International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

Speculative Bubble Detection and Prediction in the Tehran Stock Exchange

Articles in Press

Document Type : Original Article

Authors
Ahmad Moradi 1
Hamid Reza Kordloui 2
Mohamad Reza Pourfakharan 3
Mohammad Hassan Maleki 4
1 Ph.D. Student, Department of Financial Management, Qo.C., Islamic Azad University, Qom, Iran
2 Associate Professor, Department of Business Administration, Isl.C., Islamic Azad University, Islamshahr, Tehran, Iran
3 Assistant Professor, Department of Financial Management, Qo.C., Islamic Azad University, Qom, Iran
4 Professor, Faculty of Economic and Administrative Sciences, University of Qom, Qom, Iran
10.22034/ijfma.2026.79323.2382
Abstract
Financial bubbles often arise following genuine advancements in the productivity and initial profitability of a firm or industry; however, historical evidence shows that in such situations investors tend to exaggerate the fundamental capabilities of these firms. The existence of bubbles diminishes public trust in the soundness of markets, their liquidity strength, and overall market efficiency. This paper examines the usefulness of classification methods for detecting and predicting deliberate bubbles in the Over-The-Counter market of the Tehran Stock Exchange. Methods based on the Right-Tailed Augmented Dickey-Fuller (RTADF) test were used to label stocks exhibiting price bubbles, and machine learning techniques together with a deep learning approach (the Convolutional Neural Network algorithm) were employed for classification and for determining the accuracy of stock price bubble detection. The aforementioned methods were applied to the closing prices of 42 firms listed on the OTC market of the Tehran Stock Exchange, selected using a screening method for the period from December 2019 to December 2020. The results indicate that, across all evaluation metrics (precision, recall, F1 harmonic mean, and accuracy), the best average performance was achieved by the Convolutional Neural Network (CNN), followed by the Support Vector Machine (SVM).
Keywords
Intentional Speculative Bubble
Classification Methods
Anomaly Detection
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International Journal of Finance & Managerial Accounting

Articles in Press, Accepted Manuscript
Available Online from 14 October 2026