  Machine Learning (ML) has been embraced as a powerful tool by the financial
industry, with notable applications spreading in various domains including
investment management. In this work, we propose a full-cycle data-driven
investment robo-advising framework, consisting of two ML agents. The first
agent, an inverse portfolio optimization agent, infers an investor's risk
preference and expected return directly from historical allocation data using
online inverse optimization. The second agent, a deep reinforcement learning
(RL) agent, aggregates the inferred sequence of expected returns to formulate a
new multi-period mean-variance portfolio optimization problem that can be
solved using deep RL approaches. The proposed investment pipeline is applied on
real market data from April 1, 2016 to February 1, 2021 and has shown to
consistently outperform the S&P 500 benchmark portfolio that represents the
aggregate market optimal allocation. The outperformance may be attributed to
the the multi-period planning (versus single-period planning) and the
data-driven RL approach (versus classical estimation approach).
