بهینه‌یابی پرتفوی مبتنی بر پیش‌بینی در بورس اوراق بهادار تهران؛ رویکرد شبکه های عصبی NARX و LSTM
فصلنامه بورس اوراق بهادار

فصلنامه بورس اوراق بهادار

بهینه‌یابی پرتفوی مبتنی بر پیش‌بینی در بورس اوراق بهادار تهران؛ رویکرد شبکه های عصبی NARX و LSTM

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه اقتصاد، دانشکده اقتصاد و علوم اجتماعی، دانشگاه بوعلی سینا، همدان، ایران
2 دانشیار، گروه اقتصاد، دانشکده علوم اقتصادی و اجتماعی، دانشگاه بوعلی سینا، همدان، ایران،
3 استاد، گروه اقتصاد، دانشکده علوم اقتصادی و اجتماعی، دانشگاه بوعلی سینا، همدان، ایران،
4 استادیار گروه اقتصاد، دانشکده علوم اقتصادی و اجتماعی، دانشگاه بوعلی سینا،همدان، ایران
چکیده
هدف از مطالعه حاضر پیاده‌سازی بهینه‌یابی پرتفوی مبتنی‌بر پیش‌بینی در بورس اوراق بهادار تهران با استفاده از شبکه‌ عصبی خودبازگشتی غیرخطی با متغیرهای برون‌زا (NARX) به‌عنوان رویکرد اصلی و شبکه عصبی حافظه کوتاه مدت طولانی (LSTM) به‌عنوان رویکرد رقیب و نیز مقایسه عملکرد آن با روش بهینه‌یابی پرتفوی میانگین-واریانس مارکوویتز است. بدین منظور ابتدا رتبه‌بندی و تشخیص سهام شرکت‌هایی که سرمایه‌گذاری روی آن‌ها به‌صرفه است با بهره‌گیری از مدل تک عامله شارپ صورت گرفته، سپس در ‌راستای برآورد شاخص بازده مورد انتظار و ریسک پرتفوی از پیش‌بینی بازدهی سهام شرکت‌ها و واریانس خطاهای پیش‌بینی بر‌پایه رویکردهای مذکور استفاده شده و پس از آن بهینه‌یابی پرتفوی بر‌اساس روش کلی میانگین-واریانس مارکوویتز انجام‌ شده‌است. شرکت‌های موجود در شاخص 50 شرکت فعال‌تر بورسی مربوط به پایان سال 1400 به عنوان یک نمونه از جامعه آماری در نظر گرفته شدند و برآوردها با استفاده از داده‌های روزانه مربوط‌ به دوره زمانی 1/2/1399 تا 29/12/1400 انجام شد. نتایج نشان می‌دهد رویکرد شبکه عصبی NARX نسبت به رویکرد شبکه عصبی LSTM دقت بالاتری در پیش‌بینی بازدهی سهام شرکت‌ها دارد و در مرحله بهینه‌یابی نیز آن را مغلوب می‌کند. بهینه‌یابی پرتفوی مبتنی‌بر پیش‌بینی با استفاده از شبکه‌های عصبی NARX و LSTM در مقایسه‌ با بهینه‌یابی پرتفوی میانگین-واریانس مارکوویتز ریسک کمتر و حداکثر نرخ شارپ بالاتری را برای سرمایه‌گذاران فراهم‌‌می‌آورد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Prediction-based portfolio optimization in Tehran Stock Exchange; NARX and LSTM neural networks approach

نویسندگان English

Razieh Sahrai 1
Ali Akbar Gholizadeh 2
Hamid Sepehrdoust 3
Seyed Ehsan Hosseinidoust 4
1 Department of Economics, Faculty of Economic and Social Sciences, Bu-Ali Sina University, Hamedan, Iran
2 Associate Professor, Department of Economics, Faculty of Economic and Social Sciences, Bu-Ali Sina University, Hamedan, Iran.
3 Professor, Department of Economics, Faculty of Economic and Social Sciences, Bu-Ali Sina University, Hamedan, Iran
4 Assistant Prof. of Economics, Department of Economics, Faculty of Economics and Social Science, Bu-Ali Sina University, Hamedan, Iran
چکیده English

The purpose of present study is to implement prediction-based portfolio optimization in Tehran Stock Exchange using nonlinear autoregressive with exogenous variables (NARX) neural network as the main approach and long short term memory (LSTM) neural network as the competing approach and comparing its performance with Markowitz mean-variance portfolio optimization method. For this purpose, firstly, the ranking and identifying the stocks of the companies that are profitable to invest in was done by using Sharpe's single-index model, then in order to estimate the expected return index and portfolio risk, the prediction of the return of the companies' stocks and the variance of the prediction errors were used based on the mentioned approaches, and then portfolio optimization is done based on Markowitz's mean-variance general method. The companies included in the index of 50 most active stock companies related to the end of 1400 are considered as a sample of the statistical population, and the estimates were made using daily data related to the time period from 1/2/1399 to 29/12/1400. The results show that the NARX neural network approach is more accurate than the LSTM neural network approach in predicting the returns of the companies' stocks and defeats it in the optimization stage. Prediction-based portfolio optimization using NARX and LSTM neural networks provides investors with lower risk and a higher maximum Sharpe ratio compared to mean-variance Markowitz portfolio optimization.

کلیدواژه‌ها English

portfolio optimization
prediction
NARX neural network
LSTM neural network
Markowitz
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