Stock Price Pumping and Dumping Manipulation Detection Using Deep Learning Methods in Tehran Stock Exchange
Quarterly Journal of Securities Exchange

Quarterly Journal of Securities Exchange

Stock Price Pumping and Dumping Manipulation Detection Using Deep Learning Methods in Tehran Stock Exchange

Document Type : Original Article

Authors
meysam amiri 1
moslem peymany 2
Mohammad Ali Dehghan Dehnavi 3
Reza Jahanbin 4
1 Assistant Professor, Department of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
2 Associate Professor, Department of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran
3 Assistant Professor, Department of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran
4 Ph.D. Student, Department of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
10.22034/jse.2024.12351.2219
Abstract
One of the most important challenges in the stock market is the manipulation of securities. Manipulation of securities not only causes problems in the functionality of the capital market in the assets pricing, but also causes damages to the investors’ confidence. Therefore, the design and use of smart surveillance tools, especially tools based on artificial intelligence, to detect the market manipulation is very important. The purpose of this research is to provide a model based on deep learning methods to detect stock pump and dump manipulation. For this purpose, we generated labeled manipulation data using designing surveillance triggers at Securities and Exchange Organization and checking suspicious transactions. In the following, by using the adversarial generative network, misbalancing of data has been solved, and by using multi-layer perceptron neural network models, recurrent neural network, long-short-term memory neural network, and radial basis function network, price manipulation has been detected. The results show that long-term memory network and recurrent neural network had a good performance at price manipulation detection by more than 92% F2 score and have the ability to be implemented in regulatory processes and future research in other areas of stock manipulations.
Keywords
Market Manipulation
Pump and Dump
Deep Learning
Subjects
Aggarwal, R. K; & Wu, G. J. T. J. o. B. (2006). Stock market manipulations. The Journal of Business, 79(4), 1915-1953.
Broomhead, D. S; Lowe, D. J. R. s; & Malvern, r. e. (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks. Complex Systems, 25(3), 1-8.
Cao, Y; Li, Y; Coleman, S; Belatreche, A; McGinnity, T. M. J. I. t. o. n. n; & systems, l. (2014). Adaptive hidden Markov model with anomaly states for price manipulation detection. Transactions on Neural Networks and Learning Systems, 26(2), 318-330.
Charitou, C; Dragicevic, S; & Garcez, A. d. A. J. a. p. a. (2021). Synthetic data generation for fraud detection using gans. arXiv.org.
Craja, P; Kim, A; & Lessmann, S. J. D. S. S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421.
Fallah, M. & Kordlouyi, H. (2011). Test of logit models and artificial neural network to predict price manipulation in Tehran Stock Exchange. Financial Engineering and Portfolio Management, 2(7):37-72. (In Persian).
Fallah, M. & Mohammadi, M. (2011). A Model for Price Manipulation Prediction Case Study: Tehran Stock Exchange. Journal of The Economic Research, 11(2):30-41. (In Persian).
Fiore, U; De Santis, A; Perla, F; Zanetti, P; & Palmieri, F. J. I. S. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448-455.
Golmohammadi, K; & Zaiane, O. R. (2015). Time series contextual anomaly detection for detecting market manipulation in stock market. Paper presented at the 2015 IEEE international conference on data science and advanced analytics (DSAA).
Golmohammadi, K; Zaiane, O. R; & Díaz, D. (2014). Detecting stock market manipulation using supervised learning algorithms. Paper presented at the 2014 International Conference on Data Science and Advanced Analytics (DSAA).
Goodfellow, I; Pouget-Abadie, J; Mirza, M; Xu, B; Warde-Farley, D; Ozair, S; Bengio, Y. J. A. i. n. i. p. s. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
Hamedinia, H; Raei, R; Bajalan, S; & Rouhani, S. (2022). Analysis of Stock Market Manipulation using Generative Adversarial Nets and Denoising Auto-Encode Models. Advances in Mathematical Finance and Applications. 7(1), 133-151.
Hochreiter, S; & Schmidhuber, J. J. N. c. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Leangarun, T; Tangamchit, P; & Thajchayapong, S. (2018). Stock price manipulation detection using generative adversarial networks. Paper presented at the 2018 IEEE Symposium Series on Computational Intelligence (SSCI).
Leangarun, T; Tangamchit, P; Thajchayapong, S. J. I. j. o. t; economics, & finance. (2016). Stock price manipulation detection based on mathematical models. International journal of trade, economics and finance, 7(3), 81-88.
Leuz, C; Meyer, S; Muhn, M; Soltes, E; & Hackethal, A. (2017). Who falls prey to the wolf of Wall Street? Investor participation in market manipulation. Retrieved from National Bureau of Economic Research.
Li, A; Wu, J; & Liu, Z. J. P. C. S. (2017). Market manipulation detection based on classification methods. Procedia Computer Science, 122, 788-795.
MacIntosh, J. G. J. O. H. L. (1993). The Role of Institutional and Retail Investors in Canadian Capital Markets. Osgoode Hall Law Journal, 31, 371.
Markham, J. (2015). Law enforcement and the history of financial market manipulation: Routledge.
Nadiri, M, Alavi Nasab, M, Peymani, M. (2018). Investigating Some of Effective Factors on Spoofing Manipulation in Iranian Stock Market. Financial Research Journal, 20(3):327-342. (In Persian).
Öğüt, H; Doğanay, M. M; & Aktaş, R. J. E. S. w. A. (2009). Detecting stock-price manipulation in an emerging market: The case of Turkey. Expert Systems with Applications, 36(9), 11944-11949.
Rabiee, R, Nadri, M, Peymani, M, Jaberzadh, A. (2018). Determining manipulation effect on market efficiency in Tehran Stock Exchange. Journal of Securities and Exchange, 11(43): 113-131. (In Persian).
Rosenblatt, F. J. P. r. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
Rumelhart, D. E; Hinton, G. E; & Williams, R. J. J. n. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
Sridhar, S; Mootha, S. J. I. J. o. N; & Organisations, V. (2022). Stock market manipulation detection using feature modelling with hybrid recurrent neural networks. International Journal of Networking and Virtual Organisations, 26(1-2), 47-79.
Sasaki, Y. J. T. t. m. (2007). The truth of the F-measure. Teach tutor mater, 1(5), 1-5.
Shams, Sh. & Ataei, B. (2016). The detection of the stock price manipulation by hybrid genetic algorithm: Artificial Neural Network model (ANN-GA) and SQDF model. Journal of Financial Management Strategy, 4(3):149-171. (In Persian).
Uslu, N. C; & Akal, F. J. C. E. (2022). A machine learning approach to detection of trade-based manipulations in Borsa Istanbul. Computational Economics, 60(1), 25-45.
Tohidi, M. & Mosavian. A. (2018). Study and analysis of speculation based on manipulation in view point of shariah principles. Journal of Securities and Exchange, 11(43): 79-113. (In Persian).
Wang, Q; Xu, W; Huang, X; & Yang, K. J. N. (2019). Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning. Neurocomputing, 347, 46-58.
Yekehzare, M. (2019). Market Manipulation During Rights Offering: Evidence from the Tehran Stock Exchange (Master's thesis), under the guidance of Ebrahim Nezhad Ali, Sharif University of technology. (In Persian).
Quarterly Journal of Securities Exchange
Volume 18, Issue 69
Spring 2025
Pages 25-52