Predictable forward performance processes: Infrequent evaluation and applications to human‐machine interactions
研究了在二项树模型中，交易时间与绩效评估时间不一致时的离散时间可预测前向过程，给出了存在性和唯一性条件及显式构造，并发现最优策略是短视的，最后将该框架应用于自动化交易或机器人顾问中的人机交互调度。
Abstract We study discrete‐time predictable forward processes when trading times do not coincide with performance evaluation times in a binomial tree model for the financial market. The key step in the construction of these processes is to solve a linear functional equation of higher order associated with the inverse problem driving the evolution of the predictable forward process. We provide sufficient conditions for the existence and uniqueness and an explicit construction of the predictable forward process under these conditions. Furthermore, we find that these processes are inherently myopic in the sense that optimal strategies do not make use of future model parameters even if these are known. Finally, we argue that predictable forward preferences are a viable framework to model human‐machine interactions occurring in automated trading or robo‐advising. For both applications, we determine an optimal interaction schedule of a human agent interacting infrequently with a machine that is in charge of trading.