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Quantifying Cash Flow Requirements during COVID-19 (part 4)

Forecasting cash flows, measuring the impact on portfolio requirements, and managing liquidity between the private side and public side of the book.

Andrea Gentilini
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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 our final installment:

Quantifying Cash Flow Requirements

You are just about to ask Eva how she models cash flows when the video call drops, and the screen turns black.

“Eva? Are you there? Hello?” You try a couple of times, but your probing finds no response. Bling! Your phone lights up with a notification. Eva just sent a text. “Be right back, animal spirits at work… hang in there.”

Animal spirits? Last time you heard about that was in the context of exuberant markets and irrational bubbles. You wonder for a while, and take advantage of the short break to jot down a few questions to ask:  

How do you forecast cash flows? How do you measure impact on portfolio requirements? How do you handle requirements on the private side and availability on the public side?

You wonder if there is more to ask when the video call doorbell rings. Eva must be back. You mouse over the app to see her again. Oddly enough, she is now logged in twice; through her phone for video, and her computer for audio.

“Give me another sec,” says Eva, ruffling a few things in the background. The video connects, and you see a hamster furiously gnawing a black cable.

“Meet William III, who happily chewed through my camera cable and got us disconnected.” You wonder what happened to William I and II, but decide not to ask.  

“Ok,” says Eva, “here’s how we handle cash flows.” She drops William III (and his newly conquered cable) somewhere out of sight.

“First, l always keep a dashboard on Novus where capital requirements forecasted by my private equity investments, over a certain look-ahead period, are matched against the liquidity that I can get my hands on on the public side.”

She pulls up Figure 1, where you see about USD 16.7m of available liquidity (until December 2020) against USD 5.8m projected capital requirements.

“So as you can see, we’re balanced now, because I can get my hands on more cash than the amount required by private investments.”  

Figure 1—Cash available vs. required until December 2020

“Wait, how can you just estimate at the top level? It’s too simplistic, each underlying fund must have its own…”  

But Eva cuts you off, “Of course, Novus lets you assign a model by fund, or group of funds sharing characteristics in common such as geography, style, and so on.”  

You think it’s good as a starting point, but counter-argue with a practitioner’s comment, “Sure, it’s fine as an approximation, but what if the GP calls you up and gives you a forecast that is far off from what is modeled?”  

“Even better,” says Eva. “In that case, Novus lets you override your forecasts with manager-provided inputs, booking each expected transaction at the time you would expect it. If fact, I had to use these exact features the other day. Let’s do it now, I can walk you through it.”  

Eva goes on to tell you that one of her GPs, because of the COVID-19 crunch, was going to accelerate its capital call profile and draw on all commitments 5x faster. She pulls up Figure 2 on Novus, where the impact of the new forecast (on the right) is contrasted with the original assumptions (left). You notice the sharp increase in exposure, and ask if you can see the impact on cash requirements.  

Figure 2—Exposure to Private Equity investments, original (left) vs. new assumptions (right)

Eva pulls up the same chart as before, depicted in Figure 3, except now she loads the new scenario as opposed to the original assumptions. You see that projected cash requirements got closer to cash available during the period. While there is some buffer (cash available less projected cash required lies around USD 6m), you know it provides no buffer in the case that things go wrong. You also know Eva likes to operate with a wider margin of error.  

 

Figure 3—Cash available vs. required until December 2020 under new scenario assumptions

During the whole clicking and dashboard maneuvering on Novus, you wonder how she could pull up the new scenario so quickly.  

“Wait a minute, are you treating the scenario as if it was an alternative portfolio?”  

“Yes,” says Eva, “it took me a while to appreciate it, but Novus had quite the idea when modeling scenarios as parallel universes, where an alternative course of events leads to an entirely different portfolio. You can compare and contrast it with others. In short, you can treat it and analyze it as if it were a distinct and independent portfolio. Also, you can apply the same level of analytics to these parallel universes. It’s like a parallel universe.”  

Your mind races to the world of possibilities this opens, and how easy it would have been to address all the permutations your board asked you to evaluate last time.  

“By the way,” you ask, “if this were a real-life situation, how would you end up solving it?” Eva flips through Novus, pulls up the liquidity terms of her hedge fund book, and swaps a fund from one model (related to a share class with classical bi-yearly liquidity and quarterly notice) into another one with more favorable quarterly liquidity (a quarterly hard lock-up, but monthly notice afterwards). “We had an opening to swap from one share class to the other this summer and we took it,” she says as she walks you through the reasoning. You try to model the impact in your head. But she is faster (and Novus faster still), and she flips to Figure 4, where Novus calculations are visible.  

Figure 4 – Liquidity in 30, 60, 90, 180, and 365 days

“As you can see,” says Eva, hovering over the light green bar on the chart, “I now have USD 18m available, which is exactly the buffer I need.”  

You are quite floored by now, simultaneously impressed and envious that Eva can manage so much complexity with a single tool like Novus (thus finding time to maintain a mini-zoo at home), and regretful that you used last year’s budget to hire yet another analyst instead of investing in Novus. Yes, the analyst has made some changes to the macros and made Excel look prettier, but it is still damned Excel. It takes 3 minutes to load, it breaks down when bandwidth is low, and—bad news—now the analyst is the only one who can maneuver it because she made changes you have no clue how to make or recreate.  

But maybe there is a valid reason to use Excel, and it just popped in your head.  

“Eva,” you say with a tone of superior understanding, “all this automation is great, but you certainly lose control of all the assumptions, don’t you? Excel is laborious, but at least you can—albeit painfully—check everything. You feel reassured by checking all the steps.”  

“And who said you can’t do this with Novus?” replies Eva, quite unimpressed with your argument.  

“Novus is transparent about assumptions and methodologies. It lets you check every step of the way and yes, if you really want to do something in Excel, you can export files into it. But Excel stops being the main platform,” she explains.

Eva picks up William III again and caresses him between the ears. The hamster is now munching a salad leaf, a much healthier lunch option than before. While his teeth crunch fast, his frozen stare seems to tell you that he is not at all impressed with your spreadsheets.  

“Anything else I can help you with?” asks Eva, quite relaxed.  

“Yes,” you answer without thinking. “Can you introduce me to Novus?”  

“No need,” she replies, chuckling, “this is 2020, go to their website and get in touch.”  

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