2006
DOI: 10.1177/178359170600100406
|View full text |Cite
|
Sign up to set email alerts
|

REG NMS and Competition in the Alternative Trading Systems in the US

Abstract: Recent technological and regulatory changes have brought about dramatic changes in the structure, conduct and performance of the securities industry in the USA and elsewhere. Trading venues such as ECNs, CNs and other ATSs compete head to head with the traditional exchanges. The SEC has recently adopted the so-called Reg NMS regulation whose major thrust is the order-protection rule. The latter makes regulation symmetric and provides a fairer level of competition. In anticipation of this regulation, the US ind… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2008
2008
2023
2023

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 13 publications
0
2
0
Order By: Relevance
“…Gomber and Gsell (2006) argue that this provides incentives for slow markets to change into fast markets. For an analysis of RegNMS, see Gentzoglanis (2006). See Engelen (2006) for a complete description of the changes in the securities trading landscape in Europe and the United States.…”
Section: Mifid and Trading Platformsmentioning
confidence: 99%
“…Gomber and Gsell (2006) argue that this provides incentives for slow markets to change into fast markets. For an analysis of RegNMS, see Gentzoglanis (2006). See Engelen (2006) for a complete description of the changes in the securities trading landscape in Europe and the United States.…”
Section: Mifid and Trading Platformsmentioning
confidence: 99%
“…Compared with traditional model-based methods, deep learningbased methods can learn efficient and robust feature representations from data without explicit computation, thus improving their robustness for challenging scenes such as illumination changes and less texture. Common classifiers include [18][19][20][21]: softmax classifier, decision trees classifier, and rogerset regression [22][23][24][25], among which the most widely used classifiers in target recognition of images are softmax and SVM classifiers [26][27][28].…”
Section: Introductionmentioning
confidence: 99%