英文标题：
《Risk-Aware Multi-Armed Bandit Problem with Application to Portfolio
Selection》
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作者：
Xiaoguang Huo and Feng Fu
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最新提交年份：
2017
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英文摘要：
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.
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中文摘要：
近年来，顺序投资组合选择在机器学习和定量金融界引起了越来越多的兴趣。作为强化学习策略的数学框架，随机多臂bandit问题解决了不确定性条件下顺序决策的主要困难，即探索与开发的困境，因此与投资组合选择有着天然的联系。在本文中，我们将风险意识融入到经典的多武装bandit环境中，并引入了一种构建投资组合的算法。通过基于金融市场拓扑结构对资产进行过滤，并将最优多臂强盗策略与一致风险测度最小化相结合，实现了风险与收益的平衡。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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