1 Response to SEC Questions Regarding the List of Rules to Be
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Impact on Financial Markets of Dark Pools, Large Investor, and HFT
Impact on Financial Markets of Dark Pools, Large Investor, and HFT Shin Nishioka1, Kiyoshi Izumi1, Wataru Matsumoto2, Takashi Shimada1, Hiroki Sakaji1, and Hiroyasu Matushima1 1 Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan 2 Nomura Securities Co., Ltd.? [email protected] Abstract. In this research, we expanded an artificial market model in- cluding the lit market, the dark pools, the large investor, and the market maker (HFT). Using the model, we investigated their influences on the market efficiency and liquidity. We found that dark pools may improve the market efficiency if their usage rate were under some threshold. Es- pecially, It is desirable that the main users of the dark pool are large investors. A certain kind of HFT such as the market making strategy may provide the market liquidity if their usage rate were under some threshold. Keywords: Artificial Market · Dark pool · Large investor. 1 Introduction Dark pools, private financial forums for trading securities, are becoming widely used in finance especially by institutional investors [15]. Dark pools allow in- vestors to trade without showing their orders to anyone else. One of the main advantages of dark pools is their function to significantly reduce the market impact of large orders. Large investors have to constantly struggle with the problem that the market price moves adversely when they buy or sell large blocks of securities. Such market impacts are considered as trading costs for large investors. Hiding the information of large orders in dark pools may decrease market impacts. Moreover, from the viewpoint of a whole market, dark pools may stabilize financial markets by reducing market impacts[7]. -
A Pecking Order of Trading Venues
Journal of Financial Economics 124 (2017) 503–534 Contents lists available at ScienceDirect Journal of Financial Economics journal homepage: www.elsevier.com/locate/jfec R Shades of darkness: A pecking order of trading venues ∗ Albert J. Menkveld a, Bart Zhou Yueshen b, Haoxiang Zhu c, a Vrije Universiteit Amsterdam and Tinbergen Institute, De Boelelaan 1105, Amsterdam 1081 HV, Netherlands b INSEAD, Boulevard de Constance, 77300 Fontainebleau, France c MIT Sloan School of Management and NBER, 100 Main Street E62-623, Cambridge, MA 02142, USA a r t i c l e i n f o a b s t r a c t Article history: We characterize the dynamic fragmentation of U.S. equity markets using a unique data Received 16 September 2015 set that disaggregates dark transactions by venue types. The “pecking order” hypothesis Revised 13 June 2016 of trading venues states that investors “sort” various venue types, putting low-cost-low- Accepted 22 June 2016 immediacy venues on top and high-cost-high-immediacy venues at the bottom. Hence, Available online 8 March 2017 midpoint dark pools on top, non-midpoint dark pools in the middle, and lit markets at the JEL classification: bottom. As predicted, following VIX shocks, macroeconomic news, and firms’ earnings sur- G12 prises, changes in venue market shares become progressively more positive (or less nega- G14 tive) down the pecking order. We further document heterogeneity across dark venue types G18 and stock size groups. D47 Published by Elsevier B.V. Keywords: Dark pool Pecking order Fragmentation 1. Introduction R For helpful comments and suggestions, we thank Bill Schwert (ed- A salient trend in global equity markets over the last itor), an anonymous referee, Mark van Achter, Jim Angel, Matthijs decade is the rapid expansion of off-exchange, or “dark” Breugem, Adrian Buss, Sabrina Buti, Hui Chen, Jean-Edourd Colliard, Ca- role Comerton-Forde, John Core, Bernard Dumas, Thierry Foucault, Frank trading venues. -
The Upstairs Market for Large-Block Transactions: Analysis and Measurement of Price Effects
The Upstairs Market for Large-Block Transactions: Analysis and Measurement of Price Effects Donald B. Keim University of Pennsylvania Ananth Madhavan University of Southern California, Los Angeles This article develops a model of the upstairs market where order size, beliefs, and prices are determined endogenously. We test the model’s predictions using unique data for 5,625 equity trades during the period 1985 to 1992 that are known to be upstairs transactions and are iden- tified as either buyer or seller initiated. We find that price movements prior to the trade date are significantly positively related to trade size, con- sistent with information leakage as the block is “shopped” upstairs. Further, the temporary price impact or liquidity effect is a concave func- tion of order size, which may result from up- stairs intermediation. We thank Margaret Forster, Thomas George, Larry Glosten, Gary Gorton, Larry Harris, David Mauer, Oded Sarig, Byron Snider, Chester Spatt (the edi- tor), and an anonymous referee for their helpful comments. Seminar partic- ipants at Boston College, Columbia University, INSEAD, London Business School, Ohio State University, Temple University, University of Maryland, University of Southern California, University of Wisconsin, Washington Uni- versity, the 1991 Johnson Symposium, the American Finance Association Meetings, and the European Finance Association Meetings provided many useful suggestions. We also thank Dimensional Fund Advisors, Inc., for pro- viding us with the data used here, and the Q-Group and the Geewax-Terker Research Program at Wharton for their financial assistance. Edward Nelling and Pasi Hamalainen provided expert research assistance. Any errors are entirely our own. -
Dark Pools and High Frequency Trading for Dummies
Dark Pools & High Frequency Trading by Jay Vaananen Dark Pools & High Frequency Trading For Dummies® Published by: John Wiley & Sons, Ltd., The Atrium, Southern Gate, Chichester, www.wiley.com This edition first published 2015 © 2015 John Wiley & Sons, Ltd, Chichester, West Sussex. Registered office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or trans- mitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor men- tioned in this book. -
Informational Inequality: How High Frequency Traders Use Premier Access to Information to Prey on Institutional Investors
INFORMATIONAL INEQUALITY: HOW HIGH FREQUENCY TRADERS USE PREMIER ACCESS TO INFORMATION TO PREY ON INSTITUTIONAL INVESTORS † JACOB ADRIAN ABSTRACT In recent months, Wall Street has been whipped into a frenzy following the March 31st release of Michael Lewis’ book “Flash Boys.” In the book, Lewis characterizes the stock market as being rigged, which has institutional investors and outside observers alike demanding some sort of SEC action. The vast majority of this criticism is aimed at high-frequency traders, who use complex computer algorithms to execute trades several times faster than the blink of an eye. One of the many complaints against high-frequency traders is over parasitic trading practices, such as front-running. Front-running, in the era of high-frequency trading, is best defined as using the knowledge of a large impending trade to take a favorable position in the market before that trade is executed. Put simply, these traders are able to jump in front of a trade before it can be completed. This Note explains how high-frequency traders are able to front- run trades using superior access to information, and examines several proposed SEC responses. INTRODUCTION If asked to envision what trading looks like on the New York Stock Exchange, most people who do not follow the U.S. securities market would likely picture a bunch of brokers standing around on the trading floor, yelling and waving pieces of paper in the air. Ten years ago they would have been absolutely right, but the stock market has undergone radical changes in the last decade. It has shifted from one dominated by manual trading at a physical location to a vast network of interconnected and automated trading systems.1 Technological advances that simplified how orders are generated, routed, and executed have fostered the changes in market † J.D. -
Cross-Trading by ERISA Plan Managers
Cross-Trading by ERISA Plan Managers Final Report Thomas H. McInish, Ph.D., C.F.A. Tel: 901-678-4662 Fax: 901-678-3006 Email: [email protected] Cross-Trading by ERISA Plan Managers Executive Summary ERISA prohibits cross trades, the exchange of assets between two accounts without going through a public market. There have been numerous exemption requests motivated by a desire to reduce transaction costs. Mutual funds are permitted to cross trade under Rule 17a-7. Opportunities for cross trades arise when some funds within a group have cash inflows and others have cash outflows and due to differences of opinion among managers for a given mutual fund group about the desirability of holding particular assets. Cross trades represent an economically significant source of savings for mutual funds. With a view toward identifying insights relevant to cross-trading, this study reviews the academic literature dealing with the way financial markets are organized and how this organization affects transaction costs, dealer quotes and prices, and other market characteristics. Transaction costs include direct costs such as commissions and indirect costs such as the bid-ask spread, which covers order processing costs (the normal expenses of providing liquidity) and asymmetric information costs (dealer losses to informed traders). Additional indirect costs are market impact costs, delay costs, and the opportunity costs of missing a trade. Transaction costs typically range from one to four percent, depending on a number of factors such as the type of asset (equities, debt, derivatives, and currencies), daily trading volume in the asset, the size of the order, market conditions (recent news, whether others are buying or selling), and the country in which the asset is traded. -
The Bright Sides of Dark Liquidity
The Bright sides of Dark Liquidity YUXIN SUN1, GBENGA IBIKUNLE*2, DAVIDE MARE3 This Version: June 2018 Abstract Modern financial markets have experienced fragmentation in lit (displayed) order books as well as an increase in the use of dark (non-displayed) liquidity. Recent dark pool proliferation has raised regulatory and academic concerns about market quality implication. We empirically study the liquidity commonalities in lit and dark venues. We find that compared with lit venues, dark venues proportionally contribute more liquidity to the aggregate market. This is because dark pools facilitate trades that otherwise might not easily have occurred in lit venues when the limit order queue builds up. We also find that the spread of the trades subsquent to dark trades tends to be narrow. This is consistent with the order flow competition between venues in that dark trade might give market makers a signal of uninformed liquidity demand to narrow the spread. Based on the recent dark volume cap regulation under MiFID II, we provide causal evidence that dark trading cap brings a negative impact on transaction cost. JEL classification: G10, G14, G15 Keywords: Dark pools, dark trading, MiFID, Multilateral Trading Facilities (MTFs), Liquidity commonality, trading liquidity, Double Volume Cap (DVC), regulation We thank Carol Alexander, Carlo Gresse, Thomas Martha, Sean Foley, Petko Kalev, Tom Steffen, Khaladdin Rzayev, Satchit Sagade, Monika Trepp, Elvira Sojli, Micheal Brolley and Lars Norden for helpful comments and discussions. We are also grateful to the participants at the 2nd Dauphine Microstructure Workshop, the 1st SAFE Market Microstructure Conference, the 1st European Capital Markets Workshop and the 2018 EFMA conference. -
Learning Unfair Trading: a Market Manipulation Analysis from the Reinforcement Learning Perspective
Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspective Enrique Mart´ınez-Miranda and Peter McBurney and Matthew J. Howard Department of Informatics King’s College London fenrique.martinez miranda,peter.mcburney,[email protected] Abstract have names like ramping, wash trading, quote stuffing, lay- ering, spoofing, among others. Spoofing is one of the most Market manipulation is a strategy used by traders to popular strategies that uses non-bona fide orders to improve alter the price of financial securities. One type of ma- nipulation is based on the process of buying or selling the price and is considered illegal by market regulators (Ak- assets by using several trading strategies, among them tas 2013). A similar strategy used by high-frequency traders spoofing is a popular strategy and is considered illegal (HFTs) is called pinging where HFTs place orders without by market regulators. Some promising tools have been the intention of execution, but to find liquidity not displayed developed to detect manipulation, but cases can still be in the order book (where all buy and sell orders are listed found in the markets. In this paper we model spoofing in double auction markets), and has caused controversy as it and pinging trading, two strategies that differ in the le- can be viewed as a manipulative strategy (Scopino 2015). gal background but share the same elemental concept of Studies found in the literature that analyse the problem market manipulation. We use a reinforcement learning of market manipulation have mainly focused on the devel- framework within the full and partial observability of Markov decision processes and analyse the underlying opment of methods for detection. -
Financial Instruments Execution Venues1
Financial instruments execution venues1 Transactions in shares Execution venues Aequitas Lit Book Aequitas NEO Book American Stock Exchange AQUA Aquis Exchange ASX - Center Point Athens Stock Exchange Australian Stock Exchange BAML Instinct X (MLXN) Barclays LX Bats Bats Lis Bats Periodic Auction Order BIDS Trading BNP BIX BNY Millennium BofAML MLXN Bolsa de Madrid Book (auction) Borsa Italiana Budapest Stock Exchange BXE Dark (Bats Europe) Nomura NX BXE Lit (Bats Europe) Canadian Securities Exchange Chi-X Chi-X Australia Chi-X Dark Chi-X Japan CITI CROSS Citibank CitiMatch TORA CLSA Dark Pool CODA Markets Commonwealth Bank of Australia Credit Suisse Cross Finder Crosspoint CS CrossFinder CS Light Pool CXE Lit (Bats Europe) DB SuperX ATS Deutsche Bank SuperX Deutsche Börse, Xetra Equiduct Euronext Amsterdam Euronext Brussels 1 Euronext Lisbon Euronext Paris GS Sigma X GS Sigma X MTF Hong Kong Exchanges and Clearing IEX Instinet BlockMatch Instinet BLX Australia Instinet Canada Cross (ICX) Instinet CBX Hong Kong Instinet Crossing Instinet Hong Kong VWAP Cross Instinet Nighthawk VWAP Irish Stock Exchange ITG Posit Johannesburg Stock Exchange JP Morgan Cross JPM - X KCG MatchIt LeveL ATS Liquidnet Canada Liquidnet Europe Liquidnet H2O London Stock Exchange Macquarie XEN Match Now Moscow Exchange Morgan Stanley MSPool MS Pool NASDAQ NASDAQ Auction on Demand (auction) Nasdaq CX2 Nasdaq CXC Nasdaq OMX Copenhagen Nasdaq OMX Helsinki Nasdaq OMX Stockholm New York Stock Exchange Arca Nordic@Mid Omega - Lynx ATS Omega ATS Oslo Bors OTC Bulletin -
MARKET RULES and Appendices ICE Endex Markets B.V. Atlas Arena
MARKET RULES and appendices ICE Endex Markets B.V. Atlas Arena Amsterdam Australia Building, 3rd floor Hoogoorddreef 7 1101 BA Amsterdam The Netherlands Tel: +31 (0)20 305 5100 TABLE OF CONTENTS Preface - Introduction to the Rules 8 Chapter I. – General Rules 9 I-1 Definitions ........................................................................................................................... 9 I-2 Interpretation of the Rules ................................................................................................ 18 I-3 Structure of the Rules ....................................................................................................... 18 I-4 Objective of the Rules ....................................................................................................... 19 I-5 Requirements for Membership ......................................................................................... 19 I-6 Additional Membership Criteria for Direct Electronic Access Providers ........................... 23 I-7 Responsible Individual ...................................................................................................... 26 I-8 Application Procedure ....................................................................................................... 28 I-9 Obligations of Members .................................................................................................... 29 I-10 Obligations of Members engaging in Algorithmic Trading ................................................ 32 I-11 Rights of ICE -
The Design of Equity Trading Markets in Europe
The design of equity trading markets in Europe An economic analysis of price formation and market data services Prepared for Federation of European Securities Exchanges March 2019 www.oxera.com The design of equity trading markets in Europe Oxera Oxera Consulting LLP is a limited liability partnership registered in England no. OC392464, registered office: Park Central, 40/41 Park End Street, Oxford OX1 1JD, UK; in Belgium, no. 0651 990 151, registered office: Avenue Louise 81, 1050 Brussels, Belgium; and in Italy, REA no. RM - 1530473, registered office: Via delle Quattro Fontane 15, 00184 Rome, Italy. Oxera Consulting GmbH is registered in Germany, no. HRB 148781 B (Local Court of Charlottenburg), registered office: Rahel-Hirsch- Straße 10, Berlin 10557, Germany. Oxera Consulting (Netherlands) LLP is registered in Amsterdam, KvK no. 72446218, registered office: Strawinskylaan 3051, 1077 ZX Amsterdam, The Netherlands. Although every effort has been made to ensure the accuracy of the material and the integrity of the analysis presented herein, Oxera accepts no liability for any actions taken on the basis of its contents. No Oxera entity is either authorised or regulated by the Financial Conduct Authority or the Prudential Regulation Authority within the UK or any other financial authority applicable in other countries. Anyone considering a specific investment should consult their own broker or other investment adviser. Oxera accepts no liability for any specific investment decision, which must be at the investor’s own risk. © Oxera 2019. All rights reserved. Except for the quotation of short passages for the purposes of criticism or review, no part may be used or reproduced without permission. -
Block Trade Agreement for India Working Document, Version 1.0 Last Updated: 17 Nov 2015
Asia Securities Industry & Financial Markets Association Block Trade Agreement for India Working Document, Version 1.0 Last Updated: 17 Nov 2015 www.asifma.org Follow ASIFMA on Twitter and LinkedIn Any person using the Template shall be deemed to have read, understood and agreed to the terms set out below: (i) This document Template (“Template”) is provided solely as a guide and is not intended to be, and must not be regarded or relied upon as, legal or other professional advice or opinions on any matters. Use of the Template is voluntary. You are advised to seek your own professional advice as necessary. (ii) All market participants signing block trade agreements are responsible for exercising their own independent judgment as to: (a) what information, and in what form, they should require the seller/manager to comply with their legal and regulatory obligations; (b) the sufficiency, accuracy, completeness and relevance of any information provided by any seller/manager; and (c) whether a particular agreement is appropriate under the particular circumstances and conditions applicable to them. (iii) For the avoidance of doubt, the fact that a seller/manager has submitted disclosures in accordance with the Template should not be regarded as an endorsement of that seller/manager by ASIFMA or an indication that the seller/manager is necessarily fit, proper or suitable for any particular user or purpose. ASIFMA accepts no responsibility for any information disclosed pursuant to or in accordance with this Template. (iv) Any description of legal or regulatory provisions in the Template is for informational and summary purposes only and is not intended to convey the full extent or details of regulatory obligations that may apply to any firm or individual.