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High-Frequency Trading: Empirics
Jakub Rojcek
University of Zurich and Swiss Finance Institute
November 3, 2016University of Sankt Gallen
Jakub Rojcek High-Frequency Trading: Empirics 1 / 58
Outline
1 Introduction to Empirics of HFTObjectivesAlgorithmic Trading and High-Frequency TradingLiterature Overview
2 Market Quality and High-Frequency Trading
3 Evaluating the Impact of Regulations
4 Strategies of High-Frequency Traders
5 Conclusion and Outlook
Jakub Rojcek High-Frequency Trading: Empirics 2 / 58
What Can We Face while Studying HFT?
Figure: Bid size of Pfizer stock on October 10, 2011.
Jakub Rojcek High-Frequency Trading: Empirics 3 / 58
Objectives
1 Learn how to measure high-frequency activity.
2 Explore data sources.
3 Answer pressing questions about high-frequency trading.
4 Evaluate impact of regulations.
Jakub Rojcek High-Frequency Trading: Empirics 4 / 58
Algorithmic Trading vs High-Frequency Trading
Figure: Source: Johnson (2010).Jakub Rojcek High-Frequency Trading: Empirics 5 / 58
Some of the Streams of Empirical Inquiry
Impact on Market Quality
Impact of Regulations Strategies of HFTs
Optimal Execution/LOB EmpiricsHF Econometrics
Jakub Rojcek High-Frequency Trading: Empirics 6 / 58
Outline
1 Introduction to Empirics of HFT
2 Market Quality and High-Frequency TradingHasbrouck and Saar (JoFM 2013)Boehmer, Fong and Wu (2015)Brogaard, Hendershott and Riordan (RFS, 2014)Lin Tong (2015)
3 Evaluating the Impact of Regulations
4 Strategies of High-Frequency Traders
5 Conclusion and Outlook
Jakub Rojcek High-Frequency Trading: Empirics 7 / 58
Low-latency trading
Joel Hasbrouck and Gideon Saar
Journal of Financial Markets 16 (2013) 646-679
Joel Hasbrouck and Gideon Saar Low-latency trading 8 / 58
Measuring Low Latency Trading: Strategic RunsHasbrouck and Saar proxy HFT/low latency activity using Strategic Runs, which arelinked submissions, cancellations, and executions that are likely to be parts of a dynamicalgorithmic strategy.They use only longer runs–runs of 10 or more messages–to construct the measure.They construct the measure of RunsInProcess, as the time-weighted average of thenumber of strategic runs of 10 messages or more the stock experiences in an interval.
The measure is highly correlated with the NASDAQ measure of HFT trading (0.7-0.8).
Joel Hasbrouck and Gideon Saar Low-latency trading 9 / 58
Measuring Low Latency Trading: Strategic RunsThey estimate the following model using GMM, Driscoll-Kraay standard errors.
Moreover, controlling for index return & volatility and time of the day dummies.
MktQualityi,t = α1RunsInProcessi,t + α2TradingIntensityi,t + εi,t . (1)
Market Quality Measure α1 α2
Spread -0.214 (<0.001) 0.096 (<0.001)EffSprd -0.170 (<0.001) 0.103 (<0.001)NearDepth 0.246 (<0.001) -0.104 (<0.001)HighLow -0.056 (<0.001) 0.322 (<0.001)
There might be a problem of simultaneity or endogeneity in the above model.
They use RunsNotINDi,t as an instrument. Average of RunsInProcessi,t excluding theINDividual stock and the same INDustry.
2 SLS results are as below.
Market Quality Measure α1 α2
Spread -0.682 (<0.001) 0.104 (<0.001)EffSprd -0.493 (<0.001) 0.109 (<0.001)NearDepth 0.460 (<0.001) -0.107 (<0.001)HighLow -0.437 (<0.001) 0.329 (<0.001)
Joel Hasbrouck and Gideon Saar Low-latency trading 10 / 58
International Evidence on Algorithmic Trading
Boehmer, Fong and Wu
2015
Boehmer, Fong and Wu International Evidence on Algorithmic Trading 11 / 58
Study AT Proxy on 42 Equity Markets Around the World
Main data source is Thomson Reuters Tick History (TRTH) database, which containsintraday trades and quotes data for many markets around the world. They combine thesedata with TAQ, and merge the result with firm-level data in Datastream and Center forResearch and Security Prices (CRSP) database. Data on buy-side transaction costs comefrom the Ancerno database.
In construction of their AT proxy, they follow Hendershott, Jones, and Menkveld (JF
2011): algo tradi,t = − $ Volumei,t# of Messagesi,t
.
Their model is
MktQualityi,t = αi,t + γt + βalgo tradi,t−1 + δXi,t−1 + εi,t . (2)
AT proxy could again suffers from simultaneity/endogeneity, they use a co-locationdummy as intrumental variable in 2SLS estimation.
They document decreased spreads and increased efficiency, in line with the Swedish
co-location study of Brogaard, Hagstromer, Norden and Riordan (RFS, 2015).
Boehmer, Fong and Wu International Evidence on Algorithmic Trading 12 / 58
List of Co-location Events across Countries
Boehmer, Fong and Wu International Evidence on Algorithmic Trading 13 / 58
Impact of AT on Market Quality Using IV
Boehmer, Fong and Wu International Evidence on Algorithmic Trading 14 / 58
High-frequency trading and price discovery
Brogaard, Hendershott and Riordan
Review of Financial Studies, 27(8), 2267-2306, Aug 2014
Brogaard, Hendershott and Riordan High-frequency trading and price discovery 15 / 58
NASDAQ HFT Dataset vs TAQ DatasetHFTD is a sum of {HH,HN} types, HFT S of {HH,NH}.
Brogaard, Hendershott and Riordan High-frequency trading and price discovery 16 / 58
State Space Model of HFT and Prices
The state space model assumes that the price can be decomposed into permanent andtransitory component (Menkveld, Koopman and Lucas 2007):
pi,t = mi,t + si,t . (3)
The permanent (efficient) component follows a martingale:
mi,t = mi,t−1 + wi,t . (4)
Impact of HFT demanding liquidity is captured by surprises from autoregression HFTD
:
wi,t = κDi,HFT HFTD
i,t + κDi,nHFT nHFTD
i,t + µi,t . (5)
Transitory component is assumed to be stationary and modelled as:
si,t = φsi,t−1 + ψDi,HFTHFT
Di,t + ψD
i,nHFTnHFTDi,t + νi,t . (6)
Assumption: innovations are uncorrelated— cov(µt , νt) = 0.
Brogaard, Hendershott and Riordan High-frequency trading and price discovery 17 / 58
Demanding Liquidity in the State Space Model
Brogaard, Hendershott and Riordan High-frequency trading and price discovery 18 / 58
Supplying Liquidity in the State Space Model
Brogaard, Hendershott and Riordan High-frequency trading and price discovery 19 / 58
HFT Trading Around Macro News Announcements
Brogaard, Hendershott and Riordan High-frequency trading and price discovery 20 / 58
Summary and Related Literature
Overall, HFT facilitate price discovery by trading in the direction of permanent pricechanges,
and in the opposite direction of transitory pricing errors.
However, the interpretation of the results is not straightforward, which lead many to study,whether HFTs lean against the wind or go with the wind.
Boulatov, Bernhardt and Larionov (2016) study Nash equilibria when multiple traders seekto minimize transaction cost when trading continuously in a fixed time period. They findthat traders who have a small position to trade, either lean against the orders oflarge-position traders or prey on them. Both types of behavior can occur in equilibrium.The large-position traders’ optimal response to preying is to delay trading.
Korajczyk and Murphy (2016), and van Kervel and Menkveld (2016) find that HFTs leanagainst the order in the first hour, but turn around and trade with the order in the case ofmulti-hour executions.
Breckenfelder (2013) finds that increased competition leads to increase in liquidityconsumption by HFTs (going with the wind).
Tong (2015) find by combining data on institutional trades and HFT trades, that HFTincreases traditional institutional investors’ trading costs measured by execution shortfall.
Brogaard, Hendershott and Riordan High-frequency trading and price discovery 21 / 58
A Blessing or a Curse? The Impact of High FrequencyTrading on Institutional Investors
Lin Tong
2015
Lin Tong A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors 22 / 58
Are Institutional Investors Suffering Higher ExecutionCosts?
Uses the standard NASDAQ HFT dataset to measures the HFT intensity.
The measure of HFT daily activity on stock i, denoted as HFT Intensityi,t , is defined asthe aggregate HFT volume for stock i on day t divided by the stock’s average dailytrading volume in the past 30 days.
The measure of institutional trading costs is constructed from the Ancerno proprietarydataset. The execution shortfall is measured as
P1 − P0
P0× D, (7)
where P1 is the value-weighted execution price of the ticket, P0 is the price at the timewhen the broker receives the ticket, and D is a dummy variable that equals 1 for a buytrade and -1 for a sell trade.
Lin Tong A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors 23 / 58
HFT Activity Coincides with Increased Execution Shortfall
Lin Tong A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors 24 / 58
Institutional ES does not Granger-cause HFT Activity
Lin Tong A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors 25 / 58
Outline
1 Introduction to Empirics of HFT
2 Market Quality and High-Frequency Trading
3 Evaluating the Impact of RegulationsColliard and Hoffman (JF, 2016)More Regulation Event Studies
4 Strategies of High-Frequency Traders
5 Conclusion and Outlook
Lin Tong A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors 26 / 58
Financial Transaction Taxes, Market Composition, andLiquidity
Jean-Edouard Colliard and Peter Hoffmann
Forthcoming in The Journal of Finance
Jean-Edouard Colliard and Peter Hoffmann Financial Transaction Taxes, Market Composition, and Liquidity 27 / 58
On August 12, 2012, France Introduced FTT
On August 1st, 2012, France introduced an FTT of 20 bps on stock purchases. This taxapplies to shares of all listed companies incorporated in France with a marketcapitalization above one billion euros.
The tax is payable on daily net position changes (i.e., ownership transfers), which impliesthat pure intraday trading is de facto exempted.
Simultaneously, the government introduced a tax of 1 bp on the notional amount ofmodified or cancelled messages by HFTs exceeding an order-to-trade ratio of 5:1. Unlikethe FTT, this tax applies to trading in all French stocks. However, it is only levered onHFTs residing in France, which excludes all major HFT firms. Moreover, message trafficdue to market-making is exempt.
They utilize difference-in-difference approach summarized by the regression
yi,t = αi + γt + βAugDAugi,t + βSep/OctD
Sep/Octi,t + εi,t . (8)
They use high-frequency data of Euronext French stocks for treated group andcomparable Euronext non-French stocks for control group.
Jean-Edouard Colliard and Peter Hoffmann Financial Transaction Taxes, Market Composition, and Liquidity 28 / 58
Causal Impact of the FTT on all Stocks.
Jean-Edouard Colliard and Peter Hoffmann Financial Transaction Taxes, Market Composition, and Liquidity 29 / 58
Causal Impact of the FTT on all Stocks II.
Jean-Edouard Colliard and Peter Hoffmann Financial Transaction Taxes, Market Composition, and Liquidity 30 / 58
Causal Impact of the FTT on all Stocks. III
Jean-Edouard Colliard and Peter Hoffmann Financial Transaction Taxes, Market Composition, and Liquidity 31 / 58
Causal Impact of the FTT on Trading Volume and OrderFlow Composition
HFTs are strongly impacted by the FTT despite the effective exemption of intradaytrading.
Jean-Edouard Colliard and Peter Hoffmann Financial Transaction Taxes, Market Composition, and Liquidity 32 / 58
Related Literature on the Impact of RegulationsThe empirical evalution of the impact of transaction tax includes the following:
French FTT: Meyer, Wagener, and Weinhardt (2015) document a reduction in thenumber of quote and price updates by liquidity suppliers and a decline in top order bookdepth. Gomber, Haferkorn, and Zimmermann (2015) report a drop in both liquiditydemand and supply, an increase in spreads, and a decline in top order book depth.Becchetti, Ferrari, and Trenta (2014) find a reduction in turnover and intraday volatility,but mixed effects on liquidity.
Umlauf (1993) investigates the impact of the introduction of the Swedish transaction taxand documents a large shift in trading volume from Sweden to the U.K.
Cancellation fees
Malinova, Park, and Riordan (2013), find that following their introduction on the TorontoStock Exchange, quoting activity decreased by 30% and spreads increased by 9%.
Friederich and Payne (2015) study the introduction of excessive order-to-trade ratio fee onApril 2nd, 2012, by the Milan Borsa. They observe decreased depth and worsened pricediscovery.
Make/Take fees
Malinova and Park (2015) use a change in Toronto Stock Exchange fees to investigate theimpact of make-take fees on market quality. They find that rebates improve quotedspreads, decrease adverse selection costs, and increase the aggressiveness of retail traders.They also observe that the effective spread plus total fee retained by the exchangeremained constant, confirming the conjecture of Colliard and Foucault (2012) that onlytotal fees (spread plus exchange fees) affect liquidity and trading volume.
Jean-Edouard Colliard and Peter Hoffmann Financial Transaction Taxes, Market Composition, and Liquidity 33 / 58
Outline
1 Introduction to Empirics of HFT
2 Market Quality and High-Frequency Trading
3 Evaluating the Impact of Regulations
4 Strategies of High-Frequency TradersBaron, Brogaard, Hagstromer and Kirilenko (2016)Gencay, Mahmoodzadeh, Rojcek and Tseng (2016)
5 Conclusion and Outlook
Jean-Edouard Colliard and Peter Hoffmann Financial Transaction Taxes, Market Composition, and Liquidity 34 / 58
Risk and Return in High-Frequency Trading
Baron, Brogaard, Hagstromer and Kirilenko
2016
Baron, Brogaard, Hagstromer and Kirilenko Risk and Return in High-Frequency Trading 35 / 58
Measuring Latency of a HFT Based on his ActionsFor each HFT in each month, they record all cases where a passive trade is followed by anaggressive trade by the same HFT, in the same stock and at the same trading venue,within one second. The time-stamp difference between the two trades of each case formsan empirical distribution of response times. To capture the fastest possible reaction time,they define Decision Latency as the 0.1% quantile of the aforementioned distribution.
Baron, Brogaard, Hagstromer and Kirilenko Risk and Return in High-Frequency Trading 36 / 58
Profitability Statistics across HFTs
Baron, Brogaard, Hagstromer and Kirilenko Risk and Return in High-Frequency Trading 37 / 58
Impact of Latency on ProfitabilityThey measure influence of the performance in the following model:
Pi,t = αt+β1 log Latencyi,t+β21top 1i,t +β31top 2−3
i,t +β41top 4−5i,t +γ′Ci,t+month−FEs+εi,t .
(9)
Baron, Brogaard, Hagstromer and Kirilenko Risk and Return in High-Frequency Trading 38 / 58
Price Impact and Bursts In Liquidity Provision
Gencay, Mahmoodzadeh, Rojcek and Tseng
2016
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 39 / 58
Market Activity Exhibits Bursts
The quotation arrival process directly reflects market makers’ activity.
Number of quotes for Alcoa between 10:31:15 and 10:32:15 on October 10, 2011.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 40 / 58
Research Questions
We analyze the periods during which the arrival intensity of quotes is largerelative to what would be normal in a given interval. We call such periodsbursts in quotation activity.
We investigate the functioning of markets and price formation during thesebursts.
What is the impact of bursts in quotation activity on market quality?
Is there a change in the informational relationship between marketmaker and market taker? We will examine this from two perspectives.
1 Price impact of trades – ex ante adverse selection2 Realized spread based empirical measure – ex post adverse selection
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 41 / 58
Main Findings
On market characteristics during bursts:
I Quoted spread, effective spread, and volatility increase.I Size of incoming orders decreases.
On informational relationship between maker and taker during bursts:
I The informational dichotomy breaks down. Market makers no longerpassively impound information from order flow into quotes.
I Ex ante adverse selection (price impact) rises significantly, on averagefrom 0.002% to 0.011% per trade.
I Ex post adverse selection also increases.
Adverse selection is in fact asymmetric, depending on whether the marketmaker occupies the same side of the LOB as the burst.
I On the burst side, there is negative price impact.I On the opposite side, the severity of adverse selection rises significantly.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 42 / 58
Burst Detection
”one man’s noise is another man’s signal”
We detect bursts based on the following market activityvariables:
Number of trades
Number of quotes
Number of quotes on different sides of the book
These quantities are approximately exponentiallydistributed (see also Zhu and Shasha 2003, 2005. Vlachos et al 2004, 2008.)
1 For each 15 min. interval, we estimate the mean of the distribution µ.
2 We record a burst for each interval (100ms, 1s, 5s), in which the variable exceedsa threshold x = −µ log(P), where P is the tail probability (e.g. P = 99.9%).
Our definition of bursts coincides with that of unified outliers in Knorr (1997).
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 43 / 58
Example: Alcoa on October 10, 2011, 1 Second Frequency
Quotes Trades
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 44 / 58
Market Behavior Around Bursts: Quoted Spread
100ms Interval 1s Interval 5s Interval
The black line represents the average quoted spread across all 100ms (1s, 5s)intervals before, during and after bursts, as well as across all stocks in our sample.
The initial value was normalized to 100.
The shaded area represents one standard deviation from the mean.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 45 / 58
Market Behavior Around Bursts: Effective Spread
100ms Interval 1s Interval 5s Interval
The black line represents the average effective spread across all 100ms (1s, 5s)intervals before, during and after bursts, as well as across all stocks in our sample.
The initial value was normalized to 100.
The shaded area represents one standard deviation from the mean.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 46 / 58
Market Behavior Around Bursts: Return Volatility
100ms Interval 1s Interval 5s Interval
The black line represents the average volatility across all 100ms (1s, 5s) intervalsbefore, during and after bursts, as well as across all stocks in our sample.
The initial value was normalized to 100.
The shaded area represents one standard deviation from the mean.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 47 / 58
Market Behavior Around Bursts: Order Size
100ms Interval 1s Interval 5s Interval
The black line represents the average order size across all 100ms (1s, 5s) intervalsbefore, during and after bursts, as well as across all stocks in our sample.
The initial value was normalized to 100.
The shaded area represents one standard deviation from the mean.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 48 / 58
Estimating Permanent and Temporary Price Impact
We use the price impact methodology of Hasbrouck (1991).
Let
rt denote the change in the market maker’s estimate of the fundamental value.
qt be the signed order flow (-1 if more trades at bid, +1 if more trades at ask and0 if equally many or no trades.)
The VAR system is
rt = α1rt−1 + α2rt−2 + · · · + β0qt + β1qt−1 + · · · + εt︸︷︷︸Market maker’s public information
, (10)
qt = γ1rt−1 + γ2rt−2 + · · · + δ1qt−1 + δ2qt−2 + · · · + νt︸︷︷︸Market taker’s private information
. (11)
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 49 / 58
Price Impact is Higher During Bursts
We add dummy variables BQt and BT
t to identify periods with bursts in quotes and trades.
rt =10∑i=1
αi rt−i +10∑i=1
βiqt−i + β0qt + φQBQt qt + φTBT
t qt + φQ&TBQt B
Tt qt + ε′t .
α1 β0 φQ φT φQ&T R2-Adj R2-Adjwith bursts w/o bursts
AA 0.925 0.002 0.046 0.007 -0.012 0.585 0.489BAC 0.988 0.001 0.058 0.007 -0.009 0.593 0.510GE 0.917 0.001 0.028 0.007 -0.007 0.567 0.484IBM 0.247 0.003 0.006 0.005 -0.001 0.416 0.416JNJ 0.445 0.002 0.004 0.004 -0.001 0.411 0.397JPM 0.875 0.006 0.009 0.011 -0.002 0.502 0.482MRK 0.851 0.002 0.012 0.007 -0.004 0.518 0.465PFE 0.827 0.000 0.023 0.005 -0.001 0.541 0.461PG 0.619 0.002 0.005 0.005 -0.001 0.403 0.381PM 0.336 0.002 0.004 0.006 0.000 0.407 0.407T 0.742 0.001 0.015 0.006 -0.006 0.484 0.430VZ 0.846 0.001 0.009 0.007 -0.003 0.505 0.437WFC 0.922 0.005 0.013 0.013 -0.008 0.551 0.509WMT 0.705 0.002 0.007 0.005 -0.002 0.462 0.430XOM 0.463 0.002 0.004 0.006 0.002 0.459 0.441
Median coeff 0.827 0.002 0.009 0.006 -0.002 0.502 0.441Median p-value 0.000 0.000 0.000 0.000 0.125
Median daily # of events 419.3 706.1 318.0
% of significant firm-days at 5 % 99.67% 93.00% 81.67% 91.67% 30.00%
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 50 / 58
Breakdown of Informational Dichotomy
Recall the price and order flow equations:
rt = α1rt−1 + α2rt−2 + · · ·+ β0qt + β1qt−1 + · · ·+ εt︸︷︷︸Market maker’s public information
qt = γ1rt−1 + γ2rt−2 + · · ·+ δ1qt−1 + δ2qt−2 + · · ·+ νt︸︷︷︸Market taker’s private information
If the market maker can predict the order flow, εt and νt will be positivelycorrelated.
Our hypothesis is that {εt , νt} are correlated during, and only during, burstsin quotes. We test the correlation between εt and νt .
In addition to correlations during burst, {εt , νt}, and non-burst periods,{ε′t , νt}, we estimate the linear specification
νt = ηQBQt εt + η¬Q(1− BQ
t )ε′t + ν′t .
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 51 / 58
Information Correlation During Bursts in Quotes
ρQ ρ¬Q ηQ η¬Q ρAll
AA 0.487 -0.002 7.779 -0.130 -0.002BAC 0.431 0.007 4.230 0.508 0.005GE 0.514 -0.002 12.735 -0.237 -0.002IBM 0.193 0.000 7.789 0.000 0.000JNJ 0.323 0.000 20.476 -0.021 0.000JPM 0.413 0.000 13.601 -0.037 0.000MRK 0.516 -0.002 22.172 -0.247 -0.002PFE 0.478 0.000 13.568 -0.087 -0.001PG 0.339 0.000 22.921 -0.049 0.000PM 0.022 0.000 0.526 -0.001 0.000T 0.478 -0.001 21.501 -0.202 -0.001VZ 0.417 -0.003 21.597 -0.332 -0.002WFC 0.493 -0.001 15.465 -0.060 0.000WMT 0.410 0.000 26.169 -0.017 0.000XOM 0.206 0.000 15.633 -0.046 0.000
Median coeff 0.417 0.000 15.465 -0.049 0.000Median p-value 0.000 0.862 0.000 0.871 0.884
Median daily # of events 101.3 20,789 101.3 20,789 20,890
% of significant firm-days at 5 % 100% 0% 100% 0% 0%
Market maker in fact predicts price movement/order flow during bursts in quotes.
Basic market measures show no sign that a burst is anticipated by the market.
Therefore bursts are market maker-initiated informational events.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 52 / 58
Adverse Selection and the Side Where the Burst Happens
We investigate the behavior of the adverse selection proxy (Bessembinder (2003))
ADt = ESt − RSt ,
where, letting Pt denote the transaction price and Mt the mid-quote,
RSt = 2qt(Pt −Mt+s)
is the realized spread (profit) of the market maker, and
ESt = 2qt(Pt −Mt).
is the effective spread measuring the price of immediacy and the adverse selectioncomponent.
ADt is the magnitude of the ex post loss due to adverse price movement pluseffective spread.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 53 / 58
Asymmetric Adverse Selection cont’d
Adverse selection
Realized spread
Effective spread
Burst at Ask Burst at Bid
Market makers on the opposite side of the burst charge higher cost ofimmediacy thus partially compensating for the negative realized spread.Despite this partial compensation, they suffer more severe adverse selection.Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 54 / 58
MMs Burst in the Direction of the Price ImpactTo the basic specification we add B
Q,samet and B
Q,oppt representing periods when a burst occurs on the side of the book where
the order flow arrives or on the opposite side, respectively;
rt =10∑i=1
αi rt−i +10∑i=1
βiqt−i + β0qt + φQ,sameBQ,samet qt + φQ,oppBQ,opp
t qt + φTBTt qt + ε′t .
α1 β0 φQ,same φQ,opp φT R2-Adj R2-Adjwith bursts w/o bursts
AA 0.929 0.002 -0.030 0.041 0.031 0.545 0.489BAC 0.988 0.001 -0.024 0.040 0.043 0.558 0.510GE 0.913 0.001 -0.025 0.030 0.018 0.534 0.484IBM 0.247 0.003 0.003 0.007 0.007 0.416 0.416JNJ 0.445 0.002 -0.006 0.008 0.005 0.413 0.397JPM 0.875 0.006 -0.008 0.010 0.013 0.504 0.482MRK 0.848 0.002 -0.014 0.011 0.009 0.507 0.465PFE 0.826 0.001 -0.019 0.024 0.017 0.519 0.461PG 0.619 0.002 -0.003 0.005 0.006 0.397 0.381PM 0.336 0.002 0.000 0.001 0.006 0.408 0.407T 0.742 0.001 -0.013 0.016 0.011 0.472 0.430VZ 0.845 0.001 -0.010 0.010 0.009 0.486 0.437WFC 0.920 0.005 -0.013 0.016 0.014 0.547 0.509WMT 0.704 0.002 -0.008 0.009 0.006 0.455 0.430XOM 0.446 0.002 0.002 0.002 0.006 0.455 0.441
Median coeff 0.826 0.002 -0.010 0.010 0.009 0.486 0.441Median p-value 0.000 0.000 0.002 0.000 0.000
% of significant firm-days at 5 % 99.0% 93.33% 67.33% 82.67% 96.0%Median daily number of events 24.8 60.7 318
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 55 / 58
Conclusion
We have shown that during bursts in quotes:
1 The price impact rises markedly from 0.002% to 0.011% per trade.It is higher than during bursts in trades.
2 Market makers do not passively update quotes based oninformation inferred from order flow.
3 Limit orders on the opposite side of the burst suffer adverseselection.
4 Price movement during bursts is likely not part of the normal priceformation process.
5 Whether bursts are sufficiently disruptive to warrant concern is aquestion of interest to regulators, given recent attempts by exchangesto curtail high frequency spamming.
Gencay, Mahmoodzadeh, Rojcek and Tseng Price Impact and Bursts In Liquidity Provision 56 / 58
Outlook
Model extensions:
1 Change to business clock instead of 1s intervals.
2 Vary the tail probability for computing threshold to see if there isnon-linearity in price impact.
Obtain data to:
1 Investigate heterogeneity in the cross-section of firms.
2 Distinguish bursts around news announcements from others.
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Conclusion and Outlook
We have answered many questions about HFT...1 We learned how to measure the HFT activity...2 ... and how it impacts traditional measures of market quality.3 We learned that regulations can introduce frictions, evidence of which
supports theoretical predictions.4 And we learned that two papers studying the same thing can lead to
different conclusions, dwindling our confidence...5 ... and that there might exist strategies systematically manipulating
the prices.
... but we still have not much evidence on1 The true transaction costs impact of HFT for institutional investors.2 The ability of HFTs to predict order flow or extract private information.3 The relation of HFTs and systemic risk.4 The impact and influence of HFTs outside of the public equity or FX
markets.5 ...
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Selected References
Aıt-Sahalia, Y. and J. Jacod. 2014. High-Frequency Financial Econometrics. Princeton, New Jersey, USA: Princeton UniversityPress.Baron, M. D., J. Brogaard, B. Hagstromer and A. A. Kirilenko. 2016. Risk and return in high frequency trading. Available atSSRN 2433118.Baruch, S. and L. R. Glosten, 2013. Fleeting orders. Working paper, Columbia University and the University of Utah.Becchetti, L., Ferrari, M. and U. Trenta. 2014. The impact of the French Tobin tax, Journal of Financial Stability 15, 127–148.Bernales, A. 2014. How fast can you trade? High frequency trading in dynamic limit order markets. Working paper, Banque deFrance.Biais, B., T. Foucault and S. Moinas. 2015. Equilibrium fast trading. Journal of Financial Economics 116(2), 292–313.Boehmer, E., K. Y. L. Fong and J. J. Wu. 2015. International evidence on algorithmic trading. In AFA 2013 San DiegoMeetings Paper.Brogaard, J. 2010. High Frequency Trading and Its Impact on Market Quality. Working paper, Northwestern University.Brogaard, J., T. J. Hendershott and R. Riordan. 2014. High-frequency trading and price discovery. The Review of FinancialStudies 27, 2267–2306.Brogaard, J., A. Carrion, T. Moyaert, R. Riordan, A. Shkilko, and K. Sokolov (2015). High-frequency trading and extreme pricemovements. University of Washington Working Paper.Budish, E. B., P. Cramton and J. J. Shim. 2013. The high-frequency trading arms race: Frequent batch auctions as a marketdesign response. Fama-Miller Working Paper, 14–03.Chacko, G. C., J. W. Jurek and E. Stafford. 2008. The price of immediacy. The Journal of Finance 63 3, 1253–1290.Chakravarty, S., P. Jain, J. Upson, and R. Wood. 2012. Clean sweep: Informed trading through intermarket sweep orders.Journal of Financial and Quantitative Analysis 47, 415–435.
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Selected References cont’d
J.-E. and T. Foucault. 2012. Trading Fees and Efficiency in Limit Order Markets, Review of Financial Studies 25, 3389–3421.Colliard, J. E., and P. Hoffmann. 2015. Financial Transaction Taxes, Market Composition, and Liquidity. Working paper,European Central Bank.Committee of European Securities Regulators. 2010. Micro-structural issues of the European equity markets; Ref.:CESR/10-142, Call for Evidence.Commodity Futures Trading Commission (CFTC). 2014. Adoption of Rule 575 (“Disruptive Practices Prohibited”). CMESubmission No. 14 – 367.Conrad, J., S. Wahal and J. Xiang. 2015. High-frequency quoting, trading, and the efficiency of prices. Journal of FinancialEconomics 116(2), 271–291.Du, S. and H. Zhu. 2014. Welfare and Optimal Trading Frequency in Dynamic Double Auctions. Working paper, NationalBureau of Economic Research.Dufour, A. and R. F. Engle. 2000. Time and the price impact of a trade. Journal of Finance 55, 2467–2498.Egginton, J. F., B. F. Van Ness and R. A. Van Ness. 2014. Quote stuffing. Louisiana Tech University Working Paper.European Parliament. 2011. Directive of the European Parliament and of the Council on markets in financial instrumentsrepealing Directive 2004/39/EC of the European Parliament and of the Council, COM(2011) 656 final.Foucault, T., O. Kadan and F. Kandel. 2013. Liquidity Cycles and Make/Take Fees in Electronic Markets, The Journal ofFinance 68, 299–341.Foucault, T., J. Hombert and I. Rosu. 2016. News Trading and Speed. The Journal of Finance 71, 335–381.Friederich, S. and R. Payne. 2015. Order-to-trade ratios and market liquidity. Journal of Banking & Finance 50, 214–223.
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Selected References cont’d
Goettler, L. R., C. A. Parlour and U. Rajan. 2005. Equilibrium in a Dynamic Limit Order Market, The Journal of Finance 60,2149–2192.Goettler, L. R., C. A. Parlour and U. Rajan. 2009. Informed traders and limit order markets, Journal of Financial Economics 93,67–87.Hasbrouck, J. 1991. Measuring the information content of stock trades. Journal of Finance 46, 179–207.Hasbrouck, J. and G. Saar. 2013. Low-Latency Trading. Journal of Financial Markets 16, 646–679.Hasbrouck, J. 2015. High Frequency Quoting: Short-Term Volatility in Bids and Offers. Working paper, SSRN.Hendershott, T. J., Jones, C. M. and Menkveld, A. J. 2010. Does Algorithmic Trading Improve Liquidity? The Journal ofFinance 66, 1–33.Hoffmann, P. 2014. A dynamic limit order market with fast and slow traders. Journal of Financial Economics 113, 156–169.Johnson, B. 2010. Algorithmic Trading & DMA: An introduction to direct access trading strategies. Vol. 200. London:4Myeloma Press.Knorr, E. M. and R. T. Ng. 1997. A unified notion of outliers: Properties and computation. KDD Conference, 219–222.Korajczyk, R. A., and D. Murphy. 2015. High frequency market making to large institutional trades. Available at SSRN2567016.Malinova, K. and A. Park. 2015. Subsidizing Liquidity: The Impact of Make/Take Fees on Market Quality, The Journal ofFinance 70, 509–536.Malinova, K., A. Park and R. Riordan. 2013. Do retail traders suffer from high frequency traders? Working paper.Maskin, E. and J. Tirole. 2001. Markov perfect equilibrium: I. Observable actions. Journal of Economic Theory 100, 191–219.Menkveld, A. J. 2013. High Frequency Trading and The New-Market Makers. Journal of Financial Markets 16, 712–740.
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Selected References cont’d
Menkveld, A. J. and M. A. Zoican. 2016. Need for speed? Exchange latency and liquidity. Review of Financial Studies.O’Donoghue, S. M. 2015. The Effect of Maker-Taker Fees on Investor Order Choice and Execution Quality in US StockMarkets. Working paper.O’Hara, M. 2015. High frequency market microstructure. Journal of Financial Economics 116, 257–270.Pakes, A. and P. McGuire. 2001. Stochastic Algorithms, Symmetric Markov Perfect Equilibrium, and the ’curse’ ofDimensionality, Econometrica 69, 1261–1281.Riordan, R. and A. Storkenmaier. 2012. Latency, liquidity and price discovery. Journal of Financial Markets 15, 416–437.Rostek, M. and M. Weretka. 2015. Dynamic thin markets. Review of Financial Studies..Rosu, I. 2014 Fast and slow informed trading. AFA 2013 San Diego Meetings Paper.Securities and Exchange Commission (SEC). 2010. Concept release on equity market structure, Release No. 34-61358; File No.S7-02-10, Concept release.Tong, L. 2015. A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors. Working paper.Vlachos, M., K. L. Wu, S. K. Chen and S. Y. Philip. 2008. Correlating burst events on streaming stock market data. DataMining and Knowledge Discovery 16, 109–133.
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Role of Market Maker: View from the Market
“The role of the Market Maker on TSX is to augment liquidity, while maintainingthe primacy of an order-driven continuous auction market based on price-timepriority. TSX’s Market Maker system maximizes market efficiency and removes theinterfering influence of a traditional specialist. In the TSX environment, a MarketMaker manages market liquidity through a passive role. Market Makers are visibleonly when necessary to provide a positive influence when natural market forcescannot provide sufficient liquidity.”
“IMC’s primary role is to help make markets more efficient. An efficient market is
one that is liquid. A liquid market is one that allows investors to buy or sell
securities at any time, in a normal size, and get an instantaneous execution. As a
market maker, we provide that liquidity. An efficient market is one in which
transaction costs for investors are as low as possible. Low costs are the direct
result of the speed at which we trade. The faster we are able to react to adjust our
prices to information and events that move markets, the tighter we are able to
quote, ensuring competitive prices.”
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What Does A Market Maker Do?
Traditional definition: a market maker provides liquidity andimpounds information into quotes. For the market maker, order flow(& news) is information flow.
In investigating the impact of algorithmic trading on market quality,by and large the market microstructure literature has retained thedefinition of market maker as a passive liquidity provider.
However, data reveals market maker behavior that cannot beattributed to passive liquidity provision or adverse selection.
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First Observations
The spikes—bursts in quotes— in the rate of incoming quotes in factoccurs with regularity throughout the trading day, with averageinter-arrival time of approximately 40 seconds.
The average duration of bursts is approximately 1.1 seconds.
Bursts is therefore a fundamentally high frequency phenomenon.
At first glance, it cannot be attributed to the role of market maker asliquidity provider nor information aggregator.
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Related Literature
HFT surveys: Jones (2013), O’Hara (2015), Menkveld (2016) andothers. The rise in quote cancellations in the millisecond environmentis documented, e.g. by Hasbrouck and Saar (2013) and many others
Quotes volatility studied by Hasbrouck (2015) and reflect Edgeworthcycles as in Baruch and Glosten (2013).
Egginton et al. (2014) examine market quality impact of bursts.
Intermarket Sweep Orders (ISO) studied by Conrad et al. (2015) andChakravarty et al. (2012).
Brogaard et al. (2015a) HFTs as net liquidity suppliers during EPMs.Brogaard et al. (2014) find HFT trade in the direction of permanentprice impact and opposite of transitory pricing errors. Brogaard et al.(2015b) HFTs are responsible for price discovery mainly through LOs.
Cont et al. (2014) consider price impact of LOs following the OF priceimpact literature e.g. Hasbrouck (1991a, 1991b), Dufour and Engle(2000), Zhang et al. (2001), Engle and Lunde (2003), Hautsch andHuang (2012), see also Bouchaud (2010), and Pham et al. (2015).
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Comments on the VAR model
The lags serve as controls for microstructure frictions: e.g. inventorymanagement, price discreteness, lagged adjustment to information.
Inventory-control effects may cause past quote updates to influencethe market maker’s current quote update. Delayed adjustment toinformation and anticipation of order-splitting means order flow mayhave lagged effects on quote updates.
Order flow {qt} may exhibit autocorrelation due to order splitting. Itis also possible that a market taker who anticipates market maker’sinventory-neutral tendency may adjust his order flow according topast quote updates.
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Comments on the VAR model cont’d
Embedded in the exogeneity assumption of the VAR model of {rt , qt}is the traditional definition of a market maker: εt and νt areorthogonal.
Our analysis will suggest a modification of this model that departsfrom this definition and captures the informational relationshipbetween market maker and taker during bursts.
We first carry out a model selection exercise for the appropriatenumber of lags.
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VAR Lag Selection
We select the number of lags based on Akaike and Bayesianinformation criteria.
We use 10 lags throughout. Our results are robust for 5 to 30 lags.
This figure represents values of Bayesian information criterion for the returns (left) and order flow equations (right) for onetrading day of Alcoa. We obtained similar results for other stocks and days and by using Akaike information criterion as well.
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Asymmetric Adverse Selection cont’d
We will show that, concurrent with bursts, advese selection foropposite sides of LOB undergoes abrupt shifts in severity in oppositedirections.
This explains the apparent contradiction in price impact andinformational correlation.
Price impact is computed based on quotes that were actuallyexecuted against—those on the opposite side of bursts, therefore hasan upward bias.
Information correlation reflects the information of market makers whoinitiate bursts.
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Asymmetric Adverse Selection cont’d
Market makers on the opposite side of the burst charge higher cost ofimmediacy for order flow trading in the direction of the price impact,thus partially compensating for the negative realized spread.
Despite this partial compensation, they suffer more severe adverseselection.
While bursts lasts 1.1 seconds on average, the price impact it exertssignificant price impact at 30 second lag.
The asymmetry shown above suggests we adapt the VAR model toconsider price impact for the two sides of LOB separately.
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