Ryan Caldbeck – Quant in Private Markets

**Patrick O'Shaughnessy** (0:04)
Hello, and welcome everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com.

**SPEAKER_2** (0:24)
Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaughnessy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.
Clients of O'Shaughnessy Asset Management may maintain positions in the securities discussed in this podcast.

**Patrick O'Shaughnessy** (0:49)
My guest this week is Ryan Caldbeck, a private equity investor who wants to bring quantitative rigor to the private markets. Ryan is the CEO of Circle Up, which uses a system it calls Helio to identify attractive investments in early stage consumer brands. While I am of course a fan of quantitative investing, I also know from experience how much harder private markets are than public markets when it comes to the transactions themselves. We discuss this and many other potential roadblocks to bringing models to private markets.
Using many individual companies as examples, Ryan explains some of the predictive factors they've uncovered in their research. We also discuss which parts of the private markets might be infiltrated by quant processes first, and which may never be. I expect many more to go on a journey similar to Ryan's in the years to come. His serves as an interesting example for ambitious investors out there. Please enjoy our conversation.
So I think a fun device for this conversation would be to treat the core model of your business, Helio at Circle Up, as almost like a biography of it. So maybe we'll start in its infancy or even the sparkle in your eye, so to speak. Let's begin by telling us kind of what this is and start at the earliest days. How did you conceive of it and begin building it?

**Ryan Caldbeck** (1:58)
Sure. So Helio is a collection of algorithms and data sets which go out into the world, first find companies and then evaluate those companies. We focus specifically on consumer and retail. So the spark, so to speak, was when I was in consumer-focused private equity 12, 13 years ago at a business school, I had a job. And the job was someone would hand me a list of companies, four or 500 companies a week and say, go through this list. Tell me which ones we should reach out to.
And we didn't have any data on the company. I would just have to Google that company. And when I would Google the company, I'd find they were in Whole Foods or maybe they weren't sold yet, just rough information about distribution, what I thought of the brand, stuff like that. Candidly, it was really boring. It did not take a lot of intelligence. And you began to get into a rhythm after you look at a couple thousand of these. You can make a decision in 30 seconds and be 85% right.
So after a couple months of that, I kind of began to think, you know, a monkey could do this. A monkey could do this churned into a computer could do this. I had no idea what machine learning was. I was not a CS major, an undergrad. It just struck me that this was not what I wanted to be doing with my life and this probably could be done by a computer. So fast forward to Circle Up, started Circle Up six years ago and we had an online portal that companies would apply to and they'd apply, they'd give us their financials. And so I wanted to take that application process and streamline it.
So we hired a data scientist whose name is Arvin, he's still with us today, doing an amazing job. And we built what we called a classifier, which is exactly what it sounds like, which is people would give us their financials and then we'd say yes or no to the company.
We also then pulled in some external information, but most of the information that we really cared about was the company's financials, revenue, gross margin, stuff like that. And as a private equity investor, I figured that we'd make the decision based solely off of their financials. If the company is declining, we're not going to accept that company. If the company's got 3% gross margin, we're not going to accept that company. When we ran that for nine months or so, we discovered that eight of the 10 pieces of information that were most predictive of whether or not we would say yes to a company were pieces of information that we didn't need to ask for.