At Ares, quant models not only help evaluate the skill of external managers and guide secondary investments, they shed light on which industries or sectors are yielding more alpha. Turetsky joined the private-credit giant through its acquisition of Landmark Partners, which has helped boost the number of quants on staff to about 30, from fewer than 10 in 2020.
The quant ambition in private markets generally falls far short of the revolution they kicked off in equities over seven decades ago. The business is defined by intermittent deal-making and hands-on management, making it unlikely math and formulas will ever be more than auxiliary to humans.
Finding the best deals
But the bet is there’s still big potential to apply quants’ signature statistical rigor to the space, if only because there’s currently so little of it.
Firms are looking to hire data scientists to help portfolio companies dissect trends and map out markets, says Stuart Wilson, executive director at recruitment firm Dartmouth Partners. Some are using their talents to help winnow down a massive pipeline of potential deals.
“They’re looking to bring data science into that process so they can say, ‘we didn’t just make this change to the business because one of our partners had done so successfully 18 years ago,’” Wilson said. “‘We are doing it because that is the direction in which the data is pointing us.’”
Over at BlackRock, the math wizards in its $US218 billion systematic unit have built a model to help identify late-stage venture investments with the highest odds of paying off either through a listing or acquisition.
Ronald Kahn, the global head of systematic equity research, says he’d hardly spared private markets any thought until a few years ago. Now, his team is increasingly involved as the firm in June set out a goal to double revenue from the asset class over the next five years.
“One of the advantages that systematic approaches have is this idea of breadth,” Kahn said. “We do get the advantage of being able to follow thousands of companies and then having a view of which are the best ones to look at.”
More data
The BlackRock team built a model that helps guide the few private investments in its systematic equity funds, and which is now also used by the firm’s private equity arm to help determine what companies might be worth meeting. It’s based on publicly available data such as information from recent funding rounds, as well as the kinds of alternative data stock pickers use to spot trends – think news articles, job postings and management experience.
“In terms of available data, there seems to be inexorably more and more,” said Kahn, who plans to turn his attention to private real estate next. “That works to our strengths.”
Kahn declined to discuss specific transactions his team has been involved in.
BlackRock has been aggressively expanding its alternative assets group, including through the acquisition of London-based private debt manager Kreos Capital last year. In its Investor Day presentation in June the world’s largest asset manager said it reviewed over 9,000 private deals in 2022, investing in about 5 per cent of them.
Meanwhile, Ares is thriving amid a boom in private credit and is nearing the close of the biggest ever direct-lending fund.
Given the unpredictable and variable nature of private-market deals, Turetsky is skeptical that quants can fully replicate what they do in stocks: crunching the data and figuring what characteristics, or factors, predict outperformance.
While there’s been an uptick in firms looking to hire quants, “the value-add is still happening at the deal level, the corporate level,” said Greg Brown, founder of the Institute of Private Capital, a research group that brings together academics like him and industry practitioners.
Systematic players face all sorts of headaches when trying to analyze private investing. Private equity and credit have easily beaten their liquid equivalents in the post-crisis era, but estimated values are notoriously unreliable and even finding the right benchmark to measure performance against can be divisive. Returns are muddled by everything from the use of fund leverage to how quickly a firm deploys capital.
Cliff Asness, co-founder of pioneering quant firm AQR Capital Management, has suggested that the challenge of parsing these numbers is deliberate. Much of the asset class’s allure is due to the stale pricing, he says — what he dubs “volatility laundering.”
That hasn’t stopped AQR from attempting to decompose private-market returns itself, while others have gone further. Quant hedge fund Two Sigma dissects alternative data for its private-asset arm, just as it does for traded shares. Private equity firm EQT Partners AB has an artificial-intelligence program that sweeps data for promising investments. Venture investor Correlation Ventures tries to write startups speedy checks with the help of its AI model.
As insights into private investing developed, a new niche has even emerged, with firms including Societe Generale and Man Group offering systematic strategies that aim to mimic private-market returns using listed equities.
Voya Investment Management is the latest to wade in. It’s preparing to market a strategy that tries to replicate the average buyout fund’s performance by following the sector mix of actual private equity deals with cheap and high-quality small-caps, says quant analyst Justin Montminy. It will leverage up the portfolio to boost returns, and use options and a kind of insurance contract to recreate their famous lack of volatility.
Key to quant involvement in private markets is a better understanding of what makes the asset class tick, according to Barry Griffiths, a consultant for allocators and private equity funds who retired from running the Ares quant team last year.
“There’s room for managers who have better information and better analytics to make better deals,” said Griffiths. “That’s more feasible in private markets than it is in public markets because information flows more slowly. You have to make a positive effort to get this stuff.”
Bloomberg
