Institutional investors have been increasing the allocation of the illiquid alternative assets such as private equity funds in their portfolios, yet there exists a very limited literature on cash flow forecasting of illiquid alternative assets. The net cash flow of private equity funds typically follow a J-curve pattern, however the timing and the size of the contributions and distributions depend on the investment opportunities. In this paper, we develop a benchmark model and present two novel approaches (direct vs. indirect) to predict the cash flows of private equity funds. We introduce a sliding window approach to apply on our cash flow data because different vintage year funds contain different lengths of cash flow information. We then pass the data to an LSTM/ GRU model to predict the future cash flows either directly or indirectly (based on the benchmark model). We further integrate macroeconomic indicators into our data, which allows us to consider the impact of market environment on cash flows and to apply stress testing. Our results indicate that the direct model is easier to implement compared to the benchmark model and the indirect model, but still the predicted cash flows align better with the actual cash flows. We also show that macroeconomic variables improve the performance of the direct model whereas the impact is not obvious on the indirect model.
翻译:机构投资者一直在增加流动替代资产的分配,如其投资组合中的私人股本基金,然而,关于流动替代资产现金流预测的文献非常有限。私人股本基金的净现金流量通常遵循J曲线模式,但捐款和分配的时间和规模取决于投资机会。在本文件中,我们开发了一个基准模式,提出了两种新颖办法(直接对间接),以预测私人股本基金的现金流量。我们采用了一种滑动窗口办法,在现金流量数据中适用,因为不同年份的基金包含不同的现金流量信息长度。然后,我们将数据传送到一个LSTM/GRU模型,以直接或间接预测未来的现金流量(以基准模型为基础)。我们进一步将宏观经济指标纳入我们的数据,使我们能够考虑市场环境对现金流量的影响,并进行压力测试。我们的结果表明,直接模式比基准模式和间接模式更容易实施,但预测的现金流量与实际现金流量更加一致。我们还表明,宏观经济变量改善了直接模型的绩效,而直接模型对间接影响并不明显。