英文标题：
《Adaptive l1-regularization for short-selling control in portfolio
selection》
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作者：
Stefania Corsaro and Valentina De Simone
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最新提交年份：
2018
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英文摘要：
We consider the l1-regularized Markowitz model, where a l1-penalty term is added to the objective function of the classical mean-variance one to stabilize the solution process, promoting sparsity in the solution. The l1-penalty term can also be interpreted in terms of short sales, on which several financial markets have posed restrictions. The choice of the regularization parameter plays a key role to obtain optimal portfolios that meet the financial requirements. We propose an updating rule for the regularization parameter in Bregman iteration to control both the sparsity and the number of short positions. We show that the modified scheme preserves the properties of the original one. Numerical tests are reported, which show the effectiveness of the approach.
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中文摘要：
我们考虑l1正则化Markowitz模型，在经典均值-方差模型的目标函数中加入l1惩罚项以稳定解过程，提高解的稀疏性。l1惩罚条款也可以用卖空交易来解释，一些金融市场对卖空交易提出了限制。正则化参数的选择对于获得满足财务要求的最优投资组合起着关键作用。我们在Bregman迭代中提出了一种正则化参数的更新规则，以控制稀疏性和空头数量。我们证明了修改后的方案保持了原方案的性质。数值试验表明了该方法的有效性。
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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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