A1320
Title: Robo-advising under rare disasters
Authors: Jiawen Liang - University of Glasgow (United Kingdom) [presenting]
Cathy Yi-Hsuan Chen - University of Glasgow (United Kingdom)
Bowei Chen - University of Glasgow (United Kingdom)
Abstract: Robo-advisors provide automated portfolio management services to investors, and their growth has been unprecedented in the past few years. However, empirical evidence shows that robo-advisors underperformed during the recent COVID-19 pandemic. This may be because rare disasters are highly unlikely to occur and yet have a huge impact on financial markets. A novel computational framework is developed to improve the performance and robustness of robo-advising in the presence of rare disasters. It integrates reinforcement learning with importance sampling. Instead of sampling transition probability from a ground-truth probability distribution, it is sampled from a proposal distribution, where the event of interest occurs more frequently. The proposed algorithm is validated by data covering the 2008 financial crisis and the COVID-19 pandemic, showing superior performance over benchmarked methods. The algorithm is model-free and reduces the variance of value estimates through importance sampling. In addition to methodological contributions, the study contributes to the growing literature on robo-advising by considering rare events.
