Rigged Markets or Rigged Rhetoric? James J. Angel, Ph.D. CFA Associate Professor McDonough School of Business Georgetown University angelj@georgetown.edu James J. Angel @GUFinProf 1 About #GUFinProf •  Studies financial markets and regulaKon –  2010-­‐2014 Direct Edge Stock Exchange board of directors –  Former Chair of Nasdaq Economic Advisory Board –  TesKfied six Kmes before U.S. Congress –  Visited over 70 exchanges around the world –  11 patents on trading technology •  B.S. Caltech, MBA Harvard, Ph.D. Berkeley •  At Georgetown since 1991 •  At Wharton 2012-­‐2014 •  angelj@georgetown.edu •  202 687 3765 •  Twi_er: #GuFinProf James J. Angel @GUFinProf 2 Key points •  Lots of rhetoric about a “rigged” market structure •  Internal industry infighKng has spilled into the public arena –  The media loves a good fight! •  My take on it: Our markets are (mostly) be_er, but they are different than before. –  Different imperfecKons •  Not all HFTs are evil. –  So-­‐called “predatory” trading has always occurred and will always occur. –  Some market impact is inevitable. James J. Angel @GUFinProf 3 A ba_le is raging over market structure. James J. Angel @GUFinProf 4 Thousands of complaints to SEC •  h_p://www.sec.gov/comments/s7-­‐21-­‐09/s72109-­‐42.htm James J. Angel @GUFinProf 5 It’s not just from the Knfoil hats James J. Angel @GUFinProf 6 Media coverage… James J. Angel @GUFinProf 7 It’s having an impact. James J. Angel @GUFinProf 8 So what’s going on? •  Are markets rigged? •  Or prepared and ready for sailing? •  What should we do about it? James J. Angel @GUFinProf 9 Why should I care? •  Changes in market structure affect how funds should trade. –  Is your fund keeping up? •  Technology brings risk: –  What if the algo used by your trading desk blows up? •  Increasing SEC scruKny of trading pracKces –  Will your fund be in the next sweep? James J. Angel @GUFinProf 10 Who are the combatants? James J. Angel @GUFinProf 11 Commercial disputes •  Players don’t hesitate to lobby regulators and legislators to come to their side. •  Some trading pracKces benefit some players more than others. •  Some parKcipants’ business model: –  “We will protect you from those other bad guys.” •  The media love a fight! James J. Angel @GUFinProf 12 The Al Berkeley Analogy •  Former Nasdaq President •  Our markets today are like a 4-­‐team soccer game. –  Buyers and sellers are compeKng with goals at the North and South ends of the field. –  Exchanges and ATS’s have goals on the East and West sides. –  You can’t tell the players from their jerseys. –  They all want to control the ball. James J. Angel @GUFinProf 13 Long standing populist mistrust of financial markets. •  Andrew Jackson slaying the Second Bank of the US James J. Angel @GUFinProf 14 Whenever there is a loss… •  There is a call to “arrest the usual suspects” James J. Angel @GUFinProf 15 This is rouKne •  Whenever markets go down… –  Anyone who made money gets blamed •  Either for causing the problem •  Or for profiKng from it. •  Calls for reform –  To fix problems –  And to punish the bloodsucking fascist insects that prey upon the people. •  This has been going on for hundreds of years –  Amsterdam a_empted to ban short selling soon aoer modern equity trading began. James J. Angel @GUFinProf 16 The allegaKons against HFT •  “HFT has scared away investors.” •  ColocaKon and high speed data feeds give the HFT sharpshooters an unfair advantage. •  HFTs front run retail and insKtuKonal orders. •  Excessive “fragmentaKon” of venues •  Market manipulaKon by HFTs •  Risk of meltdown from computerized trading. •  Is there really fire with all this smoke, or is this just smokescreen? –  To sell books and brokerage services James J. Angel @GUFinProf 17 “HFT” is a misnomer •  Many different trading strategies use high-­‐
speed computers. –  Some are good, some bad, some ugly… –  Public debate lumps all together 18 The good •  Market making –  Computerized traders add liquidity by posKng limit orders to buy at bid or sell at offer •  Try not to hold a posiKon, long or short, for very long. •  Small profits on many trades •  Market makers provide liquidity that helps low frequency traders! 19 Arbitrage •  Simultaneous trading to profit from price discrepancies in related instruments –  Keeping prices of related instruments in line •  Examples: –  ETFs and cash equiKes –  ADRs and ordinary shares –  Cash equiKes and opKons –  Cash equiKes and futures •  Result: Retail and insKtuKons can safely trade these instruments as their prices will be in line with fundamentals. 20 Stat-­‐arb •  “StaKsKcal arbitrage” –  Even though securiKes aren’t idenKcal, they are close and should move up and down together –  When they get out of alignment: •  BUY LOW AND SHORT HIGH! –  “Pairs trading” 21 Stat-­‐arb: Coke and Pepsi 22 News reacKon (“Event driven”) •  Use computers to read news feeds –  When computer sees good news, buy •  Bad news, sell. 23 Pa_ern recogniKon •  Sniffer algorithms try to spot short-­‐term pa_erns •  How they make money –  HarvesKng informaKon leaked when investors trade pieces of blocks. •  Some call them “predatory algos” 24 HFT front running? •  The game of sniffing out the big players and running in front of them is an old game. James J. Angel @GUFinProf 25 Who are these “predators”? Two types: –  Liquidity takers going along for the ride. –  Liquidity makers gerng out of the way of large orders. •  Market makers make money by taking the other side of lots of small trades. •  When they sense a big trade coming through that will move the price, they scramble to get out of the way. –  Much to the annoyance of the large traders! James J. Angel @GUFinProf 26 But… •  Some market impact is inevitable. •  When a porsolio manager decides to trade a large block, –  The price has to move to make the other side willing to trade. James J. Angel @GUFinProf 27 The bad: ManipulaKon •  Order igniKon –  Put in an order with intent to trigger other orders –  Example: large short sale •
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Depresses price Triggers stop loss orders and margin calls Price pushed down even further Market gets demoralized and others sell Cover short by purchasing at low price. –  Takes advantage of human tendency to cluster orders (like stop loss orders) on round numbers 28 Spoofing •  Put order into market with no desire to trade –  Intent to get others to change their orders •  Example: –  Trader wants to fill large sell order –  Puts large buy order on the bid •  Fools other traders into thinking price going up, and they raise their bids and offers. •  Trader sells 29 The ugly: Excess cancellaKons •  Fire lots of orders into market and quickly cancel them. •  MoKve? –  Mixed strategy from game theory •  Keep ‘em guessing what you are up to. –  Bad sooware in “race condiKon” (infinite loop) –  Pinging markets to be first to catch new orders coming in? –  TesKng system bandwidth? –  IntenKonally slow down other traders? 30 Need for speed •  Most of these strategies are low-­‐tech and easily copied. •  In order to be successful, one has to be faster than the next compeKtor. •  Ooen a race to trade –  If you lose, it doesn’t ma_er whether you lose by a minute or a microsecond. You sKll lose. 31 What do the data say about market structure? James J. Angel @GUFinProf 32 Trade sizes have dropped. NYSE-­‐listed Average Shares per Trade Consolidated 1,200 1,000 800 600 400 200 0 Oct-­‐03 Feb-­‐05 Jun-­‐06 Nov-­‐07 Mar-­‐09 James J. Angel @GUFinProf Aug-­‐10 Dec-­‐11 May-­‐13 33 Quote traffic has exploded. Quotes per minute per security 800 700 600 500 400 300 200 100 -­‐ Jul-­‐98 Apr-­‐01 Jan-­‐04 Oct-­‐06 James J. Angel @GUFinProf Jul-­‐09 Apr-­‐12 34 Quote-­‐to-­‐trade raKo has also exploded. Quote-­‐to-­‐Trade Ra?o 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 -­‐ Jul-­‐98 Apr-­‐01 Jan-­‐04 Oct-­‐06 James J. Angel @GUFinProf Jul-­‐09 Apr-­‐12 35 Quoted depth has gone up. James J. Angel @GUFinProf 36 Intraday volaKlity has fallen Intraday (high/low) vola?lity 1.50 1.45 1.40 1.35 1.30 median_hilo 1.25 hilo_95 hilo_99 1.20 1.15 1.10 1.05 1.00 Jan-­‐90 Oct-­‐92 Jul-­‐95 Apr-­‐98 Dec-­‐00 Sep-­‐03 Jun-­‐06 James J. Angel @GUFinProf Mar-­‐09 Dec-­‐11 37 NYSE market share has fallen NYSE-­‐listed market shares 90.0% 80.0% 70.0% NYSE 60.0% NYSE-­‐Arca 50.0% NasdaqOMX Group BATS 40.0% Direct Edge Other 30.0% Total 20.0% 10.0% 0.0% Apr-­‐04 Aug-­‐05 Dec-­‐06 May-­‐08 Sep-­‐09 Feb-­‐11 James J. Angel @GUFinProf Jun-­‐12 38 InsKtuKonal trading costs have fallen. Average Transac?on Cost Es?mate for 1M Shares in a $30 Stock 160 140 120 100 Basis 80 Points 60 40 20 0 James J. Angel @GUFinProf 39 Conclusion •  Markets are be_er than before. –  But they are different! –  More complex structure provides more flexibility for insKtuKonal traders. •  But traders need to understand the new world order. •  Some noise is commercially based, but… •  Markets are not perfect –  Bad guys sKll try to game the system. –  Technological stability is a serious issue. –  Improvements can sKll be made •  Should be carefully tested first . James J. Angel @GUFinProf 40