风险感知的多臂Bandit问题及其在投资组合中的应用
Risk-Aware Multi-Armed Bandit Problem with Application to Portfolio Selection
摘要：
序列投资组合选择近年来在机器学习和量化金融领域引起了越来越多的关注。作为强化学习策略的数学框架，随机多臂强盗问题解决了不确定性下顺序决策的主要困难，即探索与开发的困境，因此提供了一个自然的连接到投资组合选择。在本文中，我们将风险意识纳入经典的多臂强盗设置，并介绍了一个算法来构建投资组合。通过基于金融市场拓扑结构的资产过滤，将最优多臂强盗策略与最小化一个一致性风险度量相结合，实现了风险与收益的平衡。
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
