A significant, and increasing, number of institutional portfolios contain private or alternative assets1. This trend is likely due to shrinking expected returns and yields in traditional public assets. Private asset market capitalization has grown to $5.8T globally in 20192, Private Equity, a large subset, has experienced 7.5x growth since 2002 compared to 3x in public markets2. Composed of a broad array of heterogeneous investments, private assets are anything but standardized. As the space is evolving to include new assets and creates unique challenges to investors, we attempt to assess the economics of some common types of investments.
In the 2020 long-term CMA methodology we will present our views on; Private Equity, specifically Leveraged Buyouts (LBO), Private Direct Real Estate (DRE), and both Private Infrastructure Equity and Debt. To properly introduce private assets into a portfolio, we suggest taking a building blocks based approach to understand and forecast return, which we will address in detail in the following sections.
While an investor in public assets can simply buy an index of an asset class, own a portion of the universe, and experience average results, an investor in private assets cannot. To align our private CMAs with our public CMAs, but still provide the custom nature private asset classes require, we built enough flexibility into our private asset models to analyze the whole market, an individual fund, or a single deal.
To emphasize the underlying reason for a customizable private model, there is simply no investable benchmark for private assets. These assets are unlisted on any tradeable market, provide at-best quarterly reporting or tender dates, and lack transparency of the underlying investments required to create a proper benchmark.
Hedge Funds and Listed Real Assets
Estimating returns for such investments is more complex than evaluating equities and fixed income, as the range of alternatives (”alts”) available runs the entire spectrum of risk. Long/short strategies, for example, behave differently than commodities, and both behave differently than global macro. And for any alternative category, it can be a challenge to know how much of the return is true, uncorrelated alpha, and how much can be attributed to broad market exposures (e.g., S&P 500 Index). In fact, academic research (Hasanhodzic and Lo, 2007; and Fung and Hsieh, 2004) suggests that a significant portion of hedge fund returns is attributable to conventional asset class and factor risks. Leaning into this research, we construct linear models using available market indexes from our traditional asset class CMAs, and measure the proportion of the estimated returns and volatility that are attributable them.
2 McKinsey; Prequin, 2019