Scenario Analysis Misconceptions
Scenario analysis and sunroofs – two things that require infrastructure around them to work. In this episode, Tom Vogt, Global Head of Customer Success at Charles River for Private Markets, talks through that analogy, along with misconceptions, solutions, and best practices investors need to know before they can scale in the private markets. Find out why you need more than just scenario analysis to be successful.
KALI JAKOBI:
Welcome to Data Disruption, a podcast all about data problems, solutions, and innovations disrupting the private markets. I’m your host, Kali Jakobi. Let’s talk data. Today I’m joined by Tom Vogt, Global Head of Customer Success at Charles River for Private Markets and karaoke extraordinaire. Tom, welcome to the show.
TOM VOGT:
Thank you for having me and for the delightful introduction.
KALI JAKOBI:
So today we’re going to be talking about a hot topic in private markets, one that we’ve talked about a couple times, and that is scenario analysis. Let’s talk about scenario planning. Why do investors ask for this now more than ever?
TOM VOGT:
Well, pick your cliche reason, I guess, is the simplest answer. I think if you look at our surveys, first of all, what does more than ever mean? We’ve run a survey on this over the last three years, and I think each year it’s crept up the number of investors that are asking for this to, the last survey I believe was over 85% of investors in some way, shape or form are asking if they can run scenarios across their portfolio. I think what you’re seeing is a combination of things. One is inflation. It’s going berserk right now. There’s a lot of concerns of a recession. And I think these are just signs of a general time, but the fact that they’re all happening right now and then on top of a pandemic, I think you have a lot of people thinking harder about we really need to be able to do this.
I think the second big shift you’re seeing is investors are actually switching a lot of their investments from public to private, and that’s not to say they’re going to do one or the other, they’re going to do a combination of them. And so what worked in the public space is what they want to see in the private space going forward. So they’re really asking themselves, how can they do that?
KALI JAKOBI:
And how is scenario analysis different in public versus private markets?
TOM VOGT:
Well, I think the simple answer is scale. Within publics, you have readily available information, you have a lot of very standardized information, and you also have technology that’s been around for a long time. In the private space, it’s a perfect storm of the opposites of that. There really is no access to information. A lot of it comes directly from third parties, which means it is not going to come in a standardized format. And technology within the private markets is fairly immature at this point. But I think what does exist, there’s a lack of education in the market around what is there to actually help in this.
KALI JAKOBI:
And in terms of the education process, what does that look like?
TOM VOGT:
Well, I think that starts with podcasts like this, Kali.
KALI JAKOBI:
A shameless plug for my podcast. You’re already my favorite guest. What does this look like in terms of a real life example for private markets? Do you have any client stories that you could share?
TOM VOGT:
I have lots of them and I’ll keep them nameless, but I will start with two types and then maybe I’ll give a couple of examples. So really when you think of running scenarios, it’s type of scenario and then it’s type of complexity. So first type of scenario, within Charles River for Private Markets, certainly we go across the investment life cycle spectrum. So we see scenarios at all times. In the origination phase, for example, completely different types of scenarios that you would run there from when you are actually managing a portfolio of held assets. And we’ve seen each of those types. We’ve seen originators trying to manage models at scale, meaning, and we call that scalable modeling. They literally want to deploy one model under a portfolio of investments and see the impact on their new portfolio. When they’re in the asset phase, it’s the opposite problem. They have very bespoke models and they want to be able to run scenarios across those.
So if I go into examples of clients, I can give you an example on each end of the spectrum of simple versus complex. In a simple form, we’ve had a client that said, “Look, we absolutely understand this and we have bespoke models. We just want to run these three inputs through each model to get this one output.” Super easy. And I’ll speak a little bit later about how that gets set up, but that’s very simple, very easy to set up, but it has to be across bespoke models. That’s the challenge they’re trying to solve. The more complex client, same problem, but the inputs and outputs were magnified by hundreds. They had 15 common scenarios they wanted to run, but it was inputs that were quarterly for each of those 15 scenarios. So they were changing quarterly values over 30 years for 15 different inputs, and then they wanted to run 20 scenarios of those inputs across their entire portfolio. So far more complexity there, but also very well defined what they wanted to get to.
KALI JAKOBI:
So when it comes to, I guess, Charles River for Private Markets implementation specifically, how difficult is this really?
TOM VOGT:
Well, the actual technical implementation is really very simple, and we’d go through that with any client to show them. We really connect to a bespoke model in a very simple way. So best answer is as long as you have people that understand how to work a model today, they’re going to be able to connect it to Charles River for Private Markets directly. It doesn’t require technical expertise. What tends to be hard is the definition. Most clients want scenario analysis, but they haven’t really defined what scenarios they even want to run. So we add efficiency, we add scale in really massive ways, but it doesn’t magically make a scenario that you speak run through 100 different bespoke models. So for example, if you want to know the impact of higher interest rates across your entire portfolio, your model actually needs to have an interest rate as an input. While it might sound obvious, that’s a mistake a lot of clients do make is the magic wand theory, so to speak.
KALI JAKOBI:
So no magic wands, no wizards here, but what would best practices for investors look like if they’re interested in deploying technology to do scenario analysis?
TOM VOGT:
Yeah, best practices actually start a little bit before that. So I would say the first best practice is make an owner of this internally. I say this all the time to different clients, and this is part of the education, so even coming back to the question you asked before. You wouldn’t buy an accounting system without having a controller internally that’s going to oversee the close process every month of your books. So you’re not going to put a scenario analysis system in place without having an owner of your modeling process. So it’s really important to have an owner of this and make sure that you’re having somebody have accountability for this as an initiative, because that’s what it is. It’s not technology. This is an initiative you have that you want to run now internally.
The second best practice I would probably highlight would be, and this is where we would come in within an implementation, is having a prep session to best prepare your models for this process. So I think many of our clients, they want to know how to get started and right after the owner, this is what we say is we’re going to have a session where we walk through with you and we actually do a model audit and we talk about what outcomes that you want to achieve and we’re going to give you feedback on how to get started from there. And we do try to keep that very simple, but it also lends itself to becoming a recommendation, because that is the challenge, is knowing how to get started.
KALI JAKOBI:
For sure. What are some misconceptions that you’ve seen either through client experiences or in the market?
TOM VOGT:
That’s a really good question. I think the first is that magic wand theory that we talked about. So I won’t elaborate on that other than to say most come in assuming that a tool in technology is a magic wand. There’s more to it than that. The second would be lack of definition. Obviously in the last couple of years we’ve been asked about a pandemic scenario. As our sales team is having conversations, that is a very normal question. Show us a scenario and can you run it for a pandemic? Sure. Tell me what that would be exactly though. What are the variables you want to move up or down? By what segments? By what regions? We can run any of that, but you have to put specificity to it. That requires definition up front.
I think another misconception would be order. So can I just sign up for scenario analysis? A lot of clients do want that. We say absolutely, but that’s sort of like buying a sunroof without the rest of the car. It can function the same, it’s cheaper, but it’s a lot more efficient to get the sunroof with the car. So that might be a little bit of an extreme example, but I think the point here is it doesn’t make a lot of sense to set up models for each asset that you’re going to run scenarios across, but not set up portfolio monitoring, which is really to collect the rest of the data you’re going to track on each of these. That might be accounting data, that might be third party data. It makes sense to do this all in one place for efficiency purposes, but also when you run scenarios, you tend to need this data anyway. So these tend to go hand in hand and there’s an order that makes sense to do those. A lot of misconceptions we get are we’ll sign up for this for just scenario analysis.
KALI JAKOBI:
And that obviously doesn’t seem like a great idea to take one piece of the toolkit away from the whole project.
TOM VOGT:
That’s right. This is a transformational type of project and scenario analysis is one major component of it and function that clients should be thinking about.
KALI JAKOBI:
Tom, talk to me about why scenario analysis is so difficult in the private market specifically.
TOM VOGT:
I’m conscious of the fact that we’re on a podcast and listening, so at the risk of making this sound very good for auditory purposes, I’ll go through it in the simplest steps I can. Picture having a 100 asset portfolio with 100 different Excel models, and say, “Go,” to your analyst team to run three scenarios through that that you’ve given them. First of all, you probably have 5 to 10 people that own all those models. So they have to individually open each model up, figure out a way to plug that scenario into it, run it, and close that model. Someone else will aggregate the information from that. That means opening that model up, extracting cash flows or whatever it may be, and aggregating it.
Now picture in the middle of that process that two things happen. One, some of those people are off, right, maybe vacations or whatever. So they come in halfway. Picture in other instances, people don’t run them correctly or something changes, and so they have to be rerun. Think about what that means for the aggregator at the end of the process that has to reaggregate and reaggregate that information. All of this is really manual, takes 10, 12 people to do this. Ultimately it ends up taking a couple of weeks to get everybody on the same page. That’s the challenge that we’re really talking about that people are trying to solve. And I think coming back to your question on education, we need to be educating the market, which is what we’re trying to do here, that there are tools available that support this. So we can walk through what that actually means and how those actually work in practicality, but we’ve really circled at Charles River for Private Markets the areas in that process that need the most help and that’s where we focused.
KALI JAKOBI:
What would the reality of taking that process that is very complex to a technology actually look like? Because I assume magic wand can’t be waved and everything works perfectly. So what does that actually look like?
TOM VOGT:
Well, I think the starting point are some of the best practices that we’ve talked about, and if those are followed, I’ll sort of give you the outcomes that we’ve seen. So the 10 to 12 people across several weeks of a process that I mentioned before can become one person in one hour. So that’s the simplest analogy to give of what this ultimately looks like when done correctly.
KALI JAKOBI:
In terms of next steps for anybody listening to this podcast, if they’re interested in starting scenario analysis, podcasts put on by companies that do the tasks that they’re talking about can feel a little bit of a self-promotion, but that’s not what we’re about here. Tom, what should clients or investors be doing right now if they don’t know what the next right step is to figure out what would be good for them?
TOM VOGT:
I love that question and I don’t want this to sell self-serving to Charles River for Private Markets, so I’ll say this more broadly about technology. I think that one of the challenges when you sell technology is that clients often come in with a list of requirements, meaning a list of features and functions that they need to have in technology. And I think when it comes to scenario analysis and more broadly what goes with scenario analysis, which I just mentioned would be something like a portfolio monitoring. You need to think broader than that, which is be okay with what you don’t know and use these tech companies as advisors and consultants.
Within Charles River for Private Markets specifically, we really do this within our sales process. Again, not to be self-serving, but just to enlighten other organizations out there thinking about this, come in with an open mind that we can help you really think about the right way to deploy this. That’s what our sales process is for, is to design this correctly up front, not to just sell you the solution. Now of course, ultimately we do want you to buy the solution, but it’s not going to be any good to you if we don’t run a strong advisory component to it. So I think the first piece of advice I would give to anyone thinking about this is embrace who could be a trusted advisor for you in that process.
KALI JAKOBI:
That’s great advice. I know it’s difficult to not sound salesy when we’re talking about such a topic that is so near and dear to our hearts, but that’s definitely something I’ve seen here at the company is how much of a partner we want to be with clients versus anything else. Well, Tom, that wraps up my questions about scenario analysis, but I do have one final question if you’re willing to answer. And that would be, if you had to sing a karaoke song right now, what would it be?
TOM VOGT:
Wow. Well, I really appreciate that question. Let me keep it in the theme of scenario analysis and I’d say Beyonce Knowles, If I Were a Boy.
KALI JAKOBI:
I love it.
TOM VOGT:
What a scenario that would be.
KALI JAKOBI:
What a scenario that would be. We’ll have to save the singing for another time. But thanks so much Tom for joining me and thanks all for listening in.
TOM VOGT:
Thank you.
KALI JAKOBI:
Thanks for listening to this episode of Data Disruption by Charles River. If you like what you heard, share and leave us a review. It helps others discover the show and I thank you for it. And if you’d like additional insights related to this conversation or others, go to our website at www.crd.com. Till next time.
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The material discussed is for informational purposes only. The views expressed in this material are the views of the author and are subject to change based on market and other conditions and factors, moreover, they do not necessarily represent the official views of Mercatus and/or State Street Corporation and its affiliates.