Data from: Risk-aware multi-armed bandit problem with application to portfolio selection

Sequential portfolio selection has attracted increasing interests in the
machine learning and quantitative finance communities in recent years. As
a mathematical framework for reinforcement learning policies, the
stochastic multi-armed bandit problem addresses the primary difficulty in
sequential decision making under uncertainty, namely the exploration
versus exploitation dilemma, and therefore provides a natural connection
to portfolio selection. In this paper, we incorporate risk-awareness into
the classic multi-armed bandit setting and introduce an algorithm to
construct portfolio. Through filtering assets based on the topological
structure of financial market and combining the optimal multi-armed bandit
policy with the minimization of a coherent risk measure, we achieve a
balance between risk and return.