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考虑保守卖空与财务困境的背景风险投资组合模型 被引量:2

Background RiskPortfolio SelectionModel with Conservative Short-selling and Financial Distress
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摘要 如何合理地考虑投资者所面临的背景风险及现实市场限制来进行有效地投资决策是人们所广泛关注的重要实际管理决策问题。本文研究投资者同时面临加性和乘性两类背景风险的前提下具有保守卖空与财务困境的投资组合选择问题。假定投资者寻求使得投资收益最大、投资风险最小及证券主体财务困境最小的最优投资组合策略,进而提出考虑保守卖空与财务困境的背景风险投资组合模型。然后,利用具有精英策略的非支配排序遗传算法对模型进行求解。最后,通过实例来阐述模型的实用性。研究结果表明:考虑保守卖空能为投资者提供更大的收益;两类背景风险的变化均导致有效前沿面的变化。 How to reasonably consider both the background risks faced by investors and the real market constraints to make effective investment decisions is an important practical management decision problem that has aroused wide concern. This paper studies a portfolio selection problem with conservative short selling and financial distress, where investors face both additive and multiplicative background risks. We assume that investors seek the optimal portfolio strategies with the objectives of maximizing the investment return, minimizing the investment risk and minimizing the financial distress about the issuers of the securities. We propose a background risk portfolio model with conservative short selling and financial distress. Then, we apply the nondominant sorting genetic algorithm with elite strategy to solve the proposed model. After that, we provide an empirical analysis to demonstrate the application of our model. The computational results show that incorporating conservative short selling into portfolio selection can provide investors with a higher investment return, and the change of either of the aforementioned two types of background risk will lead to the change of efficient frontier.
作者 刘勇军 李莹莹 张卫国 LIU Yong-jun;LI Ying-ying;ZHANG Wei-guo(South China University of Technology,School of Business Administration,Guangzhou 510640,China)
出处 《运筹与管理》 CSSCI CSCD 北大核心 2022年第12期128-135,共8页 Operations Research and Management Science
基金 国家自然科学基金资助项目(71971086) 广东省自然科学基金杰出青年基金项目(2019B151502037) 广东省青年珠江学者(粤教师函[2019]25号) 中央高校基本科研业务费(ZDPY202203)。
关键词 投资组合 背景风险 财务困境 保守卖空 NSGA-Ⅱ算法 portfolio model background risk financial distress conservative short-selling NSGA-Ⅱalgorithm
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