Reinforcement Learning Based Robo-Advising Apps under Enhanced Mean-Variance Framework

Businessman holding tablet and showing a growing virtual hologram of statistics, graph and chart with arrow up on dark background. Stock market. Business growth, planing and strategy concept.

We develop a reinforcement learning robo-advising app based on an enhanced dynamic mean-variance framework. Robo-advisors offering digital wealth management services primarily targeted at retail investors represent a rapidly growing part of the wealth management industry. It is our explicit objective to ameliorate the shortcomings of time-inconsistency and non-monotonicity in current applications, while retaining the intuitive appeal, graphic representability of the probabilistic properties of the resulting wealth levels, and the elicitability of preferences by means of questionnaires. While predicting future return distributions represents the most compelling challenge in investment, any underlying distribution can be well approximated by utilizing a mixture distribution, particularly if we are able to ensure that the component list of a mixture distribution includes all possible distributions corresponding to the scenario analysis of potential market modes. Novel machine learning algorithms will be developed for this purpose.

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