Journal of Applied Economic Research
ISSN 2712-7435
Necessity and Directions of State Adjustment and Prevention of Manipulative Transactions on the Stock Market
Zaborovskiy V.E., Zaborovskaya A.E., Pletnev K.V.
Abstract
The stock-market is one of the most important segments of any national economy. At the same time, the operation of the stock-market is always closely connected with risk, one of the manifestations of which is the involvement of transaction participants in manipulative practices. Unfortunately, only a small amount of modern research is devoted to this problem. The concept “manipulative trade” does not reflect modern challenges and needs constant adaptation. The article suggests concentrating on detailing and countering a specific list of manipulative practices, based on the damage they cause. The subject of the study are manipulative transactions in the stock market and the methods for identifying manipulative transactions on the Russian stock market. The purpose of the study is to develop specific proposals and select statistical methods which would be relevant to the Russian stock market, to improve the existing system of state control, aimed on identifying various types and ways of manipulative trading in the stock market. Within the framework of the study such methods were used as: abstract-logical method, factor analysis, analogy method, k nearest-neighbor method, system approach, comparative assessment method, economic-statistical method, extrapolation method. The authors present a classification of the methods of manipulation, the properties of which have not been yet touched upon in the works of Russian economists. The also study examined a classification of the statistical methods for identifying manipulative trade. On the basis of foreign research, it has been suggested that the most effective of them can be used in the domestic economy. The practical significance of the research consists in creating and testing a statistical machine algorithm, based on the k nearest neighbors method, in the actual conditions of the Russian stock market, which can detect non-standard trading operations. The article presents statistical information which reflects the dynamics of the individual properties of the Russian stock market. The authors propose formulas for calculating reduction in labor costs thanks to primary processing of information through the introduction of the k nearest neighbors method, as well as legislative and practical proposals for improving the efficiency of the national system of detecting manipulative trading on the stock market. The application of special technical approaches during the research reflects the originality of the methodology used by the authors.
Keywords
manipulative transaction; stock market; Bank of Russia; securities market; broker; trader; insider dealing; market abuse; financial technology; k nearest neighbors method; adjustment.
References
1. Selivanovsky, A.S. (2014). Pravovoe regulirovanie rynka tsennykh bumag [Legal regulation of the stock market]. Moscow, Higher School of Economics.
2. Bekriashev, A.K. (2012). Insaiderskaia torgovlia i problemy ekonomicheskoi bezopasnosti v sfere fondovogo rynka (Insider trading and problems of economic security in the stock market). Nauchnyi vestnik Omskoi akademii MVD Rossii (Scientific Bulletin of the Omsk Academy of the MIA of Russia), No. 2(45), 75–79.
3. Abdullin, A.R., Farrakhetdinova, A.R. (2015). Gipoteza effektivnosti rynka v svete teorii finansov [A hypothesis about market effectiveness in the context of the theory of finance]. Upravlenie ekonomicheskimi sistemami: Elektronnyi nauchnyi zhurnal [Economic systems management], No. 4. Available at:http://www.uecs.ru/гусы-76–762015/item/3473–2015–04–23–12–18–35
4. Jensen, М.С. (2008). The Use of Relational Discrimination to Manage Market Entry: When Do Social Status and Structural Holes Work Against You? Academy of Management Journal, Vol. 51,723-743. 5. Akhmedov, T.Ch. (2015). Metody protivodeistviia nepravomernomu ispol'zovaniia insaiderskoi informatsii i manipulirovaniiu rynkom v sisteme obespecheniia ekonomicheskoi bezopasnosti gosudarstva [Methods of countering misuse of insider information and market manipulation in the system of national economic security]. PhD dissertation in economics. Sankt-Peterburg, 2015, 155.
6. Kalinina, Iu.V. (2016) Pravovoe poniatie i formy manipulirovaniia rynkom (Legal concept and forms of a market manipulation). Leningradskii iuridicheskii zhurnal [Leningrad Juridical Journal], No. 4 (46), 67–73.
7. United States the court’s decision No. 20597 (1971). Cargill Inc. v. Hardin.
8. Avgouleas, E. (2010). The Mechanics and Regulation of Market Abuse: A Legal and Economic Analysis. Oxford University Press, 507.
9. Fischel, D.R., Ross, D.J. (1991). Should the Law Prohibit “Manipulation” in Financial Markets? Harvard Law Review, Vol. 105, 503–553.
10. Bobkov, O.V. (2017). Manipulirovanie rynkom: problemy effektivnosti ugolovno-pravovogo zapreta (Market manipulation: The problems of efficiency of criminal law prohibition). Iuridicheskaia nauka i pravookhranitel'naia praktika (Legal Science and Law Enforcement Practice), No. 2, 206–211.
11. Lin, T.C.W. (2015). Reasonable Investors. Boston University Law Review, Vol. 95, No. 2, 461–518.
12. Silvani, A. (2009) Beat the FOREX Dealer. An insider's look into trading today's foreign exchange market. Wiley
13. Teall, J.L. (2013). Financial Trading and Investing. Academic Press, 863.
14. Andrianova, L.N., Guseva, I.A. (2017). Osobennosti vedeniia biznesa na rossiiskom fondovom rynke v sootvetstvii s mezhdunarodnymi standartami (Features of doing business on the Russian stock market in accordance with international standards). Regional'naia ekonomika i upravlenie: elektronnyi nauchnyi zhurnal (Regional economics and management: electronic scientific journal), No. 2. Available at: eee-region.ru/article/5016/.
15. Guliatkin, A.I. (2016). Metody manipuliatsii tsenami na fondovom rynke pri provedenii sdelki vrazhdebnogo pogloshcheniia (Methods of price manipulation on a stock market during hostile takeover of a company). Vestnik Gosudarstvennogo universiteta upravleniia [Bulletin of the State University of Management], No. 7-8, 137–141.
16. Emel'ianova, E.A. (2013). Informatsionnaia priroda manipulirovaniia rynkom (Informative Nature of Market Manipulation). Vestnik SPbGU. Pravo (Vestnik SPbSU. Law), No. 3, 23–31.
17. Verstein, A. (2018). Insider tainting: Strategic tipping of material nonpublic information. Northwestern University Law Review, Vol. 112, No. 4, 725–788. 18. Aldridge, I., Krawciw, S. (2017). Real-Time Risk: What Investors Should Know About FinTech, High-Frequency Trading, and Flash Crashes. Wiley, 224. 19. Scopino, G. (2015). The questionable legality of high-speed «pinging» and «front running» in the futures market. Connecticut Law Review, Vol. 47, No. 3, 607-697.
20. FINRA sanctions Trillium Brokerage Services, director of trading, chief compliance officer and nine traders $2.26 million for illicit “layering” trading strategy (2010). Corporate & Financial Weekly Digest. Available at: documents.jdsupra.com/da1d595e-b7f3-4487-9d08-9d1e16b472ce.pdf.
21. Yadav, Ye. (2015). Insider Trading in Derivatives Markets. Georgetown Law Journal, Vol. 103, 381–432.
22. Foucault, T., Pagano, M., Röell, A. (2013). Market Liquidity: Theory, Evidence, and Policy. Oxford University Press, 424.
23. Aldridge, I. (2014). High-frequency runs and flash-crash predictability. Journal of Portfolio Management, Vol. 14, No. 3, 113–123.
24. Nivorozhkina, L.I., Arzhenovsky, S.V. (2018). Mnogomernye statisticheskie metody v ekonomike [Multi-dimensional statistical methods in economics]. Moscow, INFRA-M.
25. Galochkin, M.I. (2010). Zarubezhnyi opyt gosudarstvennogo regulirovaniia rynka proizvodnykh tsennykh bumag i vozmozhnosti ego adaptatsii k rossiiskoi praktike (Foreign experience of state regulation of derivatives markets and their possible adaptation). Ekonomicheskie nauki (Economic Sciences), No. 6 (67), 162–165.
26. Fedorova, E.A., Sivak, A.R. (2012). Sravnenie modelei CAPM i Famy – Frencha na rossiiskom fondovom rynke (Compare models and CAPM Famy-Frencha on stock market). Finansy i kredit (Finance and Credit), No. 42 (522), 42–48.
27. Lapko, A.V., Lapko, V.A. (2012). Svoistva neparametricheskoi otsenki mnogomernoi plotnosti veroiatnosti nezavisimykh sluchainykh velichin [Properties of distribution-free evaluation of multi-dimensional probability density of independent random values]. Statisticheskie sistemy [Statistical Systems], No. 1(31), 166–174.
28. Slama, M., Stromma, E. (2008). Trade-Based Stock Price Manipulation and Sample Entropy. Master’s Thesis in Finance. Stockholm School of Economics, 69.
29. Golovinov, A.O., Klimova, E.N. (2017). Preimushchestva neironnykh setei pered traditsionnymi algoritmami [Advantages of neural networks over traditional algorithms]. Eksperimental'nye i teoreticheskie issledovaniia v sovremennoi nauke : sbornik statei po materialam V Mezhdunarodnoi nauchno-prakticheskoi konferentsii Proceedings of the 5th international scientific conference "Experimental and theoretical investigations in modern science", 11–15.
30. Kryshtanovsky, A.O. (2006). Analiz sotsiologicheskikh dannykh s pomoshch'iu paketa SPSS [Analysis of sociological data with the use of IBM SPSS software]. Moscow, Higher School of Economics
31. Shiriaev, A. (2014). Veroiatnostno-statisticheskie metody v teorii priniatiia reshenii [Probability and Statistical Methods in Decision Making Theory]. Moscow, MTsNMO.
32. Haykin, S. (2016). Neural Networks: A Comprehensive Foundation. Prentice Hall.
33. Levitin, A., Krasikov, I. (2006). Algoritmy. Vvedenie v razrabotku i analiz [Algorithms. Introduction to development and analysis]. Moscow, Vil'iams.
34. Glukhova, A.I. (2014). Sushchnost' metoda priniatiia upravlencheskikh reshenii «derevo reshenii» (The main idea of the adoption of management decisions method "Decision tree"). Master’s Journal, No. 2, 316–321.
35. Chernyshova, G.Iu. (2012). Intellektual'nyi analiz dannykh [Intellectual analysis of data]. Saratov, Saratov Socio-Economic Institute.
36. Li, A., Wu, J., Liu, Z. (2017). Market manipulation detection based on classification methods. Procedia Computer Science, Vol. 122, 788-795.
37. Palshikar, G.K., Apte, M.M. (2008). Collusion set detection using graph clustering. Data Mining Knowledge Discovery, Vol. 16, No. 22, 135–164.
38. Islam, M.N., Haque, S.R., Alam, K.M., Tarikuzzaman, M. (2009). An approach to improve collusion set detection using MCL algorithm. Proceedings of 12th International Conference on Computers and Information Technology. Dhaka, 237–242.
39. Franke, M., Hoser, B. Schroder, J. (2008). On the analysis of irregular stock market trading behavior. Proceedings of the 31st Annual Conference «Data Analysis, Machine Learning and Applications». Edited by C. Preisach, H. Burkhardt, L. Schmidt-Thieme, R. Decker. Berlin, Heidelberg, Springer Berlin Heidelberg, 355-362.
40. Wang, J.J., Zhou, S.G., Guan, J.H. (2012). Detecting potential collusive cliques in futures markets based on trading behaviors from real data. Neurocomputing, Vol. 92, 44–53.
41. Zalesova, A.A. (2017). Rossiiskii fondovyi rynok: sovremennye tendentsii i problem (Russian Stock Market: Contemporary Trends and Problems). Ekonomika i biznes: teoriia i praktika (Economy and business: Theory and practice), No. 7, 29–32.
42. Hastie, T., Tibshirani, R., Friedman, J. (2017). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. Springer, 745.
About Authors
Zaborovskiy Vyacheslav Evgen’evich – Candidate of Economic Sciences, Associate Professor, Department of Finance, Monetary Circulation and Credit, Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russia (620002, Ekaterinburg, Mira street, 19); e-mail: vezletters@gmail.com.
Zaborovskaya Alena Evgen’evna – Candidate of Economic Sciences, Associate Professor, Department of Finance, Monetary Circulation and Credit, Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russia (620002, Ekaterinburg, Mira street, 19); e-mail: zaborovskaya.alena@bk.ru.
Pletnev Konstantin Vyacheslavovich – Master's Degree Student, at the Department of Finance, Monetary Circulation and Credit, Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russia (620002, Ekaterinburg, Mira street, 19); e-mail: fgeyrysdea@gmail.com.
For citation
Zaborovskiy V.E., Zaborovskaya A.E., Pletnev K.V. Necessity and Directions of State Adjustment and Prevention of Manipulative Transactions on the Stock Market. Bulletin of Ural Federal University. Series Economics and Management, 2018, Vol. 17, No. 5, 839-868. DOI: 10.15826/vestnik.2018.17.5.038.
Article info
Received August 12, 2018; Accepted September 18, 2018.
DOI: http://dx.doi.org/10.15826/vestnik.2018.17.5.038
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