نوع مقاله : مقاله پژوهشی
نویسنده
گروه مهندسی صنایع، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.
کلیدواژهها
موضوعات
عنوان مقاله English
نویسنده English
Purpose: Optimization problems are one of the most interesting, important, and popular fields of financial mathematics. A better portfolio optimization model can help investors earn more sustainable profits. The existing literature shows that traditional mean-variance portfolio strategies are not suitable. To address this issue, this study uses a multilayer perceptron neural network and a convolutional neural network to predict future stock price movements.
Methodology: We compare the prediction accuracy of these two methods and use the outputs from the higher-accuracy method in the proposed model. Then, given the future direction of stock prices, we propose an efficient stock selection scheme for investors. We also test the proposed stock selection scheme and investment strategies using the components of the Tehran Stock Exchange index as test cases.
Findings: The experimental results show that the proposed stock selection scheme can effectively improve the performance of all investment strategies. In addition, the proposed investment strategy outperforms the traditional minimum global variance investment strategy.
Originality/Value: This research provides an innovative framework for portfolio selection based on deep learning networks. These networks are key to improving investment efficiency, risk management, and decision-making in the Iranian capital market and serve as an advanced model for similar markets.
کلیدواژهها English
