PEARL: Private Equity Accessibility Reimagined with Liquidity

TL;DR

PEARL introduces an AI-driven framework that enhances private equity fund accessibility by using liquid assets and accounting for sale timing, leverage, and management changes, achieving better alignment with private equity benchmarks.

Contribution

The paper presents a novel AI-powered model that captures private equity fund dynamics more accurately using liquid proxies and asymmetry, outperforming previous methods.

Findings

Model correlates strongly with established benchmarks.

Outperforms initial proxies in private equity performance.

Aligns more closely with quarterly private equity benchmarks.

Abstract

In this work, we introduce PEARL (Private Equity Accessibility Reimagined with Liquidity), an AI-powered framework designed to replicate and decode private equity funds using liquid, cost-effective assets. Relying on previous research methods such as Erik Stafford's single stock selection (Stafford) and Thomson Reuters - Refinitiv's sector approach (TR), our approach incorporates an additional asymmetry to capture the reduced volatility and better performance of private equity funds resulting from sale timing, leverage, and stock improvements through management changes. As a result, our model exhibits a strong correlation with well-established liquid benchmarks such as Stafford and TR, as well as listed private equity firms (Listed PE), while enhancing performance to better align with renowned quarterly private equity benchmarks like Cambridge Associates, Preqin, and Bloomberg Private Equity Fund indices. Empirical findings validate that our two-step approachdecoding liquid daily private equity proxies with a degree of negative return asymmetry outperforms the initial daily proxies and yields performance more consistent with quarterly private equity benchmarks.