advice. Investment strategies discussed may not be suitable for all investors, as individual circumstances vary.