10.5061/DRYAD.H628H
Huo, Xiaoguang
Cornell University
Fu, Feng
Dartmouth College
Data from: Risk-aware multi-armed bandit problem with application to
portfolio selection
Dryad
dataset
2017
portfolio selection
multi-armed bandit
2017-10-17T12:59:52Z
2017-10-17T12:59:52Z
en
https://doi.org/10.1098/rsos.171377
87019 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
data and codesThe data and matlab codes can be used to replicate the
results in the paper.Data_and_codes.zip