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Sequential portfolio selection has attracted increasing interests in themachine learning and quantitative finance communities in recent years. As amathematical framework for reinforcement learning policies, the stochasticmulti-armed bandit problem addresses the primary difficulty in sequentialdecision making under uncertainty, namely the exploration versus exploitationdilemma, and therefore provides a natural connection to portfolio selection. Inthis paper, we incorporate risk-awareness into the classic multi-armed banditsetting and introduce an algorithm to construct portfolio. Through filteringassets based on the topological structure of financial market and combining theoptimal multi-armed bandit policy with the minimization of a coherent riskmeasure, we achieve a balance between risk and return.

Risk-Aware Multi-Armed Bandit Problem with Application to Portfolio Selection