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Robo-Advising: A Dynamic Mean-Variance Approach

In contrast to the traditional financial advising, robo-advising needs to elicit investors' risk profile via several simple online questions and to provide advice consistent with conventional investment wisdom, e.g. rich and young people should invest more in risky assets. We propose a dynamic portfolio choice model with the mean-variance criterion over portfolio log-returns that meets the two challenges. The model yields analytical and  time-consistent optimal portfolio policies and can be used for robo-advising.

This work is jointly with Hanqing Jin, Steven Kou, and Yuhong Xu.