英文标题：
《Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning》
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作者：
Babak Mahdavi-Damghani, Konul Mustafayeva, Stephen Roberts, Cristin
Buescu
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最新提交年份：
2019
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英文摘要：
With the recent rise of Machine Learning as a candidate to partially replace classic Financial Mathematics methodologies, we investigate the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two assets that are intertwined. In Financial Mathematics approach we model the asset prices not via the common approaches used in pairs trading such as a high correlation or cointegration, but with the cointelation model that aims to reconcile both short-term risk and long-term equilibrium. We maximize the overall P&L with Financial Mathematics approach that dynamically switches between a mean-variance optimal strategy and a power utility maximizing strategy. We use a stochastic control formulation of the problem of power utility maximization and solve numerically the resulting HJB equation with the Deep Galerkin method. We turn to Machine Learning for the same P&L maximization problem and use clustering analysis to devise bands, combined with in-band optimization. Although this approach is model agnostic, results obtained with data simulated from the same cointelation model as FM give an edge to ML.
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中文摘要：
随着机器学习作为部分取代经典金融数学方法的候选者的兴起，我们研究了这两种方法在解决连续时间、有限时间内的动态投资组合优化问题方面的性能，这两种方法都适用于两种资产相互交织的投资组合。在金融数学方法中，我们不是通过配对交易中使用的常见方法（如高度相关性或协整）来建模资产价格，而是通过旨在协调短期风险和长期均衡的协整模型。我们使用金融数学方法最大化总体损益，该方法在均值-方差最优策略和电力效用最大化策略之间动态切换。我们使用电力效用最大化问题的随机控制公式，并用深伽辽金方法数值求解得到的HJB方程。我们转向机器学习来解决相同的损益最大化问题，并使用聚类分析来设计带，并结合带内优化。虽然这种方法是模型不可知的，但使用与FM相同的共同命名模型模拟的数据获得的结果为ML提供了优势。
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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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