The Joint Impact of Fragmentation into the Dark and Algorithmic Trading on Implicit Trading Costs and Market Manipulation

2021 ◽  
pp. joi.2021.1.211
Author(s):  
Michael Aitken ◽  
Drew Harris ◽  
Frederick Harris
2020 ◽  
Vol 26 (4) ◽  
pp. 796-814
Author(s):  
E.K. Ovakimyan

Subject. The article examines the laws regulating insider trading. Objectives. The study outlines recommendations for refining Law On Countering the Illegal Use of Insider Information and Market Manipulation and Amendments to Some Legislative Acts of the Russian Federation, № 224-ФЗ of July 27, 2010. Methods. The methodological framework includes a general dialectical method, analysis and synthesis, induction and deductions, and some specific methods, such as comparative and formal logic analysis to specify the definition of insider information, structural logic and functional analysis to improve the mechanism for countering insider trading and market manipulation. Results. We discovered key drawbacks to be addressed so as to improve the business environment in Russia. Although the Russia laws mainly mirror the U.S. laws, they present a more extended list of terms concerning the insider information. I believe the legislative perfection should be continued. Conclusions and Relevance. The study helps apply the findings to outline a new legislative regulation or amend the existing ones, add a new mention on the course of financial markets to students’ books, develop new methods for detecting and countering and improving the existing ones. If all parties to insider relationships use the findings, they will prevent insider trading crimes in financial markets and (or) reduce the negative impact of such crimes on the parties.


2020 ◽  
Vol 42 (1) ◽  
pp. 33-46
Author(s):  
Raúl Gómez-Martínez ◽  
Camila Marqués-Bogliani ◽  
Jessica Paule-Vianez

Behavioural finance has shown that investment decisions are the result of not just rational but also emotional brain processes. On the assumption that emotions affect financial markets, it would seem likely that football results might have a measurable effect on financial markets. To test this, this study describes three algorithmic trading systems based exclusively on the results of three top European football teams (Juventus, Bayern München and Paris St Germain) opening long or short positions in the next market season of the futures market of the index of each country (MIB (Milano Italia Borsa), DAX (Deutscher Aktien Index) and CAC (Cotation Assistée en Continu). Depending on the outcome of the last game played a long position was taken after a victory and a short position after a draw or defeat. The results showed that the algorithmic systems were profitable in the case of Juventus and Bayern whereas in the case of PSG, the system was profitable, but in an inverse way. This study shows that investment strategies that take account of sports sentiment could have a profitable outcome.


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