In this paper we show as the neural network methodology, coupled with the Least Squares Monte Carlo approach, can be very helpful in valuing R&D investment opportunities. As it is well known, R&D projects are made in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as a sequential investment and therefore R&D projects can be considered as compound options. In addition, R&D investments often involve considerable cost uncertainty so that they can be viewed as an exchange option, i.e. a swap of an uncertain investment cost for an uncertain gross project value. Finally, the production investment can be realized at any time before the maturity date, after that the effects of R&D disappear. Consequently, an R&D project can be considered as a compound American exchange option. In this context, the Least Squares Monte Carlo method is a powerful and flexible tool for capital budgeting decisions and for valuing American-type options. But, using the simulated values as “targets”, the implementation of a neural network allows to extend the results for any R&D valuation and to abate the waiting time of Least Squares Monte Carlo simulation.

在本文中，我们展示了神经网络方法与最小二乘蒙特卡罗方法相结合，可以非常有助于评估研发投资机会。众所周知，研发项目是分阶段进行的，后续阶段的启动取决于前一阶段的顺利完成。这被称为连续投资，因此研发项目可以被视为复合选择。此外，研发投资通常涉及相当大的成本不确定性，因此可以将其视为一种交换选择，即将不确定的投资成本换成不确定的项目总价值。最后，生产投资可以在到期日之前的任何时间变现，之后研发的效果消失。因此，R& D 项目可视为复合美式交换期权。在这种情况下，最小二乘蒙特卡罗方法是用于资本预算决策和评估美式期权的强大而灵活的工具。但是，使用模拟值作为“目标”，神经网络的实施允许扩展任何研发估值的结果并减少最小二乘蒙特卡罗模拟的等待时间。