A better way to build private market portfolios

A better way to build private market portfolios

Rules of thumb and traditional optimization techniques are no longer sufficient. Today’s larger, more diverse private-asset allocations call for quantitative portfolio construction techniques that combine data science with human expertise.

Private market challenges

With their potential to enhance returns and help mitigate risk through diversification, private assets play an increasingly important role in institutional portfolios.  Maximizing their desired benefits can be a challenge, however, as illiquidity, opaque valuations, limited data and implementation hurdles make portfolio construction harder than it is with public-market assets.  We have developed a framework that addresses these complexities through a three-step process.

Our process
01

Forecast

We forecast a distribution of performance for possible investment opportunities across time and asset classes.

02

Optimize

We use the forecasted risk/return distribution across opportunities to optimize a series of possible portfolio allocations based on an investor’s risk tolerance.

03

Simulate

We simulate a series of plausible cash-flow curves for each opportunity whose returns are consistent with those forecasted in the first step and aggregate them into portfolio-level cash flows for each of our optimized portfolios.

Potential benefits

As in the public markets, private asset classes have varied in performance and attractiveness over time (see the table below). This creates an opportunity to improve portfolio outcomes with active allocation decisions across the various private market strategies. Establishing a framework to determine the right strategic asset allocation enables investors to combine portfolio outcomes, risk tolerances and constraints in a cohesive and structured process.

Broad scope for active allocation

Asset class net IRR rankings by vintage

Asset class net IRR rankings by vintage

Source: Thomson Reuters, December 2020. This information is not intended as a recommendation to invest in any particular asset class or strategy or as a promise — or even an estimate — of future performance. Past performance is not indicative of future results.

The human touch

Unlike traditional marketable securities, private asset classes typically require specialized modeling techniques and carefully curated data. Importantly, any quantitative approach to modeling should be tempered by the judgment of an experienced private market investor. We therefore take a “quantamental” approach, wherein an experienced private markets investor helps determine the validity of cash flows generated from our model.

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Episodes of market turbulence have underscored the value of thoughtful portfolio construction in safeguarding desired portfolio outcomes.

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What’s next

Our improved portfolio optimization methodology, combined with careful development of a forecasting model and cash flow simulation, brings quantitative portfolio construction techniques to the private markets. It can also be extended to additional dimensions of allocation planning. Future applications could include adding granularity around region, industry sector and investment type. They could also include scenario analysis for portfolios, and analysis of portfolios that contain both private and public assets.

Download “A better way to build private market portfolios” report
Investors with larger, more diverse allocations to private assets need greater precision in portfolio construction. Our new methodology combines data science and human expertise to offer a solution.
Private market report cover
Pam Chan
Chief Investment Officer and Global Head of the Alternative Solutions Group
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Stephen Boyd, Ph.D.
Professor and Chair of the Electrical Engineering Department, Stanford University
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Mykel Kochenderfer, Ph.D.
Professor of Aeronautics and Astronautics, Stanford University
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Vidy Vairavamurthy
Co-Head of Research of the Alternative Solutions Group
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Brandon Levy
Co-Head of Research of the Alternative Solutions Group
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Jonathan Callan
Alternative Solutions Group
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Pascal Nguyen
Alternative Solutions Group
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Raphael Benarrosh
BlackRock AI Labs
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