Private equity fund cash flow forecasting: the sophisticated approach
Private capital funds like Buyout, Venture Capital, Real Estate, etc., constitute highly illiquid alternative investment vehicles. It is, therefore, paramount for investors but also for fund managers to have a clear understanding of the cash flow profile of their portfolio /fund(s). Nobody wants to be surprised by unexpected cash flow events! To avoid any surprises, our two previous blog posts featured the topics of liquidity stress testing and liquidity planning for private capital fund managers and investors. In this article, we describe our innovative cash flow forecasting engine that we employ for these tasks in greater detail. Whereas some market participants believe stochastic cash flow modeling for PE is just a “too complex problem”, our approach is to challenge this problem with the most sophisticated methods available.
First, we give a general overview of the most commonly used approaches for cash flow modeling in the private equity space. Next, we describe the econometric principles of our own proprietary cash flow forecasting engine. Finally, we demonstrate how we leverage our powerful forecasting engine to deliver advanced liquidity stress testing and liquidity planning results.
There are multiple approaches to forecasting the future cash flows of a private capital fund portfolio. To give some initial overview, we distinguish between the five most frequently observed modeling choices:
AssetMetrix adopts the reduced form approach to cash flow modeling to come up with a cutting-edge solution that is, in our view, elegant and efficient. Our forecasting engine is a comprehensive suite of econometric models that uses the current macroeconomic environment to forecast the funds’ future. Using established results from the public market literature, we select a forecasting framework that is purposefully designed for incomplete information settings in the sense of Jarrow and Protter (2012) (especially with respect to deal-level information). When it comes to model estimation, the general data scarcity in the private capital world and the approximative nature of fund Net Asset Values (NAVs) are the biggest challenges for model estimation. Therefore, our market-leading forecasting engine focuses primarily on the probabilistic modeling of fund cash flows since private capital funds are inherently cash-flow-driven vehicles. Here, the statistical nature of our methodology inherently allows the derivation of so-called probability bands around the expected values, which is self-evidently vital for downside risk management (as depicted in Figure 1).
Available in the market for several years now, our forecasting engine is thoroughly back-tested whenever new features or improvements are introduced. Our base forecasts, as a result, constitute a solid, safe, and sound basis for the actual stress testing, which follows as the next step.
Figure 1: Probability bands around the TVPI prognosis
Stress testing always depends on a base forecast of the future. Relative to this basis, several adverse (i.e., stressed) scenarios are then calculated. For the base forecast, we project all future fund/portfolio cash flows using the forecasting engine mentioned above. Next, our new Liquidity Stress Testing (LST) module creates stressed versions of these base forecasts. Specifically, our LST methodology is guided by three reasonable requirements:
Moreover, three types of shocks are incorporated into our LST methodology:
For the timing component, we employ statistical survival models, which are commonly employed for life insurance applications. The performance model draws on an advanced Generalized Additive Model (GAM), where we thoroughly tested the impact of a broad selection of macroeconomic factors on the performance of private capital funds. The FX effect is, from a methodological viewpoint, straightforward to calculate.
Figure 2 displays the six macro-market factors that can be dynamically stressed by our Liquidity Stress Testing model.
Figure 2: Example of a macroeconomic stress scenario
If you want to access state-of-the-art analytics in a modern web portal which includes
Buchner, A., Kaserer, C. and Wagner, N., 2010. Modeling the cash flow dynamics of private equity funds: Theory and empirical evidence. The journal of alternative investments, 13(1), pp.41-54.
Buchner, A., 2017. Risk management for private equity funds. Journal of Risk, 19(6).
De Malherbe, E., 2005. A model for the dynamics of private equity funds. The Journal of Alternative Investments, 8(3), pp.81-89.
Jarrow, R.A. and Protter, P., 2012. Structural versus Reduced‐Form Models: A New Information‐Based Perspective. The Credit Market Handbook: Advanced Modeling Issues, pp.118-131.
Shen, J., Li, D., Qiu, G.T., Jeet, V., Teng, M.Y. and Wong, K.C., 2021. Asset Allocation and Private Market Investing. The Journal of Portfolio Management, 47(4), pp.71-82.
Takahashi, D. and Alexander, S., 2002. Illiquid alternative asset fund modeling. The Journal of Portfolio Management, 28(2), pp.90-100.
Tausch, C., Buchner, A. and Schlüchtermann, G., 2022. Modeling the exit cashflows of private equity fund investments. Journal of Risk.
If you want to learn more, also read our two previous articles
As evidenced by recent EU regulations, Liquidity Stress Testing (LST) is a vital topic for Alternative Investment Fund Managers (AIFMs). Find out more about LST solutions.
Liquidity planning and management continue to be crucial topics for private capital funds. In this article, we provide insights on how a sophisticated tool can simplify your planning.