英文标题：
《Active and Passive Portfolio Management with Latent Factors》
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作者：
Ali Al-Aradi and Sebastian Jaimungal
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最新提交年份：
2019
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英文摘要：
We address a portfolio selection problem that combines active (outperformance) and passive (tracking) objectives using techniques from convex analysis. We assume a general semimartingale market model where the assets\' growth rate processes are driven by a latent factor. Using techniques from convex analysis we obtain a closed-form solution for the optimal portfolio and provide a theorem establishing its uniqueness. The motivation for incorporating latent factors is to achieve improved growth rate estimation, an otherwise notoriously difficult task. To this end, we focus on a model where growth rates are driven by an unobservable Markov chain. The solution in this case requires a filtering step to obtain posterior probabilities for the state of the Markov chain from asset price information, which are subsequently used to find the optimal allocation. We show the optimal strategy is the posterior average of the optimal strategies the investor would have held in each state assuming the Markov chain remains in that state. Finally, we implement a number of historical backtests to demonstrate the performance of the optimal portfolio.
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中文摘要：
我们利用凸分析技术解决了一个组合投资问题，该问题结合了主动（跑赢大市）和被动（跟踪）目标。我们假设一个一般的半鞅市场模型，其中资产的增长率过程由一个潜在因素驱动。利用凸分析的技巧，我们得到了最优投资组合的闭式解，并给出了一个证明其唯一性的定理。纳入潜在因素的动机是实现更好的增长率估计，否则这是一项众所周知的困难任务。为此，我们关注一个模型，其中增长率由不可观测的马尔可夫链驱动。这种情况下的解决方案需要一个过滤步骤，以从资产价格信息中获得马尔可夫链状态的后验概率，然后使用后验概率来找到最优配置。我们证明了最优策略是投资者在每个状态下持有的最优策略的后验平均值，假设马尔可夫链保持在该状态。最后，我们实施了一些历史回溯测试来证明最优投资组合的性能。
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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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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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