Forecasting Tehran Stock Market Return Using Quantum Artificial Intelligence Algorithms
Quarterly Journal of Securities Exchange

Quarterly Journal of Securities Exchange

Forecasting Tehran Stock Market Return Using Quantum Artificial Intelligence Algorithms

Document Type : Original Article

Authors
Reza Maaboudi 1
Younes Nademi 2
Soheila Aghaee 3
1 Associate Professor, Faculty Member of the Department of Economics, Faculty of Humanities, Ayatollah Ozma Boroujerdi University, Boroujerd, Iran.
2 Associate Professor, Department of Economics, Faculty of Humanities, Ayatollah Boroujerdi University, Boroujerd, Iran.
3 M.A. Student in Economics, Department of Economics, Faculty of Humanities, Ayatollah Boroujerdi University, Boroujerd, Iran.
10.22034/jse.2026.12631.2400
Abstract
This study aims to forecast the returns of the Tehran Stock Market by employing the novel QDL approach. To this end, daily data of the Tehran Stock Exchange Value-Weighted Index over the period from February 1, 2016, to October 6, 2025, are used to compute stock market returns. Subsequently, stock market returns are modeled and predicted using a QDL framework, which integrates the capabilities of deep learning and quantum computing. The predictive performance of the proposed model is benchmarked against six competing models, including the ARIMA model, machine learning algorithms such as Random Forest and Support Vector Regression, as well as deep learning models, including LSTM, GRU, and MLP. Results, based on Mean Squared Error, Root Mean Squared Error, and Theil’s index, indicate that the QDL model outperforms all competing models in forecasting stock market returns. Moreover, the Diebold–Mariano and Wilcoxon tests confirm the statistical significance of the superior predictive accuracy of the proposed model, thereby reinforcing the robustness of the error-based evaluation results. The distinctive characteristics of quantum computing, such as superposition and entanglement, enable the QDL model to more effectively capture hidden and nonlinear patterns in stock market dynamics, leading to improved performance under conditions of heightened volatility and unpredictable market behavior. Based on the empirical findings, it is recommended that the Securities and Exchange Organization facilitate the development and adoption of advanced technologies such as quantum computing by establishing the necessary infrastructure, thereby enabling smarter market surveillance and the early detection of potential bubbles and systemic risks.
Keywords
Stock Return Forecasting
Tehran Stock Market
Quantum Deep Learning
Subjects
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Quarterly Journal of Securities Exchange
Volume 19, Issue 75
Summer 2026
Pages 135-170