توسعه مدل برنامه‌ریزی تصادفی برای مسأله انتخاب سبد دارایی چنددوره‌ای

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار دانشگاه خوارزمی

2 عضو هیئت علمی گروه مهندسی صنایع دانشگاه جامع امام حسین

10.22054/jims.2017.22059.1769

چکیده

در این مقاله، به توسعه یک مدل برنامه‌ریزی تصادفی برای مسأله انتخاب سبد دارایی چنددوره‌ای با درنظرگرفتن هزینه‌های معامله و محدودیت تعداد دارایی پرداخته می‌شود. مدل ارائه‌شده، ضمن تضمین دستیابی به حداقلی از بازده، ریسک را کمینه می‌کند. به منظور تولید درخت سناریوی پارامترهای تصادفی، از تبدیل جانسون و فرآیند نمونه‌گیری در چارچوب یک مدل گام تصادفی استفاده می‌شود. سپس، داده‌های تاریخی 28 شاخص صنعت داخلی به منظور پیاده‌سازی روش تولید درخت سناریوی پارامترهای تصادفی مورد استفاده قرار می‌گیرند. نهایتاً مدل برنامه‌ریزی تصادفی، با استفاده از مجموعه سناریوهای تولیدشده حل می‌شود. نتایج حل مدل ارائه‌شده نشان می‌دهند که افزایش هزینه‌ معامله و ثروت هدف، ریسک سرمایه‌گذاری را افزایش می‌دهند. همچنین، نتایج حل مدل با مجموعه سناریوهای متفاوت، پایایی درون‌نمونه‌ای مناسبی را از منظر ریسک و بازده نشان می‌دهد. به علاوه، شبیه‌سازی پویای ارزش تجمعی دارایی‌های سرمایه‌گذار نشان می‌دهد که با افزایش حداقل بازده مورد انتظار، نوسان‌پذیری ثروت سرمایه‌گذار افزایش خواهد یافت.

کلیدواژه‌ها


عنوان مقاله [English]

A Stochastic Programming Framework for Multi-period Portfolio Optimization

نویسنده [English]

  • Ardeshir Ahmadi 2
2 Department of systems Engineering, IHU university, Tehran, Iran
چکیده [English]

This paper presents a scenario-based multistage stochastic programming model to deal with multi-period portfolio optimization problem with cardinality constraints and proportional transaction costs. The presented model aims to minimize investor's expected regret, while setting a minimum level of expected return. To generate the scenario tree of stochastic parameters, a random walk model based on Johnson transformation and a sampling procedure is used. To implement the scenario tree generation method, historical returns of 28 domestic indices are used. Then, the scenario tree of stochastic parameters are used to solve the proposed multistage stochastic programming model. In addition, the impact of transaction costs, minimum expected returns and predetermined target wealth are investigated. Numerical results show that transaction costs, minimum expected returns and target wealth have a direct impact on expected regret. Finally, back testing simulation is used to assess and analyze the impact of the proposed approach in a dynamic, multi-period setting.

کلیدواژه‌ها [English]

  • Scenario-based multistage stochastic programming
  • Multi-period portfolio optimization
  • Scenario Tree
  • Random walk model
  • Johnson transformation
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