Modeling Private Investment Cash Flows with Market-Sensitive Periodic Growth
In this paper, IAS explores a modification to the TA model in which a series of periodic growth rates are used to model distributions and valuations.
Institutional investors have increasingly allocated more capital to private assets such as private equity, real estate and private credit. While these private investments can offer a potential return premium over public assets, along with diversification benefits, they can also be very illiquid and costly to liquidate. We believe one of the primary risks of allocating to private assets is that they may not be available to generate cash when needed.
At the same time, investors may be concerned that they are sacrificing too much portfolio performance for the sake of liquidity. PGIM’s IAS team has developed an asset allocation framework that can help investors determine the optimal mix of private and public assets, as well as the mix within both private and public portfolios. The framework measures a portfolio’s liquidity as the probability of meeting future cash obligations. An optimal asset allocation may maximize expected portfolio performance while meeting all future cash obligations with a desired level of confidence.
This framework can help investors examine the fundamental tradeoff between liquidity and portfolio performance. Figure 1 shows that as the liquidity requirement increases, the portfolio’s expected horizon value decreases, with steeper drops at higher liquidity requirement levels. This loss in horizon value as the liquidity requirement increases captures the “cost of liquidity.”
An important feature of the framework is that it explicitly incorporates unique characteristics of private assets such as the delay and uncertainty in capital calls, lumpy and high transaction costs, and their high idiosyncratic risk. For private assets, the framework distinguishes LP allocation value from LP investment value by including in the former the horizon value of any undrawn capital which is invested in a “default public investment” until called by the GP. The horizon value of an LP allocation, or an LP investment, is affected by the timing and magnitude of the capital calls, which are at the GP’s discretion.
The framework also allows investors to conduct extensive “what if ” analyses, i.e., what is the impact on optimal asset allocation if one or more assumptions change. For example, investors may wish to express their own views on expected private asset performance relative to public markets, and their fund-selection skill relative to their peers, which is an important driver of private asset performance. Figure 2 shows an example where an investor has a view that LP mezzanine debt will perform better than its historical performance relative to public markets, and that they can select above-average LP mezzanine debt funds. Correspondingly, allocation to LP mezzanine debt increases from 8% to 23%. In contrast, the combined negative outlook on LP buyout investments and below-average LP buyout fund-selection skill lead to a reduced allocation from 16% to 12%.
The framework highlights that the optimal public-private asset allocation, and the optimal asset allocation within both the public and private portfolios, are interrelated. Investors may use the framework to measure the cost of making their portfolios more liquid. Increasing a portfolio’s liquidity typically implies the portfolio must become less risky and have a lower allocation to private assets. Investors may find that a high liquidity requirement can be particularly expensive in terms of expected portfolio performance, and by quantifying the cost of liquidity some investors may conclude that their portfolios might be too liquid.