约束投资组合问题的局部搜索技术
Local Search Techniques for Constrained Portfolio Selection Problems
摘要：
我们考虑的问题是选择一个资产组合，为投资者提供一个适当的预期收益和风险的平衡。相对于马科维茨的开创性的均值-方差模型，我们考虑了对投资组合的基数和个股数量的额外约束。此类约束更好地捕捉了现实世界的交易系统，但使问题更难用精确的方法解决。我们探讨使用本地搜索技术，主要是禁忌搜索，投资组合选择问题。我们比较和结合以前的工作，利用局部搜索方法的投资组合选择，我们提出了新的算法，结合不同的邻域关系。此外，我们展示了如何使用随机化和一个简单的形式的自适应简化了大量的关键参数的设置。最后，我们展示了我们的技术在公共基准测试中的表现。
英文摘要：
We consider the problem of selecting a portfolio of assets that provides the investor a suitable balance of expected return and risk. With respect to the seminal mean-variance model of Markowitz, we consider additional constraints on the cardinality of the portfolio and on the quantity of individual shares. Such constraints better capture the real-world trading system, but make the problem more difficult to be solved with exact methods. We explore the use of local search techniques, mainly tabu search, for the portfolio selection problem. We compare and combine previous work on portfolio selection that makes use of the local search approach and we propose new algorithms that combine different neighborhood relations. In addition, we show how the use of randomization and of a simple form of adaptiveness simplifies the setting of a large number of critical parameters. Finally, we show how our techniques perform on public benchmarks.
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