Risk-Factor Modeling Your Private Equity Portfolio

Three researchers use risk factors to optimize private equity portfolios, and then wonder, “Why hasn’t this been done before?”

(March 28, 2013) - Investor sophistication in allocating to private equity has lagged behind nearly every other asset class, according to three quantitative investment experts, and so they decided to try and bring it up to speed. 

Using risk factor-based models, S. P. Kothari (a Massachusetts Institute of Technology management professor), Gitanjali Swamy (founder of quantitative research outfit Alternative Asset Risk Management), and HEC Paris MBA candidate Konstantin Danilov attempted to create a superior risk-reward profile for real private equity portfolios. 

The study's results highlighted the researchers' original question: Why hasn't someone done this before?

The author's dataset comprised of five private equity portfolios belonging to top-tier institutional investors: the California Public Employees' Retirement System (CalPERS), New York State Common Fund, University of California Regents, Washington State Investment Board, and the Univeristy of Texas Investment Management Company. 

In total, these portfolios represent nearly $200 billion in private equity investments, or roughly 10% of the entire industry. A custom algorithm evaluated the holdings for diversification and weighted risk factors, then eliminated investments that neither reduced overall risk nor increased the portfolios' expected returns. Cuts were limited to under 10% of the original portfolios.   

On average, the modified portfolios outperformed the originals by 10%, based on market data from 1999 to 2010. CalPERS had the most to gain through risk factor-based optimization: the algorithm boosted the public pension fund's private equity returns by 18%. All five of the sample portfolios benefited from the risk-return rebalancing, although New York State's Common Fund had the least to gain at 2%.   

"Given the quality of improvement in results, as well as the simplicity and self-evident nature of the experiment performed, why hasn't this analysis been previously performed?" the authors concluded. "The lack of available data (and an overall lack transparency), as well as a shortage of tools to perform the appropriate analysis, have slowed advancements in PE portfolio management research." 

"In the absence of an adequate level of research and evaluation, the industry has settled on the claim that it is an asset class to which classical investment analysis frameworks (i.e. modern portfolio theory) do not apply. As a result, investment decisions have been based on personal judgment and/or access to 'top brand name' managers instead of a more-rigorous, quantitative approach (like that which is used for other investments)."

Read the entire paper, "Generating Superior Performance in Private Equity: A New Investment Methodology," here

 

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