英文标题：
《Mean-Reversion and Optimization》
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作者：
Zura Kakushadze
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最新提交年份：
2016
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英文摘要：
The purpose of these notes is to provide a systematic quantitative framework - in what is intended to be a \"pedagogical\" fashion - for discussing mean-reversion and optimization. We start with pair trading and add complexity by following the sequence \"mean-reversion via demeaning -> regression -> weighted regression -> (constrained) optimization -> factor models\". We discuss in detail how to do mean-reversion based on this approach, including common pitfalls encountered in practical applications, such as the difference between maximizing the Sharpe ratio and minimizing an objective function when trading costs are included. We also discuss explicit algorithms for optimization with linear costs, constraints and bounds.
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中文摘要：
这些注释的目的是提供一个系统的定量框架——以“教学”的方式——来讨论均值回归和优化。我们从配对交易开始，按照“通过贬低实现均值回归->回归->加权回归->约束优化->因子模型”的顺序增加复杂性。我们详细讨论了如何基于这种方法进行均值回归，包括在实际应用中遇到的常见陷阱，例如在考虑交易成本时，最大化夏普比率和最小化目标函数之间的差异。我们还讨论了线性代价、约束和界的显式优化算法。
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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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