基于监督神经网络的非流动性另类资产现金流预测
Supervised Neural Networks for Illiquid Alternative Asset Cash Flow Forecasting
摘要：
机构投资者在其投资组合中增加了非流动性另类资产的配置，如私募自有资金，但关于非流动性另类资产现金流预测的文献非常有限。私募自有资金的净现金流通常遵循J曲线模式，但出资和分配的时间和规模取决于投资机会。在本文中，我们开发了一个基准模型，并提出了两种新的方法（直接与间接）来预测私人自有资金的现金流。我们引入滑动窗口方法应用于我们的现金流数据，因为不同年份的基金包含不同长度的现金流信息。然后，我们将数据传递给LSTM/ GRU模型，以直接或间接（基于基准模型）预测未来现金流。我们进一步将宏观经济指标纳入我们的数据，这使我们能够考虑市场环境对现金流的影响，并应用压力测试。我们的研究结果表明，直接模型比基准模型和间接模型更容易实现，但预测的现金流量仍然更好地与实际现金流量保持一致。我们还发现，宏观经济变量提高了直接模型的性能，而间接模型的影响并不明显。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
