摘要翻译：
有几种投资组合选择模型考虑到包括的资产数量及其在投资组合中的权重的实际限制。本文研究了有限资产Markowitz(LAM)、有限资产平均绝对偏差(LAMAD)和有限资产条件风险价值(LACVaR)模型，其中资产是有限的，引入了数量和基数约束。我们提出了一种全新的求解LAM模型的方法，该方法基于标准二次规划的重新表述和一些最新的理论结果。利用这种方法，我们得到了一些著名的金融数据集的最优解，也得到了一些尚未解决的大型投资组合问题的最优解。我们还在五个新的数据集上测试了我们的方法，这些数据集涉及来自主要股票市场的真实世界资本市场指数。我们的计算经验表明，用我们的算法求解二次型LAM模型比用混合整数线性规划(MILP)的最佳商业代码之一CPLEX求解线性LACVaR和LAMAD模型更容易，这是出乎意料的。最后，在新的数据集上，我们还用样本外分析的方法，比较了有限资产模型所得到的投资组合的绩效与无约束模型所提供的绩效以及与官方资本市场指数所提供的绩效。
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英文标题：
《Portfolio selection problems in practice: a comparison between linear
  and quadratic optimization models》
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作者：
Francesco Cesarone, Andrea Scozzari, Fabio Tardella
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最新提交年份：
2011
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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：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
  Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices.
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PDF链接：
https://arxiv.org/pdf/1105.3594