英文标题：
《Growth-Optimal Portfolio Selection under CVaR Constraints》
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作者：
Guy Uziel and Ran El-Yaniv
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最新提交年份：
2017
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英文摘要：
  Online portfolio selection research has so far focused mainly on minimizing regret defined in terms of wealth growth. Practical financial decision making, however, is deeply concerned with both wealth and risk. We consider online learning of portfolios of stocks whose prices are governed by arbitrary (unknown) stationary and ergodic processes, where the goal is to maximize wealth while keeping the conditional value at risk (CVaR) below a desired threshold. We characterize the asymptomatically optimal risk-adjusted performance and present an investment strategy whose portfolios are guaranteed to achieve the asymptotic optimal solution while fulfilling the desired risk constraint. We also numerically demonstrate and validate the viability of our method on standard datasets.
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中文摘要：
迄今为止，在线投资组合选择研究主要集中在最小化财富增长中定义的遗憾。然而，实际的财务决策与财富和风险息息相关。我们考虑股票投资组合的在线学习，其价格受任意（未知）平稳和遍历过程控制，目标是财富最大化，同时将条件风险价值（CVaR）保持在期望阈值以下。我们刻画了渐近最优的风险调整绩效，并提出了一种投资策略，其投资组合在满足期望风险约束的情况下，保证达到渐近最优解。我们还在标准数据集上对我们的方法的可行性进行了数值演示和验证。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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