Dark Pools, High-Frequency Trading, and the Financial Transaction Tax: a Solution Or Complication?
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Ethics in Financial Markets
Chapter Five Ethics in Financial Markets Anything that can be owned can be traded, and if trading in something is frequent, a market probably exists for that purpose. This holds true not only for commodities and valuable objects, such as pork bellies and French Impres- sionist paintings, but also for financial instruments of all kinds. However, unlike pork bellies, which can be carved up and packaged only in limited ways, financial instruments can take a wide variety of forms for trade in many dif- ferent markets. With puts and calls, swaps and strips, and a host of other colorfully named instruments, the possibilities for trading in financial markets are limited only by human inventiveness and the constraints of law—which may often be gotten around with even more inventiveness. The broad aim of financial market regulation is to secure “fair and orderly markets” or “just and equitable principles of trade.” These expressions, which are standard in securities law and market rules, combine the economic value of efficiency with an ethical concern for fairness or equity, thereby giving rise to the familiar equity/efficiency trade-off. When applied to markets, the concepts of fairness, justice, and equity (which are roughly synonyms) serve mainly to forbid fraud and manipulation, the violation of certain rights, and the exploitation of asymmetries in such matters as information and bargaining power. Prohibitions of unfair market practices are designed to protect both market participants and the integrity of markets themselves, which cannot function properly when they lack fairness. In addition to an examination of what constitutes fairness in markets, this chapter examines three specific areas where unfairness is often alleged. -
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]. -
I Mercati Veloci: Il Trading Ad Alta Frequenza
Corso di Laurea Magistrale in Economia e Finanza (Ordinamento ex D.M. 270/2004) Tesi di Laurea Magistrale I Mercati veloci: il Trading ad Alta Frequenza Relatore Ch. Prof. Marcella LUCCHETTA Correlatore Ch. Prof. Antonio PARADISO Laureando Francesco CARUZZO Matricola 842173 Anno Accademico 2016 / 2017 INDICE INTRODUZIONE…………………………………………………………...…………………4 CAPITOLO I EVOLUZIONE DEL TRADING ELETTRONICO 1.1 Evoluzione storica ed informatizzazione del Trading…………………….……………6 1.2 Drivers dello sviluppo del AT/HFT………………………………………….…………...8 1.3 Flash Crash: 6 Maggio 2010………………………………………………...…………….12 CAPITOLO II HIGH FREQUENCY TRADING: DEFINIZIONI E CONCETTI CONNESSI 2.1 Trading algoritmico……………………………………………...………………………..14 2.2 High Frequency Trading………………………………………..………………………..16 2.3 Concetti connessi……………………………………………………….…………………20 2.2.1 Market making…………………………………………………..…………………20 2.2.2 Quantitative Portfolio Management (QPM)…………………………………….21 2.2.3 Smart Order Routing (SOR)………………………………………………………23 CAPITOLO III STRATEGIE HIGH FREQUENCY TRADING 3.1 Strategie passive……………………………………………………………….………….26 3.2 Strategie aggressive……………………………………………………………….………33 3.2.1 Politiche riguardanti le strategie HF aggressive……………………….……….38 2 CAPITOLO IV IMPATTI POSITIVI E POSSIBILI EFFETTI NEGATIVI DEL HIGH FREQUENCY TRADING 4.1 Impatti positivi del Trading ad Alta Frequenza…………………………….…………42 4.2 Impatti negativi del Trading ad Alta Frequenza………………………………………47 CAPITOLO V TREND E PROFITTABILITA’ DEL TRADING AD ALTA FREQUENZA 5.1 Order to Trade Ratio (OTR)…………………………………………………...…………57 -
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 Flash Crash of May 2010: Accident Or Market Manipulation? Chris Rose, Capella University, USA
Journal of Business & Economics Research – January, 2011 Volume 9, Number 1 The Flash Crash Of May 2010: Accident Or Market Manipulation? Chris Rose, Capella University, USA ABSTRACT On May 6th., 2010, the Dow fell about a thousand points in a half hour and Wall Street lost $800 billion of value. Some claim that it was just an isolated incident and there was nothing nefarious but with the majority of trading being done by electronic exchanges and with the increase in High Frequency Trading, evidence is emerging that the crash just might have been a case of deliberate manipulations of the market. Keywords: Flash crash; market manipulation; SEC; NYSE BACKGOUND tock markets today are very different from the first organized stock exchange, which was created in 1792 at Wall Street, New York. There a small group of financial leaders signed an agreement on fees, rules and S regulations that would apply in transactions. Every day securities were auctioned and the seller paid a commission on each stock or bond sold. These trades took place in a free and open market where willing buyers and sellers assembled and freely exchanged goods, services and information. Since 1998, the Securities and Exchange Commission (SEC) authorized the formation of electronic exchanges, so that anyone with a computer can compete within established markets such as the New York Stock Exchange (NYSE). About 97% of NYSE trades today are executed by computers on electronic communication networks (ECNs) which in the last decade have quickly replaced traders, trading on floors as the main global venue for buying and selling every asset, derivative, and contract (Stokes, 2009). -
UNITED STATES DISTRICT COURT SOUTHERN DISTRICT of NEW YORK X CITY of PROVIDENCE, RHODE ISLAND, Individually and on Behalf of Al
UNITED STATES DISTRICT COURT SOUTHERN DISTRICT OF NEW YORK x CITY OF PROVIDENCE, RHODE ISLAND, : Civil Action No. Individually and on Behalf of All Others Similarly: Situated, : CLASS ACTION : Plaintiff, : COMPLAINT FOR VIOLATION OF THE : FEDERAL SECURITIES LAWS vs. : : BATS GLOBAL MARKETS, INC., BOX : OPTIONS EXCHANGE LLC, CHICAGO : BOARD OPTIONS EXCHANGE, INC., : CHICAGO STOCK EXCHANGE, INC., C2 : OPTIONS EXCHANGE, INC., DIRECT EDGE : ECN, LLC, INTERNATIONAL SECURITIES : EXCHANGE HOLDINGS, INC., THE NASDAQ : STOCK MARKET LLC, NASDAQ OMX BX, : INC., NASDAQ OMX PHLX, LLC, NATIONAL : STOCK EXCHANGE, INC., NEW YORK : STOCK EXCHANGE, LLC, NYSE ARCA, : INC., ONECHICAGO, LLC, BANK OF : AMERICA CORPORATION, BARCLAYS PLC, : CITIGROUP INC., CREDIT SUISSE GROUP : AG, DEUTSCHE BANK AG, THE GOLDMAN : SACHS GROUP, INC., JPMORGAN CHASE & : CO., MORGAN STANLEY & CO. LLC, UBS : AG, THE CHARLES SCHWAB : CORPORATION, E*TRADE FINANCIAL : CORPORATION, FMR, LLC, FIDELITY : BROKERAGE SERVICES, LLC, SCOTTRADE : FINANCIAL SERVICES, INC., TD : AMERITRADE HOLDING CORPORATION, : CITADEL LLC, DRW HOLDINGS, LLC, GTS : SECURITIES, LLC, HUDSON RIVER : TRADING LLC, JUMP TRADING, LLC, KCG : HOLDINGS, INC., QUANTLAB FINANCIAL : LLC, TOWER RESEARCH CAPITAL LLC, : TRADEBOT SYSTEMS, INC., TRADEWORX : INC., VIRTU FINANCIAL INC. and CHOPPER : TRADING, LLC, : : Defendants. : x DEMAND FOR JURY TRIAL SUMMARY OF THE COMPLAINT 1. This securities class action is brought on behalf of public investors who purchased and/or sold shares of stock in the United States between April 18, 2009 and the present (the “Class Period”) on a registered public stock exchange (the “Exchange Defendants”) or a United States- based alternate trading venue and were injured as a result of the misconduct detailed herein (the “Plaintiff Class”). 2. -
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. -
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. -
Modelling and Mitigation of Flash Crashes
Munich Personal RePEc Archive Modelling and mitigation of Flash Crashes Fry, John and Serbera, Jean-Philippe School of Computing Mathematics and Digital Technology, Manchester Metropolitan University, Sheffield Business School, Sheffield Hallam University 12 September 2017 Online at https://mpra.ub.uni-muenchen.de/82457/ MPRA Paper No. 82457, posted 08 Nov 2017 22:35 UTC Modelling and mitigation of Flash Crashes September 2017 Abstract The algorithmic trading revolution has had a dramatic effect upon markets. Trading has become faster, and in some ways more efficient, though potentially at the cost higher volatility and increased uncertainty. Stories of predatory trading and flash crashes constitute a new financial reality. Worryingly, highly capitalised stocks may be particularly vulnerable to flash crashes. Amid fears of high-risk technology failures in the global financial system we develop a model for flash crashes. Though associated with extreme forms of illiquidity and market concentration flash crashes appear to be unpredictable in advance. Several measures may mitigate flash crash risk such as reducing the market impact of individual trades and limiting the profitability of high-frequency and predatory trading strategies. JEL Classification: C1 F3 G1 K2 Keywords: Flash Crashes; Flash Rallies; Econophysics; Regulation 1 Introduction Algorithmic Trading (AT) incorporates a range of high speed and predatory strategies termed Flash Trading Strategies by Lewis (2014) in recognition of the sheer speed involved – orders of magnitude faster than the blink of a human eye. Searching for a competitive edge the need for speed even led to the construction of new optic fibre tunnels to shorten the network distance between New York and Chicago to save crucial milliseconds. -
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