ارائه مدل ترکیبی بهینه‌سازی سبد سهام براساس پیش‌بینی قیمت با شبکه عصبی بازگشتی LSTM به کمک محدودیت‌های کاردینالیتی و روش‌های تصمیم‌گیری چندمعیاره (مطالعه موردی بورس اوراق بهادار تهران)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی مالی، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران.

2 دانشیار، گروه حسابداری، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران.

3 استادیار، گروه حسابداری، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران.

4 استادیار، گروه مدیریت، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران.

چکیده

پیش‌بینی قیمت سهام به دلیل ماهیت نوسانی بازار و پویایی روند حرکت قیمت، نقش مهمی در ایجاد یک استراتژی کارآمد با بازدهی بالا دارد، همچنین نتایج حاصل از پیش‌بینی، پیش‌نیاز ایجاد سبد سهام با ساختاری بهینه است. لذا هدف از انجام این پژوهش ارائه مدلی ترکیبی است تا به سرمایه‌گذاران در انتخاب سبد سهام بهینه کمک نماید. بنابراین، با استفاده از فرآیند تحلیل سلسله مراتبی بهبود‌یافته از میان صنایع فعال در بورس اوراق بهادار تهران، ده صنعت برتر براساس معیارهای مؤثر بر ارزش صنایع انتخاب می‌شوند. سپس با کمک شبکه عصبی بازگشتی حافظه کوتاه مدت ماندگار قیمت سهام شرکت‌های فعال طی بازه زمانی ابتدای خرداد 1395 تا ابتدای خرداد 1400، در افق‌های زمانی مورد نظر، پیش‌بینی می‌گردد. در گام بعد با روش راه‌حل سازشی ترکیبی، سه سبد سهام با افق زمانی کوتاه‌مدت، میان‌مدت و بلند‌مدت انتخاب می‌شود و در نهایت براساس مدل دارایی محدود مارکویتز با استفاده از روش برنامه‌ریزی غیرخطی عدد صحیح مختلط با الگوریتم شاخه و برش، اوزان بهینه مشخص و مرز کارا رسم می‌گردد. نتایج پژوهش نشان می‌دهد، مدل ارائه شده، بازدهی بیشتری را باتوجه به ریسک در تشکیل سبدهای سهام با افق‌های زمانی مشخص نسبت به روش‌های سنتی نصیب سرمایه‌گذاران می‌نماید.

کلیدواژه‌ها

عنوان مقاله [English]

A Hybrid Model for Portfolio Optimization Based on Stock Price Forecasting with LSTM Recurrent Neural Network using Cardinality Constraints and Multi-Criteria Decision Making Methods (Case study of Tehran Stock Exchange)

نویسندگان [English]

  • Nasimeh Abdi 1
  • mehdi Moradzadeh Fard 2
  • Hamid Ahmadzadeh 3
  • Mahmoud Khoddam 4
1 Ph.D. Candidate in Financial Engineering, Department of Management, Karaj Branch, Islamic Azad University, Karaj, Iran.
2 Associate Prof, Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.
3 *** Assistant prof, Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.
4 Assistant prof, Department of Management, Karaj Branch, Islamic Azad University, Karaj, Iran.
چکیده [English]

Due to the dynamic trend of stock prices and the volatile nature of the market, asset price forecasting plays a key role in creating an efficient strategy, and the results of price forecasting are a prerequisite for creating an optimal stock portfolio. The purpose of this study is to provide a hybrid model to help investors in selecting the optimal portfolio. Therefore, ten top industries have been selected among the active industries of the Tehran Stock Exchange using IAHP method, Then, the stock price of companies listed on the Tehran Stock Exchange from 2016 to 2021 are forecast at the considered time horizons using LSTM. In the next step, three portfolios with different time horizons are selected by using the CoCoSo method, and finally, optimal weights have been determined and an efficient frontier has been drawn using Mixed-Integer Quadratic Program and Branch and Cut Algorithm based on LAM Model. According to the results of this research, the proposed model gives higher returns to investors due to the risk of constituting portfolios with specified time horizons in contrast to traditional approaches

کلیدواژه‌ها [English]

  • Price Forecasting
  • LSTM
  • IAHP
  • CoCoSo
  • Cardinality Constraint

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چشم انداز مدیریت مالی
دوره 11، شماره 36
دی 1400
صفحه 119-143

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