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Real-world datasets for portfolio selection and solutions of some stochastic dominance portfolio models

机译:用于投资组合选择的现实世界数据集和一些随机支配投资组合模型的解决方案

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摘要

A large number of portfolio selection models have appeared in the literature since the pioneering work of Markowitz. However, even when computational and empirical results are described, they are often hard to replicate and compare due to the unavailability of the datasets used in the experiments.We provide here several datasets for portfolio selection generated using real-world price values from several major stock markets. The datasets contain weekly return values, adjusted for dividends and for stock splits, which are cleaned from errors as much as possible. The datasets are available in different formats, and can be used as benchmarks for testing the performances of portfolio selection models and for comparing the efficiency of the algorithms used to solve them. We also provide, for these datasets, the portfolios obtained by several selection strategies based on Stochastic Dominance models (see “On Exact and Approximate Stochastic Dominance Strategies for Portfolio Selection” (Bruni et al. )). We believe that testing portfolio models on publicly available datasets greatly simplifies the comparison of the different portfolio selection strategies.
机译:自Markowitz的开创性工作以来,大量的投资组合选择模型出现在文献中。但是,即使描述了计算结果和经验结果,由于实验中使用的数据集不可用,它们也常常难以复制和比较。在此,我们提供了一些数据集,供选择投资组合时使用多个主要股票的实际价格生成市场。数据集包含每周收益值,这些收益值针对股息和股票分割进行了调整,并尽可能地消除了误差。数据集有不同的格式,可以用作测试投资组合选择模型的性能以及比较用于解决投资组合模型的算法效率的基准。对于这些数据集,我们还提供了基于随机优势模型通过几种选择策略获得的投资组合(请参阅“关于投资组合选择的精确和近似随机优势策略”(Bruni等人))。我们认为,在公开数据集上测试投资组合模型可以大大简化不同投资组合选择策略的比较。

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