2001/04/18 by Andrea Schaerf, Schaerf, Andrea
Computer Science · Decision Sciences · Engineering · #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #Finance #I.2.8 #J.1 #Optimization and Mathematical Programming #Optimization and Variational Analysis #Risk and Portfolio Optimization #and Science (cs.CE) #cs.AI #cs.CE
paper · pdf · doi:10.48550/arxiv.cs/0104017
22 pages, 3 figures
arxiv created 2001/04/18 · openalex publication_date 2001/04/18 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.