Analysis of Stock Market Manipulation using Generative Adversarial Nets and Denoising Auto-Encode Models

محورهای موضوعی : Financial Mathematics

Hamed Hamedinia ¹ , Reza Raei ² , Saeed Bajalan ³ , Saeed Rouhani ⁴

1 - Ph.D. student in university of Tehran Chief investment Officer in Maskan Investment Bank
2 - Prof. finance University of Tehran
3 - Finance university of Tehran
4 - IT management University of Tehran

تاریخ دریافت : 1400/02/22 تاریخ پذیرش : 1400/05/13 تاریخ انتشار : 1400/10/11

کلید واژه: Generative Adversarial Net (GAN), Stock Manipulation Detection, Anomaly detection, deep learning,

چکیده مقاله :

Market manipulation remains the biggest concern of investors in today’s securities market. The development of technologies and complex trading algorithms seems to facilitate stock market manipulation and make it inevitable for regulators to use Deep Learning models to prevent manipulation. In this research, a Denoising GAN-based model has been designed. The proposed model (GAN-DAE4) consists of a three-layer encoder along with a 2-dimension encoder as the discriminator and a three-layer decoder as the generator. First, using statistical methods such as sequence, skewness, and kurtosis tests and some unsupervised learning methods such as Contextual Anomaly Detection (CAD) and some visual and graphical methods, the manipulated stocks have been detected in the Tehran Stock Exchange from 2015 to 2020; then GAN-DAE4 and some supervised deep learning models have been applied to the prepared data set. The results show that GAN-DAE4 outperformed other deep learning models (with F2-measure 73.71%) such as Decision Tree (C4.5), Random Forest, Neural Network, and Logistic Regression.

منابع و مأخذ:

• Al-Thani, H.A., Detecting market manipulation in stock market data, Detecting market manipulation in stock market data, 2017. Doi: https://www.proquest.com/openview/8d73380cd0dd690f46a37b2db715def0/1?pq-origsite =gscholarandcbl=2026366


• Allen, F.,Gale, D., Stock-price manipulation. The Review of Financial Studies, 1992, 5(3), P. 503-529.

 Doi: 10.1093/rfs/5.3.503

• Amini, M., Isaei Tafreshi, M., Shahbazinia, M., Securities Market Manipulation: Concept, Principles, and Methods (A Comparative Study of Iranian and American Law), Comparative Law Research, 2014, 19, P. 1-26. (in persian)


• Banimahd, B., The Ranking of Fraudulent Financial Reporting By Using Data Envelopment Analysis: Case of Pharmaceutical Listed Companies, Advances in Mathematical Finance and Applications, 2020, 5(1), P.69-80. Doi: 10.22034/amfa.2019.1863571.1193


• Ben‐David, I., Do hedge funds manipulate stock prices?, The Journal of Finance, 2013, 68(6), P. 2383-2434. Doi: 10.1111/jofi.12062


• Carhart, M. M., Kaniel, R., Musto, D. K., Reed, A. V., Leaning for the tape: Evidence of gaming behavior in equity mutual funds, The Journal of Finance, 2002, 57(2), P. 661-693. Doi: 1111/1540-6261.00438


• Cherian, J.,Kuriyan, V., Informationless manipulation in a market maker-type economy, 1995, Available at SSRN 6501. Doi: https://ssrn.com/abstract=6501


• Cherian, J.A.,Jarrow, R.A., Market manipulation, Handbooks in Operations Research and Management Science, 1995, 9, P. 611-630. Doi: 10.1016/S0927-0507(05)80064-7


• Comerton-Forde, C., Putniņš, T.J., Measuring closing price manipulation, 2011, Journal of Financial Intermediation, 20(2), P. 135-158. Doi: 1016/j.jfi.2010.03.003


• Comerton-Forde, C., Putniņš, T.J., Stock price manipulation: Prevalence and determinants, Review of Finance, 2014, 18(1), P.23-66. Doi: 1093/rof/rfs040


• Diaz, D., Theodoulidis, B., Sampaio, P., Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices, Expert Systems with Applications, 2011, 38(10), P.12757-12771. Doi: 1016/j.eswa.2011.04.066


• Ergün, H. O., Yalaman, A., Manahov, V., and Zhang, H., Stock market manipulation in an emerging market of Turkey: how do market participants select stocks for manipulation? 2020, Applied Economics Letters, P. 1-5. Doi: 1080/13504851.2020.1753874


• Falahzadeh, H., Detection of stock manipulation using data mining techniques, University of Science and Art affiliated with University Jahad. (in persian)


• Fallah Shams Layalestani, M., Badpa, A., Badpa, I., Designing a model to identify the possibility of final price manipulation in the Tehran Stock Exchange, Financial Engineering and Securities Management, 2015, 6, P. 135-152. (in persian)


• Fallah Shams, M.,Teymouri Shandi, A., Designing a price manipulation forecasting model in Tehran Stock Exchange, Research Quarterly of Imam Sadegh University, 2005, 27. (in persian)


• Fallah Shams, M., Kordloui, H.R., Test of logit models and artificial neural network to predict price manipulation in Tehran Stock Exchange, Financial Engineering and Securities Management, 2011, 2(7), P. 37-69. (in persian)


• Fallah Shams, M., Kordloui, H.R., Rashno, M., Investigating price manipulation in Tehran Stock Exchange using the support vector machine model, Financial Research, 2012, 14(1), P. 69-84. (in persian)


• Farshadfar, Z., Prokopczuk, M., Improving Stock Return Forecasting by Deep Learning Algorithm, Advances in Mathematical Finance and Applications, 2019, 4(3), P.1-13. Doi: 10.22034/amfa.2019.584494.1173


• Felixson, K., Pelli, A., Day end returns—stock price manipulation, Journal of Multinational Financial Management, 1995, 9(2), P. 95-127. Doi: 1016/s1042-444x(98)00052-8


• Ferdousi, Z., Maeda, A., Anomaly Detection Using Unsupervised Profiling Method in Time Series Data, in ADBIS Research Communications, Doi: 10.1.1.89.4348andrep=rep1andtype=pdf


• Fischel, D.R., Ross, D.J., Should the Law Prohibit" Manipulation" in Financial Markets? Harvard Law Review, 1991, 105(2), P.503-553. Doi: 2307/1341697


• Ghorbani, M., Bagheri, A., Securities market manipulation, Public Law Research, 2008, 12, P. 301-326. (in persian)


• Golmohammadi, K., Zaiane, O.R., Díaz, D., Detecting stock market manipulation using supervised learning algorithms, in 2014 International Conference on Data Science and Advanced Analytics (DSAA), 2014, IEEE. Doi:1109/DSAA.2014.7058109


• Golmohammadi, K., Zaiane, O.R., Time series contextual anomaly detection for detecting market manipulation in stock market, in 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015, IEEE. Doi: 1109/DSAA.2015.7344856


• Goodfellow, I.J., Generative adversarial networks. arXiv preprint arXiv:1406.2661, 2014. https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf


• Griffin, J.M., Shams, A., Manipulation in the VIX? The Review of Financial Studies, 2018, 31(4), P. 1377-1417. Doi: 2139/ssrn.2972979


• Hart, O.D., On the profitability of speculation. The Quarterly Journal of Economics,1977, P. 579-597.

 Doi: 10.2307/1885883

• Hillion, P., Suominen, M., The manipulation of closing prices, Journal of Financial Markets, 2004, 7(4),

375. 351-375. Doi: 10.1016/j.finmar.2004.04.002

• Jarrow, R.A., Market manipulation, bubbles, corners, and short squeezes, Journal of financial and Quantitative Analysis, 1992, P.311-336. Doi: 2307/2331322


• Kazemi Tabar, S.J., Shahbazzadeh, M., Detection of fraud in the stock market using the Chebyshev inequity, Quarterly Journal of Signal and Data Processing, 2019, 17, P. 3-14. (in persian)


• Leangarun, T., Tangamchit, P., Thajchayapong, S., Stock Price Manipulation Detection using Generative Adversarial Networks, in 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018, IEEE. Doi: Doi:10.1109/SSCI.2018.8628777


• Lee, E.J., Eom, K.S., Park, K.S., Microstructure-based manipulation: Strategic behavior and performance of spoofing traders, Journal of Financial Markets, 2013, 16(2), P. 227-252. Doi: 1016/j.finmar.2012.05.004


• MehdiYekeZare, M., manipulation in rights offering trading on the Tehran Exchange Securities, Sharif University of Technology, 2019. (in persian)


• Nadiri, M., Alavi Nasab, S., Peymani, M., Rabiee, R., Investigating the effect of some factors affecting price manipulation in the Iranian stock market, Financial Research, 2018, 20(3), P. 327-342. (in persian)


• Öğüt, H., Doğanay, M.M., Aktaş, R., Detecting stock-price manipulation in an emerging market: The case of Turkey. Expert Systems with Applications, 2009, 36(9), P. 11944-11949. Doi: 1016/j.eswa.2009.03.065


• Phua, C., Lee, V., Smith, K., Gayler, R., A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv, 2010. Doi: 10.1016/j.chb.2012.01.002


• Poostforooshan, M.H., Sadrabadi, N., Moinuddin, M., Evaluation of price manipulation using quadratic discriminant analysis models and genetic algorithm based on artificial neural network, Financial Knowledge of Securities Analysis, 2014, P. 119-143. (in persian)


• Putnins, T., An Overview of Market Manipulation, Corruption and Fraud in Financial Markets Malpractice, Misconduct and Manipulation, 2020. Doi: https://ssrn.com/abstract=3398258


• Rabiee, R., Nadiri, M., Peymani, M., Jaberizade, A., Investigating the effect of price manipulation on market efficiency in Tehran Stock Exchange, Stock Exchange Quarterly, 2018, 11(43), P. 113-131. (in persian)


• Rahmani, A., Zanjirdar, M., Ghiabi, H., Effect of Peer Performance, Future Competitive Performance, and Factors of Correlation with Peer Companies on Manipulation of Abnormal Real Operations, Advances in Mathematical Finance and Applications, 2021, 6(1), P.57-70. Doi: 10.22034/amfa.2020.1875478.1272


• Roodposhti, F.R., Shams, M.F., Kordlouie, H., Forecasting stock price manipulation in capital market. International Journal of Economics and Management Engineering, 2011, 5(8), P. 957-967.

Doi: 10.5281/zenodo.1329717

• Takahashi, S., Chen, Y., Tanaka-Ishii, K., Modeling financial time-series with generative adversarial networks, Physica A: Statistical Mechanics and its Applications, 2019, 527, P. 121261.

Doi: 10.1016/j.physa.2019.121261

• Tohidi, M., Mousavian, S.A., Jurisprudential study and analysis of stock exchange based on price manipulation in the securities market, Stock Exchange Quarterly, 2018, 11, P. 79-113. (in persian)


• Westphal, C., Blaxton, T., Data mining solutions: methods and tools for solving real-world problems, John Wiley and Sons, Inc. ISBN: 978-0-471-25384-6


• Xu, I., Livshits, B., The anatomy of a cryptocurrency pump-and-dump scheme, In28th USENIX Security Symposium (USENIX Security 19), 2019, P.1609–1625. https://www.usenix.org/system/files/sec19-xu-jiahua_0.pdf


• Zhai, J., Cao, Y., and Ding, X., Data analytic approach for manipulation detection in stock market. Review of Quantitative Finance and Accounting, 2018, 50(3), P.897-932. Doi: 1007/s11156-017-0650-0


• Zheng , Y.J., Zhou, X.H., Sheng, W.G., Xue, Y., Chen, S.Y., Generative adversarial network based telecom fraud detection at the receiving bank. Neural Networks, 2018, 102, P. 78-86. Doi: 10.1016/j.neunet.2018.02.015


• Zheng, Y. J., Ling, H. F., Xue, J. Y., and Chen, S. Y., Population classification in fire evacuation: A multi-objective particle swarm optimization approach, IEEE Transactions on Evolutionary Computation, 2013, 18(1), P.70-81. Doi: 10.1109/TEVC.2013.2281396


• Interview with Capital Market Expert (Part 1), How is the loss of microfinance on the stock exchange minimized?, https://plus.irna.ir/news/84140636/, 2021.