The Impacts of Automa0on and High Frequency Trading on Market Quality Robert Litzenberger Jeff Castura and Richard Gorelick Introduc0on •  In recent decades, U.S. equity markets evolved from manual trading with limited compe00on to highly automated and compe00ve markets •  NASDAQ stocks changed earlier (1997); NYSE later (2006) •  Overall market quality improved; bid-­‐ask spreads narrowed, liquidity increased, transitory price impacts decreased •  Greater improvement in NYSE stocks than NASDAQ stocks since 2006 suggest causal link between market structure changes and market quality •  Studies of exchange data sets show HFT firms contributed to narrowing bid-­‐ask spreads, increasing liquidity and reducing intra-­‐day transitory pricing errors and intra-­‐day vola0lity Pre-­‐Electronic Trading & Ins0tu0onal Investors •  1960’s -­‐ NYSE’s exclusive specialists, floor brokers, floor traders, fixed commissions, and minimum 1/8th 0cks put under pressure by the growing size and turnover rates of ins0tu0onal trades •  Late 1960’s -­‐ SEC ins0tu0onal study in late 1960’s found large price impact of sales of large blocks •  To mi0gate the price impact, floor brokers worked large orders, but their physical presence revealed the presence of these orders to floor traders and specialists •  As an alterna0ve to gradually implemen0ng a large buy or sell program on the exchange floor, block posi0oners nego0ated prices off the floor and then crossed the block on the exchange Block Trading •  Price reflected a discount (sell order) or premium (buy order) and the ins0tu0on paid double commission for using the services of a block posi0oner •  Kraus & Stoll (1972) dichotomized price impact of a block trade into a permanent component (“Informa0on Effect”) and a transitory component (“Distribu0on Effect”) •  The immediate price impact averaged 1.14% followed by a subsequent price recovery of 0.71% by the end of the day implying a transi0onal “distribu0on effect” of 0.71% plus one commission and an “informa0on effect” of 0.43% Don Weeden – Financial Prophet In a 1969 leler to the SEC Don Weeden commented: “With today’s electronic miracles available to the industry, all market makers whereever located could be combined into a central, interrelated market for fast and efficient access by investors to all of its segements. The true central marketplace demands access to all available pools of posi?oning capital for maximum liquidity.” Almost four decades later, “electronic miracles” have indeed 0ed together numerous sources of liquidity from diverse loca0ons to the benefit of investors. NYSE Listed Stocks •  Early 2005, ATS’s accounted for over half the trading volume in NASDAQ stocks, but only a small frac0on of NYSE-­‐listed stocks •  Compe00on from ATSs was disadvantaged by an an0quated “trade through” rule that required that all orders in NYSE listed shares be exposed to the floor for a 30-­‐second period for price improvement •  Negated advantages of ATS trading; i.e., speed, anonymity, and certainty of execu0on Leveling the Playing Field •  By the mid-­‐1990’s, most NYSE and AMEX stocks s0ll traded manually on exchange floors; NASDAQ stocks traded electronically but s0ll entered manually through keyboards •  Through the SEC Order Handling Rules (1996-­‐1997) NASDAQ market makers were effec0vely required to include price quotes from ECN’s into their quota0ons •  2005 SEC Reg NMS approved (fully implemented 2007) – priority given to the na0onal best bid or offer (NBBO) available immediately and automa0cally Characteris0cs of HFT •  HFT “market making” uses res0ng orders to quote two-­‐sided prices and rapidly adjusts quotes in response to market condi0ons •  HFT market making earns a gross profit from bid-­‐ask spreads, which is mi0gated by adverse selec0on •  HFT direc0onal trading uses marketable orders and includes mean-­‐rever0ng and momentum strategies •  In prac0ce, the dis0nc0ons between market making and direc0onal trading are less clear Bid/Ask Spreads •  Smaller spreads are cet. par. evidence of a beler cost structure for investors. Figure 1 shows a decline in the effec0ve spread on NYSE from 1993 thru 2002 •  These changes are not alributable to either automated order matching or HFT and are due to the reduc0on in 0ck increments since the NYSE’s automated execu0on and quo0ng systems came later •  Figure 2 shows the average effec0ve spreads for NYSE stock halved from 2006-­‐2009 while the average effec0ve spread for NASDAQ stocks remained the same. This decline coincided with automated order matching and gradual improvements in execu0on 0mes. The upward spike in spreads in 2007-­‐2008 is related to the high vola0lity during the liquidity/credit crisis Value-­‐Weighted Daily Average Effec0ve Spread NYSE 1993-­‐2002 Figure 1: Effective spreads, NYSE-listed stocks between 1993 and 2002. Original source
Chordia, Roll & Subrahmanyam (2008). Reproduced from Angel, Harris & Spatt (2010)
85
0.05
Effec%ve Spreads between 2006-­‐2009, NYSE-­‐listen and NASDAQ stocks. Taken from Rule 605 reports from Thomson, Market orders, 100-­‐999 shares. ●
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Figure 2: Effective Spreads between 2006-2009, NYSE-listen and NASDAQ stocks. Taken
from Rule 605 reports from Thomson, Market orders, 100-999 shares.
Direct Causal Link Between HFT and Narrowing of Spreads •  Chi-­‐X, the electronic pan-­‐European stock market began trading Dutch stocks in 2007 about one year prior to trading Belgium stocks, while both traded on Euronext. Menkveld found that the ac0vi0es of a single HFT market maker resulted in bid-­‐ask spreads in Dutch stocks falling by 50% rela0ve to Belgium stocks •  Using a data set provided by NASDAQ that flagged HFT trades (60 NASDAQ and 60 NYSE listed stocks), Brogaard found that HFT matched or improved the non-­‐HFT inside prices 83% of the 0me and strictly improved non-­‐HFT inside prices 14% of the 0me for large cap stocks, while the numbers are 51% and 22%, respec0vely, for small cap stocks Posted Liquidity •  Credit Suisse (2010) showed that the median displayed depth at the NBBO for the Dow Jones stocks increased by 75% between 2006 and 2010. •  Angel et. al. (2011) showed median displayed depth at NBBO increased 125% for S&P 500 stocks and 30% for Russell 2000 •  They also showed median depth within $0.06 band is more than 10x greater than available at the NBBO •  Marketable orders for most S&P 500 stocks much greater than 10,000 shares can be executed within an 1/8th •  The 1969 SEC ins0tu0onal investor study considered 10,000 shares a large block whose sale required the services of an “upstairs” block posi0oner to arrange off the exchange floor Original source Knight Capital Group. Taken from Angel, Harris & SpaR (2010) Median displayed depth within six cents of NBBO between 2003 and 2009. Original source Knight Capital Group. Taken from Angel, Harris & SpaR (2010) Direct Evidence of HFT Liquidity Provision •  Using the NASDAQ dataset Brogaard found that HFT res0ng orders par0cipated in 41.1% of dollar volume traded, while their marketable orders par0cipated in 42% of dollar volume traded •  Addi0onally, Brogaard showed that HFT increased the inside dollar value by about 50% on average Trade Sizes •  Angel, Harris and Spal show that mean trade size has decreased from 700 shares in 2004 to about 300 shares in 2009 •  Ins0tu0onal investors frequently use computer algorithms provided by brokers to break up large trades in order to gradually acquiring or liquidate large posi0ons giving more investors the opportunity to compete and mi0gate the transitory price impact •  These algorithms are analogous to floor brokers of by gone days working large orders on the exchange floor Market Efficiency •  In a classic contribu0on to efficient market theory, Samuelson [1965] showed that “properly an0cipated” prices should be indis0nguishable from a random process •  Mean reversion as a result of trading ac0vity represents an inefficiency •  Under a random walk, an es0mate of variance per unit of 0me would have the same expected value whether es0mated using a 0me series of 1-­‐s returns or of 10-­‐s returns •  Market orders exceeding the inside size cause transitory price errors. Subsequent reversals would result in the ra0o of variance per unit of 0me based on 10-­‐s to those based on 1-­‐s to be less than unity •  For large NYSE stocks the decrease in the percentage of stock having variance ra0os significantly below 1.0 approaches 5% which would be expected in an efficient market 10 second / 1 second Variance Ra%o: Russell 1000 1.0
10 second / 1 second Variance Ratio: Russell 1000
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Figure 9: Average 10-second to 1-second variance ratio for Russell 1000 stocks, NYSE-listed
and NASDAQ stocks between 2006 and 2011
Average 10-­‐second to 1-­‐second variance ra%o for Russell 1000 stocks, NYSE-­‐listed and NASDAQ stocks between 2006 and 2011 10 second / 1 second Variance Ra%o: Russell 2000 1.0
10 second / 1 second Variance Ratio: Russell 2000
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Figure 10: Average 10-second to 1-second variance ratio for Russell 2000 stocks, NYSElisted and NASDAQ stocks between 2006 and 2011
Average 10-­‐second to 1-­‐second variance ra%o for Russell 2000 stocks, NYSE-­‐ listed and NASDAQ stocks between 2006 and 2011 Rela0on of Permanent and Transitory Price Movements to HFT •  Hendershol and Riordan [2012] used the NASDAQ and BATS data sets to examine the link between HFT and transitory and permanent price components •  Changes in efficient price, mi,t as a linear func0on of unan0cipated HFT limit order flow and unan0cipated marketable order flow. Use of unan0cipated net order flow removes the auto-­‐correlated component of net order flow, consistent with mar0ngale permanent price process •  The transitory pricing error, si.t, is a linear func0on of net HFT marketable order flow, Hi,tinit, and the net HFT res0ng order flow, Hi.tPass. r the first week of each quarter in 2008 and 2009 are prese
elow.
Hendershol and Riordan State Space Model te space model is reproduced below for reference:
!!,! = !!,! + !!,!
!!,! = !!,!!! + !!,!
!"#$
!"##
!!,! = !!!"#$ !!,!
+ !!!"## !!,!
+ !!,!
!"#$
!"##
!!,! = !!!,!!! + !!!"#$ !!,!
+ !!!"## !!,!
+ !!,!
del can be more clearly understood by restating it in a redu
!"#$
!"##
!!,! = !!!"#$ !!,!
+ !!!"## !!,!
+ !!,!
Hendershol and Riordan ! = !!
+! !
+!
!
+!
Reduced Form Equa0on !,!
!,!!!
!"#$
!
!"#$
!,!
!"##
!
!"##
!,!
!,!
eir model can be more clearly understood by restating it in a reduced
m. The change in stock price may be expressed as:
!!!,! = !!,! − !!,!!! = !!,! + !!!,!
!"#$
!"## !!"## + !!"#$ !!!"#$ + !!"## !!!"## + ! !!
= ! !!"#$
!
+
!
!,!
!,!
!,!
!,!
!
!
!
!
!
!,!−1 + !!,!
!!,! = −!! !!,!!! + !!!,! + !!,!
te that !!,! may be viewed as an error. The components !! !!,!!! , !!!,! and
are uncorrelated with !!!,! , have zero autocorrelations and have zero
ial correlations with each other; therefore !!,! is not autocorrelated. Thus,
Hendershol and Riordan Empirical Results Profitability •  Using the NASDAQ data set, Brogaard found HFT trades earned an average of 0.72 bps •  Extrapola0ng to the en0re market, he es0mated gross annual trading revenues for HFT firms as a group to be $2.8 billion in 2008 and 2009 •  Using the same data set, Hendershol and Riordan found “HFT marketable orders’ informa?onal advantage is sufficient to overcome the bid-­‐ask spread and trading fees to generate posi?ve trading revenues” and that HFT res0ng orders “result in pos?ve revenues as the costs associated with adverse selec?on are smaller than the bid-­‐ask spread and liquidity rebates” •  Brogaard reported HFT margins in 2008 and 2009 were about 1/7th that of specialists immediately aver decimaliza0on Figure 11: Peak 1-minute messages per second for UQDF. Source: Financial Information
Forum (www.fif.com)
Peak 1-­‐minute messages per second for UQDF. Source: Financial Informa%on Forum (www.fif.com) Quoted spreads of Russell 1000 and Russell 2000 stocks, NYSE-­‐
listed and NASDAQ stocks between 2006 and 2011 VIX-­‐adjusted Quoted spreads of Russell 1000 and Russell 2000 stocks, NYSE-­‐ listed between 2006 and 2011 NYSE−listed Russell 1000
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Figure 4: VIX-adjusted Quoted spreads of Russell 1000 and Russell 2000 stocks, NYSElisted between 2006 and 2011
Figure 6: Median displayed depth within six cents of NBBO between 2003 and 2009.
Original source Knight Capital Group. Taken from Angel, Harris & Spatt (2010)
Available Liquidity: Russell 1000 NYSE−listed
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Available Liquidity: Russell 1000
Figure 7: Average dollar value posted at the inside for Russell 1000 stocks, NYSE-listed and
NASDAQ stocks between 2006 and 2011
Average dollar value posted at the inside for Russell 1000 stocks, NYSE-­‐listed and NASDAQ stocks between 2006 and 2011 Available Liquidity: Russell 2000 Available Liquidity: Russell 2000
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Figure 8: Average dollar value posted at the inside for Russell 2000 stocks, NYSE-listed and
NASDAQ between 2006 and 2011
Average dollar value posted at the inside for Russell 2000 stocks, NYSE-­‐listed and NASDAQ between 2006 and 2011