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Heydarpour, Mehrdad, Ghanbari, Hossein, Mohammadi, Emran, Shavvalpour, Saeed. (1404). Robust Portfolio Optimization using LSTM-based Stock and Cryptocurrency Price Prediction: An Application of Algorithmic Trading Strategies. سامانه مدیریت نشریات علمی, 9(3), 151-169. doi: 10.22067/ijaaf.2025.46039.1466
Mehrdad Heydarpour; Hossein Ghanbari; Emran Mohammadi; Saeed Shavvalpour. "Robust Portfolio Optimization using LSTM-based Stock and Cryptocurrency Price Prediction: An Application of Algorithmic Trading Strategies". سامانه مدیریت نشریات علمی, 9, 3, 1404, 151-169. doi: 10.22067/ijaaf.2025.46039.1466
Heydarpour, Mehrdad, Ghanbari, Hossein, Mohammadi, Emran, Shavvalpour, Saeed. (1404). 'Robust Portfolio Optimization using LSTM-based Stock and Cryptocurrency Price Prediction: An Application of Algorithmic Trading Strategies', سامانه مدیریت نشریات علمی, 9(3), pp. 151-169. doi: 10.22067/ijaaf.2025.46039.1466
Heydarpour, Mehrdad, Ghanbari, Hossein, Mohammadi, Emran, Shavvalpour, Saeed. Robust Portfolio Optimization using LSTM-based Stock and Cryptocurrency Price Prediction: An Application of Algorithmic Trading Strategies. سامانه مدیریت نشریات علمی, 1404; 9(3): 151-169. doi: 10.22067/ijaaf.2025.46039.1466

Robust Portfolio Optimization using LSTM-based Stock and Cryptocurrency Price Prediction: An Application of Algorithmic Trading Strategies

Iranian Journal of Accounting, Auditing and Finance
مقاله 9، دوره 9، شماره 3 - شماره پیاپی 32، آبان 2025، صفحه 151-169    اصل مقاله (626.9 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22067/ijaaf.2025.46039.1466
نویسندگان
Mehrdad Heydarpour1؛ Hossein Ghanbari2؛ Emran Mohammadi* 3؛ Saeed Shavvalpour3
1MSc Student in Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
2School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
3IUST
چکیده
Algorithmic trading (AT) has become widely used recently because of its high speed and accuracy in implementing diverse and complex strategies. Using algorithms also allows traders to execute their trading strategies in a high volume and numerous transactions without involving human emotions. While AT has many advantages, it also carries some risks due to the uncertain stock market conditions and the impact of news and political, social, and other events. Therefore, forming a stock portfolio and stabilizing against uncertainties, in conjunction with accurate market predictions, can significantly reduce risk.in this paper, For the first time, we developed a robust portfolio optimization model based on LSTM prediction using the AT strategies based on short-term moving average techniques. First, we implement the strategies derived from the VLMA, FLMA, EMA, and SMA algorithms based on the LSTM's predicted price. Secondly, we develop a robust portfolio optimization model using the abovementioned algorithms. The results show that in both stock and crypto portfolios, moving average strategies will perform better than the benchmark strategy (Buy-and-hold). Also, when the model parameters are deterministic, the robust portfolio constructed stocks and crypto will perform better than Buy-and-hold for all algorithms. However, when the variance from certain models increases, VLMA and FLMA (15-day holding) for stocks and FLMA (30-day holding) for the crypto will not be a suitable investment option. Additionally, portfolios constructed using all AT strategies and all assets outperform the benchmark portfolio in certain and non-certain markets.
کلیدواژه‌ها
Robust portfolio optimization؛ Algorithmic trading؛ Price prediction؛ LSTM
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