This article proposes a factor model approach that robustly explains the valuations observed in private market transactions, effectively addressing the biases inherent in appraised or estimated valutations.
Summary
Problem: Illiquidity in private markets complicates the valuation of private companies. As a result, appraisals use subjective inputs, often resulting in smoothed and stale valuations, which in turn lead to unrepresentative benchmarks that misrepresent the risk and performance of private markets.
Solution: A factor model calibrated with the latest transaction data can help refine sparse, noisy, and biased inputs into evolving factor prices that, on average, more accurately explain transaction prices.
Findings: Key factors affecting valuation include size and country risk, which reduce valuation on average, and profitability, leverage, and public market valuation, which increase it.
Application: The factor prices from the model can be applied to unlisted companies to produce a robust estimate of current market prices, consistent with fair-value accounting standards. The dynamic calibration of the model can also facilitate more frequent valuation and realistic risk estimates, by incorporating incremental information from each new observed transaction. Such an approach can improve asset allocation, performance attribution, and the monitoring of private investments.