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Risk-aware multi-armed bandit problem with application to portfolio selection.

ABSTRACT: Sequential portfolio selection has attracted increasing interest 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 the 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.

SUBMITTER: Huo X

PROVIDER: S-EPMC5717697 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Risk-aware multi-armed bandit problem with application to portfolio selection.

Huo Xiaoguang X Fu Feng F

Royal Society open science 20171115 11

Sequential portfolio selection has attracted increasing interest 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 <i>exploration</i> versus <i>exploitation</i> dilemma, and therefore provides a natural connection to portfolio selection. In this paper, we incorporate risk ...[more]

PMID: 29291122

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