بهینه سازی سبد سرمایه‌گذاری در بورس اوراق بهادار تهران با استفاده از الگوریتم چرخه‌ آب (WCA)

نوع مقاله : علمی - پژوهشی

نویسنده

دانشکده مدیریت دانشگاه تهران

چکیده

انتخاب سبد سرمایه گذاری بهینه یکی از مهم­ترین چالش های علوم مالی است. هدف این مطالعه بکارگیری الگوریتم چرخه­ آب چند هدفه (MOWCA) برای یافتن ترکیبی کارآمد از سبد سرمایه­گذاری است. مسئله­ مورد مطالعه یک مسئله­ چند هدفه غیر خطی است که توابع هدف آن شامل حداکثر سازی بازده و حداقل سازی ریسک است. الگوریتم چرخه آب از فرآیند چرخه­ آب در طبیعت شبیه­سازی شده است و نخستین بار توسط مرادی و همکاران (2017) از این الگوریتم برای بهینه سازی سبد سهام در چهار بورس بزرگ دنیا بهره گرفته شده است. در این تحقیق از اطلاعات روزانه طی سال های1392 تا 1394، 30 شرکت بزرگ بورس تهران استفاده شده است. به علاوه عملکرد MOWCA برای حل مسائل بهینه­سازی چندهدفه با سایر بهینه­سازهای چندهدفه از قبیل الگوریتم ژنتیک چندهدفه (MOGA) و الگوریتم پرندگان چندهدفه (MOPSO) مقایسه شده­است. به منظور مقایسه از چهار معیار عملکرد برای مقایسه­ نتایج الگوریتم ها از چهار معیار مرسوم استفاده شده است: فاصله، یکنواختی، تنوع و پوشش. یافته ها حاکی از آن است که بر اساس اغلب معیارهای ارزیابی عملکرد مورد استفاده در این تحقیق، MOWCA درمقایسه با سایر الگوریتم های فرا ابتکاری برای مسائل بهینه­سازی سبد سرمایه­گذاری کارآمدی بیشتری دارد.

کلیدواژه‌ها

عنوان مقاله [English]

Portfolio Optimization in Tehran Stock Exchange by Water Cycle Algorithm

نویسنده [English]

  • Mohammad Moradi
Management Faculty, University of Tehran
چکیده [English]

Portfolio selection is one of the vital financial challenges. This study seeks to apply the multi-objective water cycle algorithm (MOWCA) to find efficient frontiers associated with portfolio. This problem is non-linear multi-objective problem including maximiaing return and minimizing risk of portfolio. The inspired concept of WCA is based on the simulation of water cycle process in the nature. At the first time, it was applied by Moradi et al. (2017) for optimizating portfolio. Computational results are obtained for analyses of daily data for the period 2013 to 2015 including TSE 30. The performance of the MOWCA for solving portfolio optimization problems has been evaluated in comparison with other multi-objective optimizers including the MOGA and MOPSO. Four well-known performance metrics are used to compare the reported optimizers: GD, MS, S and ∆. Statistical optimization results indicate that the applied MOWCA is an efficient and practical optimizer compared with the other methods for handling portfolio optimization problems.

کلیدواژه‌ها [English]

  • Portfolio optimization
  • Multi-Objective Optimization
  • Water Cycle Algorithm
  • Genetic algorithm
  • Particle Swarm Algorithm

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