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Institutional investors have been increasing the allocation of the illiquidalternative assets such as private equity funds in their portfolios, yet thereexists a very limited literature on cash flow forecasting of illiquidalternative assets. The net cash flow of private equity funds typically followa J-curve pattern, however the timing and the size of the contributions anddistributions depend on the investment opportunities. In this paper, we developa benchmark model and present two novel approaches (direct vs. indirect) topredict the cash flows of private equity funds. We introduce a sliding windowapproach to apply on our cash flow data because different vintage year fundscontain different lengths of cash flow information. We then pass the data to anLSTM/ GRU model to predict the future cash flows either directly or indirectly(based on the benchmark model). We further integrate macroeconomic indicatorsinto our data, which allows us to consider the impact of market environment oncash flows and to apply stress testing. Our results indicate that the directmodel is easier to implement compared to the benchmark model and the indirectmodel, but still the predicted cash flows align better with the actual cashflows. We also show that macroeconomic variables improve the performance of thedirect model whereas the impact is not obvious on the indirect model.

Supervised Neural Networks for Illiquid Alternative Asset Cash Flow Forecasting