Working Paper: NBER ID: w26514
Authors: Arpit Gupta; Stijn Van Nieuwerburgh
Abstract: We propose a new valuation method for private equity investments. First, we construct a cash-flow replicating portfolio for the private investment, applying Machine Learning techniques on cash-flows on various listed equity and fixed income instruments. The second step values the replicating portfolio using a flexible asset pricing model that accurately prices the systematic risk in bonds of different maturities and a broad cross-section of equity factors. The method delivers a measure of the risk-adjusted profit earned on a PE investment and a time series for the expected return on PE fund categories. We apply the method to buyout, venture capital, real estate, and infrastructure funds, among others. Accounting for horizon-dependent risk and exposure to a broad cross-section of equity factors results in negative average risk-adjusted profits. Substantial cross-sectional variation and persistence in performance suggests some funds outperform. We also find declining expected returns on PE funds in the later part of the sample.
Keywords: Private Equity; Valuation; Risk-Adjusted Profits; Expected Returns
JEL Codes: G00; G11; G12; G23; G32; R30; R51
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| private equity fund characteristics (G23) | risk-adjusted profit (G22) |
| private equity funds (G23) | expected returns (G17) |