英文标题：
《Regression techniques for Portfolio Optimisation using MOSEK》
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作者：
Thomas Schmelzer, Raphael Hauser, Erling Andersen and Joachim Dahl
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最新提交年份：
2013
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英文摘要：
Regression is widely used by practioners across many disciplines. We reformulate the underlying optimisation problem as a second-order conic program providing the flexibility often needed in applications. Using examples from portfolio management and quantitative trading we solve regression problems with and without constraints. Several Python code fragments are given. The code and data are available online at http://www.github.com/tschm/MosekRegression.
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中文摘要：
回归被许多学科的实践者广泛使用。我们将潜在的优化问题重新表述为二阶圆锥规划，提供了应用中经常需要的灵活性。利用投资组合管理和定量交易的例子，我们解决了有约束和无约束的回归问题。给出了几个Python代码片段。有关代码和数据，请访问http://www.github.com/tschm/MosekRegression.
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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：Mathematics 数学
二级分类：Optimization and Control 优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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