We apply robust optimization and machine learning techniques to develop data-driven asset management models that are free from parameter estimation and are relatively stable and insensitive to the random market environment.

Traditional wealth management is exclusive and expensive, and generally accessible to only high-net-worth individuals. Technology-enabled wealth management, exemplified by robo-advisors, significantly reduces costs and is both inclusive and scalable, thereby offering huge social benefits and business potential.

Most of the classical asset management models are variants of Merton’s expected utility maximization model and Markowitz’s mean-variance model, yet these models have major drawbacks. Firstly, their solutions are very sensitive to the key parameters, namely the mean and the covariance matrix of the stocks. Secondly, the true underlying probability is unknown in practice; so one has to resort to empirical versions of the mean and the covariance matrix. We project that our data-driven asset management models will provide a more accurate picture of the market environment.