Detecting Social Stock Pumping Using Machine Learning: Empirical Evidence from the Russian Market

Gleb A. Khaziev

National Research University Higher School of Economics, Moscow, Russia

Abstract

In recent years, the Russian stock market has experienced a growing influence of social media on the behavior of retail investors, which creates preconditions for short-term anomalies in stock prices and trading volumes. While the role of social media publications in driving stock dynamics has intensified since 2019, no comprehensive academic or practical studies have yet explored this phenomenon in depth, which highlights the relevance of the present research. The study aims to identify and quantitatively assess cases of social stock pumping—situations where spikes in discussions and sentiment on social media precede abnormal increases in prices and trading volumes of individual securities. The research tests two hypotheses: first, that social media activity can serve as a causal factor in market anomalies; and second, that machine learning models outperform traditional econometric approaches in detecting such cases. The analysis is based on data for 104 Russian companies covering the period from 2019 to 2025, incorporating market features along with aggregated indices of discussion intensity and investor sentiment. The methodology includes constructing a model of normal stock behavior based on historical data and calculating deviations on days identified as potential social pumping events. For classification, four machine learning models (CatBoost, SVM, Random Forest, KNN) and logistic regression were applied. The results confirm the existence of persistent anomalies in price and volume behavior on social pumping days, along with a statistically significant rise in social indicators. Machine learning models achieved ROC-AUC scores exceeding 0.9, thus outperforming many earlier published approaches. The findings contribute to the theoretical development of behavioral finance and offer practical tools for regulators, analysts, and traders to detect market anomalies and incorporate social influence into trading strategies.

Keywords

stock market; stocks; manipulation; social pump; machine learning; investor sentiment.

JEL classification

References

1. Teplova, T.V., Sokolova, T.V., Tomtosov, A.F., Buchko, D.V., Nikulin, D.D. (2022). The sentiment of private investors in explaining the differences in the trade characteristics of the Russian market stocks. Journal of the New Economic Association, 53–84. (In Russ.). https://doi.org/10.31737/2221-2264-2022-53-1-3

          2. Teplova, T., Tomtosov, A., Sokolova, T. (2022). A retail investor in a cobweb of social networks. PLOS ONE, Vol. 17, Issue 12. e0276924. https://doi.org/10.1371/journal.pone.0276924

          3. Konovalova, M.E. (2023). Insider trading and methods of its prevention in the Russian securities market. Creative Economy, Vol. 17, No. 10, 3817–3832. (In Russ.). https://doi.org/10.18334/ce.17.10.119204

          4. Shamanina, E.I., Seroshtan, E.S. (2024). Manipulative transactions: Essence, risk analysis, and approaches to identifying unfair practices in the Russian securities market. Bulletin of the Altai Academy of Economics and Law, No. 7-2, 389–394. (In Russ.). https://doi.org/10.17513/vaael.3611

          5. Sridhar, S., Sanagavarapu, S. (2021). ELM-AD: Extreme Learning Machine Framework for Price and Volume Anomaly Detection in Stock Markets. Proceedings 2021 International Conference on Computing and Communications Applications and Technologies (I3CAT). IEEE, 44–51. https://doi.org/10.1109/I3CAT53310.2021.9629409

          6. Tallboys, J., Zhu, Y., Rajasegarar, S. (2022). Identification of Stock Market Manipulation with Deep Learning. Proceedings of 17th International Conference on Advanced Data Mining and Applications (ADMA 2022). Part I. Edited by B. Li, L. Yue, J. Jiang, W. Chen, X. Li, G. Long, F. Fang, H. Yu. Springer Cham, 408–420.. https://doi.org/10.1007/978-3-030-95405-5_29

          7. Uslu, N.C., Akal, F. (2022). A Machine Learning Approach to Detection of Trade-Based Manipulations in Borsa Istanbul. Computational Economics, Vol. 60, 25–45. https://doi.org/10.1007/s10614-021-10131-8

          8. Ueda, K., Suwa, H., Yamada, M., Ogawa, Y., Umehara, E., Yamashita, T., Tsubouchi, K., Yasumoto, K. (2024). SSCDV: Social media document embedding with sentiment and topics for financial market forecasting. Expert Systems with Applications, Vol. 245, 122988. https://doi.org/10.1016/j.eswa.2023.122988

          9. Li, Y., Bu, H., Li J., Wu, J. (2020). The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning. International Journal of Forecasting, Vol. 36, Issue 4, 1541–1562. https://doi.org/10.1016/j.ijforecast.2020.05.001

          10. Lin, P., Ma, S., Fildes, R. (2024). The extra value of online investor sentiment measures on forecasting stock return volatility: A large-scale longitudinal evaluation based on Chinese stock market. Expert Systems with Applications, Vol. 238, Part B, 121927. https://doi.org/10.1016/j.eswa.2023.121927

          11. Mili, M., Sahut, J., Teulon, F., Hikkerova, L. (2024). A multidimensional Bayesian model to test the impact of investor sentiment on equity premium. Annals of Operations Research, Vol. 334, 919–939. https://doi.org/10.1007/s10479-023-05165-0

          12. Dixon, M.F., Halperin, I., Bilokon, P. (2020). Machine Learning in Finance: From Theory to Practice. Springer, 548 p. https://doi.org/10.1007/978-3-030-41068-1

          13. Cai, Y., Tang, Z., Chen, Y. (2024). Can real-time investor sentiment help predict the high frequency stock returns? North American Journal of Economics and Finance, Vol. 72, 102147. https://doi.org/10.1016/j.najef.2024.102147

          14. Gupta, R., Nel, J., Pierdzioch, C. (2023). Investor confidence and forecastability of US stock market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, Vol. 24, Issue 1, 111–122. https://doi.org/10.1080/15427560.2021.1949719

          15. Chen, H., De, P., Hu, Y.J., Hwang, B.H. (2013). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of Financial Studies, Vol. 27, Issue 5, 1367–1403. https://doi.org/10.1093/rfs/hhaa114

          16. Fama, E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, Vol. 25, No. 2, 383–417. https://doi.org/10.2307/2325486

          17. Samuelson, P.A. (1965). Proof that Properly Anticipated Prices Fluctuate Randomly. Industrial Management Review, Vol. 6, No. 2, 41–49. Available at: https://capital-gain.ru/assets/docs/Proof-that-properly-anticipated-prices-fluctuate-randomly.pdf

          18. Shiller, R.J. (1981). Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends? The American Economic Review, Vol. 71, No. 3, 421–436. Available at: https://www.aeaweb.org/aer/top20/71.3.421-436.pdf

          19. Baker, M., Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, Vol. 61, Issue 4, 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x

          20. Baker, M., Wurgler, J. (2007). Investor Sentiment in the Stock Market. Journal of Economic Perspectives, Vol. 21, No. 2, 129–151. https://doi.org/10.1257/jep.21.2.129

          21. Niu, H., Pan, Q., Xu, K. (2023). Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China’s leading stocks. PLoS ONE, Vol. 18, Issue 11, e0294460. https://doi.org/10.1371/journal.pone.0294460

          22. Liu, Q., Lee, W.S., Huang, M., Wu, Q. (2023). Synergy between stock prices and investor sentiment in social media. Borsa Istanbul Review, Vol. 23, Issue 1, 76–92. https://doi.org/10.1016/j.bir.2022.09.006

          23. Cookson, J.A., Lu, R., Mullins, W., Niessner, M. (2024). The social signal. Journal of Financial Economics, Vol. 158, 103870. https://doi.org/10.1016/j.jfineco.2024.103870

          24. Islam, S.R., Ghafoor, S.K., Eberle, W. (2018). Mining illegal insider trading of stocks: A proactive approach. Proceedings 2018 IEEE International Conference on Big Data. IEEE, 1397–1406. https://doi.org/10.1109/BigData.2018.8622303

          25. Liu, Q., Wang, C., Zhang, P., Zheng, K. (2021). Detecting stock market manipulation via machine learning. International Review of Financial Analysis, Vol. 78, 101887. https://doi.org/10.1016/j.irfa.2021.101887

          26. Chong, E., Han, C., Park, F.C. (2017). Deep learning networks for stock market analysis and prediction. Expert Systems with Applications, Vol. 83, 187–205. https://doi.org/10.1016/j.eswa.2017.04.030

          27. Li, A., Wu, J., Liu, Z. (2017). Market manipulation detection based on classification methods. Procedia Computer Science, Vol. 122, 788–795. https://doi.org/10.1016/j.procs.2017.11.438

          28. Nam, D., Skillicorn, D.B. (2023). Detecting pump & dump stock market manipulation from online forums. arXiv, 2301.11403. https://doi.org/10.48550/arXiv.2301.11403

          29. Domashova, J., Yakimov, D., Bredikhin, D., Gorbunov, K., Slavik, R., Kadyrov, I. (2022). Detection and analysis of atypical stock transactions with possible misuse of insider information and market manipulation: methods and models. Procedia Computer Science, Vol. 213, 165–174. https://doi.org/10.1016/j.procs.2022.11.052

          30. Münster, J., Reichenbach, M., Walther, T. (2024). Robinhood, Reddit, and the news: The impact of traditional and social media on retail investor trading. Journal of Financial Markets, Vol. 71. 100929. https://doi.org/10.1016/j.finmar.2024.100929

          31. Warkulat, M., Pelster, M. (2024). Social media attention and retail investor behavior: Evidence from r/wallstreetbets. International Review of Financial Analysis, Vol. 96, Part B, 103721. https://doi.org/10.1016/j.irfa.2024.103721

          32. Keasey, K., Lambrinoudakis, C., Mascia, D.V., Zhang, Z. (2025). The impact of social media influencers on the financial market performance of firms. European Financial Management, Vol. 31, Issue 2. 745–785. https://doi.org/10.1111/eufm.12513

          33. Semenova, M., Winkler, A. (2021). Reddit's self-organized bull runs: Social contagion, sentiment, and risk. arXiv, 2104.01847. https://doi.org/10.48550/arXiv.2104.01847

          34. Xu, L., Xue, C., Zhang, J. (2024) The impact of investor sentiment on stock liquidity of listed companies in China. Investment Management and Financial Innovations, Vol. 21, Issue 2, 1–14. https://doi.org/10.21511/imfi.21(2).2024.01

          35. Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., Mozetič, I. (2015). The effects of Twitter sentiment on stock price returns. PLOS ONE, Vol. 10, Issue 9, e0138441. doi.org/10.1371/journal.pone.0138441

About Authors

Gleb Andreevich Khaziev

Post-Graduate Student, Basic Department of Financial Markets Infrastructure, Faculty of Economic, Junior Research Fellow at the Center for Financial Research and Data Analysis, National Research University Higher School of Economics, Moscow, Russia (109028, Moscow, Pokrovsky Boulevard, 11); ORCID https://orcid.org/0000-0003-3346-0006 e-mail: glebhaziev@mail.ru

For citation

Khaziev, G.A. (2025). Detecting Social Stock Pumping Using Machine Learning: Empirical Evidence from the Russian Market. Journal of Applied Economic Research, Vol. 24, No. 4, 1445-1474. https://doi.org/10.15826/vestnik.2025.24.4.047

Article info

Received June 9, 2025; Revised July 30, 2025; Accepted August 13, 2025.

DOI: https://doi.org/10.15826/vestnik.2025.24.4.047

Download full text article: