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An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming

机译:基于数据包络分析,人工蜂群算法和遗传规划的综合投资组合优化程序

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摘要

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.
机译:投资组合优化是投资/财务决策领域的重要问题,受到了研究人员和从业人员的极大关注。但是,除了优化投资组合外,完整的投资程序还应包括选择有利可图的投资目标,并确定购买/出售投资目标的最佳时机。在这项研究中,提出了一种使用数据包络分析(DEA),人工蜂群(ABC)和遗传规划(GP)的集成程序来解决投资组合优化问题。通过对台湾股票市场半导体分部的股票投资进行了为期4年的案例研究,对提出的程序进行了评估。从2007年11月1日到2011年10月31日,平均潜在的6个月投资回报率为9.31%,这表明所建议的程序可以被视为制定出色的投资计划并因此在台湾股市上获利的可行且有效的工具。此外,这是一种可以帮助投资者即使在整个股市遭受亏损的情况下也能获利的策略。

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