Modeling Private Portfolio Impact during COVID-19 (part 3)
Staying on top of total fund commitments, net paid in capital, dry powder, NAV, as well as breaking down distributions by recallable vs. non-recallable.
Grinding the world economy to a halt, COVID-19 demonstrated an urgent need for better visibility into investment portfolios. Especially when it comes to the complexities of multi-asset class portfolios. Our goal with this series is to demonstrate the strategies that modern-day investors can use to make the most of opportunities, and respond productively to challenges. Today is the third installment of four parts:
- Assessing Liquid Portfolio Corrections
- Understanding Private Company Fundamentals
- Modeling Private Portfolio Impact
- Quantifying Cashflow Requirements
Modeling Private Portfolio Impact
“Before I show you this next dashboard,” Eva says while looking away for a split second, “I need just a moment, it’s bird feeding time. You can keep talking if you’d like, I’ve got my wireless headset on.” As if he understood what she said, Eva’s dog lifts his head up and turns to look out the window. You’ve lost sight of Eva, but you can hear her opening the door, birds chirping in excitement.
Meanwhile, you’re wondering whether you can take the essence of what Eva has showed you so far and apply it to your own way of approaching portfolio impact. While what she shared with the Novus Platform makes sense, your board has a different way of thinking about estimates. You wonder whether your processes could even be accurately reflected using Novus.
The bird chirping becomes muffled as Eva shuts the door and sits back down at her desk. “Sorry, they are like clockwork, showing up for seeds at the same time every day. If I don’t deliver, they start pecking at my window!”
You assure her it’s no problem, you’re just grateful that she’s taking the time to help you.
“Eva, what you showed me is great, but my colleagues have a completely different way of thinking about this. To estimate private portfolio impact, the board always wants to see—by fund—the total committed capital and paid-in capital up until the most recent date. I haven’t seen this in your dashboards…”
In the past you’ve often become frustrated with your service providers when making requests for improvements. “We’ll get back to you,” they always say. But 90% of the time they never do. And if they do, it’s typically something like, “Our engineers have put your request in the pipeline, you’ll have to wait a few months.” And of course, they forget to get back to you.
While you reminisce on your previous bad experiences, Eva asks, “You mean something like this?” She pops open another window and Figure 1 appears, exactly as you would need it.
“I guess you’d be expecting more capital calls from the funds where the total Paid-In Capital is further away from the Total Fund Commitment, in this case Renewable Growth IV,” Eva says, and she is spot on.
But wait, something doesn’t make sense. Why is Paid-In Capital for Progressive Investors I higher than Fund Commitment? This seems like a mistake; it’s mathematically impossible. Of course, you think. This is the classical Garbage-In-Garbage-Out problem. If the data is wrong, then it doesn't matter how streamlined your dashboards are. You point out the mistake to Eva.
“Oh yeah, I think you’re right. But don’t forget about fund expenses. In some legal agreements with GPs, there are expenses passed through to LPs, which are on top of the total fund commitment. The Novus team is precise in their data model—they distinguish the two and give you that level of transparency.” A few more clicks and Figure 2 appears.
Eva also rearranges the columns to allow for Net Paid-In and Paid-In Capital to follow after Total Fund Commitment. You note the speed, remembering the hours and hours you’ve spent with your team, trying to cobble data together from mountains of paper. You decide to push the limits and see how far this system can go…
“Well, the board also wants to distinguish between Recallable and Non-Recallable Distributions, is that possible?” You know all-too-well that this is one of the most painful points of data management, and one which requires care. While you start explaining the problems you’ve had in the past, Eva is already done, and Figure 3 appears, where total distributions are split into their Recallable and Non-Recallable portions.
You’re impressed, but you press further. “What if you want to show the difference between Total Fund Commitment and Net Paid-In Capital to estimate how much dry powder the GPs have left to call?” Eva does not say anything this time. Figure 4 simply appears.
You give up poking around for problems.
“I know what you’re thinking. It seems too good to be true, right?” says Eva. You nod your head. “Of course, there are problems—nothing is perfect. And there are always outlying cases you and/or the Novus team haven’t thought about. The difference with Novus is that the platform is flexible enough to create many of the permutations you may need. And in cases where it isn’t possible to solve problems like we did today, you have specialists on the line who have sat in the same seat as you in the past. They understand what you’re after. And yes, they actually get back to you.”
Your skepticism is softening, but there’s another critical aspect you would need covered.
“Eva, this is all great, but so far we’ve only dealt with manipulations of historical data. As you know, there’s a ton of work which needs to happen to forecast cashflows in the future. I have not seen any of that”
“Oh that's the fun part!” Eva says. "Do you want to see what the Novus team has put together?”