| Autor: |
Rustam Abubakirovich Fayzrakhmanov, Anton Aksenov, Elizaveta Grebenshchikova |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). |
| DOI: |
10.1109/summa50634.2020.9280575 |
| Popis: |
It is now widely recognized that abuse by unscrupulous participants is one of the biggest threats to stock markets. One of the main problem is represented by the manipulative trading of securities on the stock market. Currently, researches in this area are theoretical and existing models identify only deviations from standard market behavior. This approach is ineffective and outdated. The development of a model capable of unambiguously identifying certain types of manipulation will be interesting both for regulators and for market participants themselves. "Front-running" is one of the most common schemes of manipulation. This manipulation has a strong impact on the entire market, because it causes a sharp price movement and distorts the fair execution of trades. Counteracting this scheme of fraud is an urgent task. In the course of this work the model of identification of "front-running" on the stock market, based on the Moscow stock exchange data, was developed. The main characteristics of manipulation and the indicators by which it could be detected were considered. The model makes it possible to determine the threshold values for the selected signs that separate fraudulent deals from others. As a result of the model work, it was possible to identify about 140 cases of manipulation, which can be identified as "front-running". The model works with minimal human involvement, which is especially relevant in the era of algorithmic trading. The data obtained confirm the hypotheses put forward and can be used to improve automated oversight systems. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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