In other asset classes, an investment strategy can reach the targeted exposure relatively quickly. In private equity, by contrast, a commitment is drawn down over the course of several years. Furthermore, the rate at which capital is drawn can vary significantly — not only between different fund managers but also from one fund to the next of the same manager. This, combined with capital being returned from realizations of investments made in the early years of the life of a private equity fund, makes it relatively rare for the exposure to equal the full original commitment.
This highlights the importance of investment pacing and cash flow modeling to manage exposures properly as well as to plan capital deployment accurately. While these tools can provide useful insights, the assumptions imbedded in the models can lead to a false sense of certainty which can, in turn, have a negative impact on a private equity investment program.
The current private equity market provides a pertinent demonstration of the limitations of quantitative models. The strong exit and fundraising markets of the past several years have resulted in many quality fund managers returning to the market much sooner than predicted by quantitative models. If investors do not have the capacity or flexibility to adjust to this unexpected development, they might miss good investment opportunities with some of their high-performing existing fund managers. Skipping a fund that is in high demand might limit future access to that fund manager, thereby having greater long-term negative consequences for the private equity portfolio. Also, the work to replace an existing fund manager and develop a relationship with a new fund manager can be substantial.
Another limitation of quantitative models is the difficulty of incorporating “trade-up” optionality. It is good practice to continuously monitor private equity markets. Doing so allows for identifying new fund managers as well as for comparing an existing fund manager to its peer set, which can provide better context for the existing manager's strategy and performance. If a potentially better manager is identified in this process, it could be worth considering investing in that manager and passing on the existing manager when they return to the market. This is difficult to incorporate into a quantitative model, and strict adherence to an investment pacing model may curtail identifying potentially superior fund managers unless they happen to be raising contemporaneously to an existing fund manager.
If the pacing models are not comprehensive enough to establish annual specific investment constraints either geographically or to the various private equity subsectors, they could result in a suboptimal private equity portfolio. Research has shown that, unlike many other asset classes, the performance of a superior private equity manager dominates the performance of the sector. Thus, manager selection is critical. For example, if the depth of U.S. growth managers fundraising in a given year is poor but a set amount must be invested in U.S. growth managers, an investor may be forced to decrease their underwriting standards and accept lesser quality managers. Alternatively, if the depth of U.S. growth managers exceeds the set investment amount, an investor might pass on high-quality managers that could be additive to the portfolio. Both situations result in a return for the portfolio that is inferior to that which could be optimally achieved. However, accepting lower-quality managers to fill an allocation could result in a significantly lower realized return whereas passing on high quality managers is largely an opportunity loss.
Certainly, investment pacing and cash flow models are useful tools for managing modern private equity portfolios, but as with any quantitative model, the input assumptions are critical to the veracity of the model's forecasts. Quantitative models in private equity are used to forecast several years into the future, and the more distant years are very susceptible to significant changes in market conditions, which can limit the value of a those forecasts. To improve the model's accuracy, it is critical to maintain close contact with fund managers so any changes to assumptions — such as their investment pace, expected distributions, or next fundraising — are updated in the models as soon as possible. Even with good communication, it is important to maintain flexibility and an opportunistic element outside of the model's constraints when optimizing a private equity portfolio.
