BELLA ANALYTICS
One of the most difficult issues fund managers and investors alike face in managing their private capital investment portfolios is estimating future returns. The reasons for this are manifold, ranging from a lack of sufficiently detailed or complete data needed for developing such forecasts to more nuanced methodological issues that reflect the unique nature of cash flows that occur in and out of private capital funds.
The Bella team has worked with a number of clients to help overcome these issues. To do this, we developed a novel Monte Carlo-style approach wherein we simulate thousands of PE portfolios using historical private equity cash flow data. Each of these simulated portfolios mirrors a client’s actual portfolio, representing one possible trajectory for how the portfolio’s performance may evolve. Moreover, once these cash flows are forecasted, it is possible to then subject them to specific client payout structures to provide many possibilities for how the cash flows from those funds would flow through specific waterfall models. By aggregating the results of thousands of simulations, we can answer questions like:
What do the average simulated portfolio’s cash flows look like?
What is the worst-case scenario in terms of performance?
What level of distributions can we expect at the 90th percentile?
As we developed this Monte Carlo approach, we realized an analysis like this could benefit a number of organizations, such as:
Institutional investors estimating capital availability for liability matching (i.e. pensioners, universities, etc.).
Investment banks engaging in complex private equity transactions.
Rating agencies determining the risk of private equity-based transactions.
Funds-of-funds managers estimating the range of returns of their portfolios.
There are two main advantages of using Monte Carlo simulations in these contexts. First, the simulations can be constructed using historical private equity cash flow data which reflect various market conditions, so the results are often more robust than predictions made using other models. Moreover, the model can be calibrated to target certain historical periods, answering questions like “what would my portfolio’s performance have been during the GFC era?” The second advantage is that thousands of models are generated, instead of a single model with set assumptions. Having a large dataset of simulated models allows for any number of analyses to then be performed on the simulated data, which would produce much more nuanced insights than any single model could. Moreover, these simulations may be passed through custom-built payout structures that mirror the unique waterfalls and needs of each organization.
The Monte Carlo approach is extremely powerful, but we are just beginning to explore the full spectrum of possible use-cases for this methodology. We are excited about the prospect of giving users access to this tool and are eager to see what types of new applications we can develop based on our users’ needs. For more information, see www.bella-analytics.com or reach out to info@bella-pm.com to learn more about this exciting new technology.