Ekkehart Boehmer, Singapore Management University
Kingsley Fong, University of New South Wales
Julie Wu, University of Georgia
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
High frequency trading (HFT): activity of
algorithms that submit and cancel orders,
reacting within milliseconds to market
updates .

HFT is for real – between 60% and 80% of
trading volume.

HFT strategies are not transparent
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
Regulators and academics are interested
in the consequences of HFT for
•
•
•

market quality
welfare (of traders, society, …)
systemic risk
Our study focuses on market quality.
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Depends on strategies
 Passive market making should improve liquidity
 Stat arb should improve efficiency
 Structural and directional strategies could be wealth transfers
Algorithmic trading (AT) is a precondition for all HFT
strategies.
Since actual strategies are not known to researchers,
most research studies the AGGREGATE EFFECT of
AT/HFT.
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
Data used
 Transactions data where AT/HFT is inferred indirectly
from the rate of electronic message traffic
▪ cost and speed consideration - > electronic orders
▪ commonly used as a proxy by consultants, exchanges and
other market venues
 Transactions data with trader category information
that have a set of transactions attributed to AT/HFT
 Transactions data with trader account information
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
Hendershott, Jones, and Menkveld (JF2011):
electronic message counts from NYSE’s System
Order Data (SOD) database as a proxy for AT
 concentrate on 2003 NYSE autoquote event
 algo trading improves spreads and price discovery, reduces
information asymmetry

Hasbrouck and Saar (JFM 2013): similar findings with
HFT inferred from ITCH millisecond episodes.

Eggington at al. (WP2014): liquidity worsens on
extremely high-volume days
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
Brogaard (and his coauthors), in several
recent papers, uses a 2008-2009 random
sample of 120 Nasdaq stocks with 26 HFT
firms
 HFT activity is associated with better liquidity,
mixed effect on volatility, better price discovery
 Potential selection issue with exchange-selected
HFT firms
▪ nature of order flow, fraction of order flow, no large
proprietary trading desks
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
Kirilenko, Kyle, Samadi, and Tuzun (WP 2014)
 see individual strategies in S&P500 E-minis
 find that HFT may have worsened (but did not
cause) the Flash Crash on May 6, 2010.

Baron, Brogaard, and Kirilenko (WP 2014)
 find large returns in E-minis for top performing
HFT firms.
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
Broaden the scope of evidence on AT/HFT to an
international sample over a long period and
assess effects on
 liquidity
 price efficiency
 volatility

Examine differences in the cross section of firms
 Size, price level

Study AT/HFT liquidity provision in different
market conditions
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• Intraday quote and trade data from Thomson-Reuters
Tick History (TRTH) and Trades andQuotes (TAQ)
 42 stock exchanges, 37 countries, 2001-2011
 on average about 21,552 common stocks per year



Daily data on returns, volume, price from Datastream
and Center for Research in Security Prices (CRSP)
Buy-side transaction costs data from the Ancerno
database
Information about trading protocols from Reuter’s
Speedguide, Exchange Handbook, World Federation
of Exchanges
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
AT = - trading volume / # messages
 volume per message times (-1), (US$100)
▪ follows Hendershott, Jones, and Menkveld (JF 2011)
▪ normalize raw message traffic with trading volume
▪ messages include trades and quote updates
▪ for the US, TAQ and System Order Data (SOD) based measures are
highly correlated
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
Spreads
RQS=(Ask-Bid)/M, RES=2*|P-M|/M
▪ P is transaction price, M is bid-ask midpoint


Amihud = |daily return|/dollar volume
Short = Dk * ( XP – RP ) / RP
▪ execution shortfall : Ancerno actual (buy side)
institutional price impacts.
▪ XP is the volume weighted average price across
component trades of a daily order ; RP is the reference
price, defined as the opening price on the day of the
order; D is buy(sell) indicator
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
|ARn| for various time intervals
 for each stock, compute mid-quote returns for
various intervals
 then compute autocorrelation of returns
 ignore overnight returns
▪ no bid-ask bounce in this measure

The more efficient the stock price (the closer it is
to a random walk), the smaller is |ARn|
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
Use several standard volatility measures
 |Ret|, Ret^2 for stock raw daily return
 |MktadjRet|, MktadjRet^2, for market-adjusted daily
return
 Log (Ret10_Var), Log (Ret30_Var)
▪ Intraday return variances computed from 10-min and 30-min
mid-quote returns
 daily relative price range = (High-Low)/Close
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
Have three-dimensional unbalanced panel
 42 markets, about 2770 days, about 550 stocks per
market
 Main method: estimate firm and day fixed effects
panel regression for each market, then aggregate
across markets

All variables are winsorized (99.5% and 0.5%)
and standardized daily within a market so
coefficients are comparable across markets
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
Regress market quality measures on AT proxy
and controls
 All regressions control for volume, volatility, inverse
price, firm size
▪ volatility regressions exclude volatility control and add
controls for RES and |AR|

Inference is based on means across markets (42
coefficients, use cross-sectional t-test for
inference)
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RQS
Mean coef. on AT -0.0093
t-stat
-6.69
% positive**
2%
RES
Amihud
-0.0097
-3.52
26%
-0.0110
-6.81
5%
More AT activity is associated with higher liquidity
(i.e. lower spreads and smaller price impact).
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
How does the relation between AT and
liquidity differ for stocks with different
characteristics?

Sort within each market according to firm
characteristic (e.g. SIZE)

Create dummies for Small and Large tercile

Include interactions with AT in regression
models
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
Solid colors
indicate
significance at
the 5% level

More AT is
associated with
better liquidity
in medium and
large stocks
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Mean coef. on AT
t-stat
% positive**
|AR10|
|AR30|
-0.0126
-7.23
7%
-0.0042
-4.01
14%
More AT activity is associated with better informational
efficiency.
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
More AT is
associated with
consistently
better price
efficiency in
large stocks.
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|ret| Ret^2
Mean coef. of AT 0.027 0.0182
t-stat
7.65 6.67
%positive**
81% 81%
Ln(Ret10_ Ln(Ret30_
PriceRange
Var)
Var)
0.0401
0.0216
0.0295
8.52
4.25
5.17
83%
71%
79%
• More AT activity is associated with higher volatility.
• control for efficiency and liquidity.
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
The positive ATvolatility relation
decreases with
firm size.
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
A two-step procedure
 1. estimate a cross-sectional regression within each
market each day, using liquidity, efficiency, and
volatility as dependent variables, and record the AT
coefficients.
 2. compute Spearman rank correlations between AT
coef on liquidity and AT coef on volatility.

The correlations are positive (0.02 -
0.14).
 On an average day, when AT is associated with higher
volatility, AT also is associated with wider spreads.
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
Probably not.

Controlling for the efficiency of prices in
regressions produces the same result.

This implies that higher volatility cannot
easily be attributed to greater price efficiency
accompanied with higher AT.
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

Identify days when market making is difficult
MMs dislike one-sided order flow that moves
price.
 E.g., consider sell imbalance: price moves down, MM
is long, faces inventory losses


Tend to cut back on liquidity provision on such
one-sided trading days
Tend to cut back more when imbalance
continues through the next day
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
Select all days when the daily return has the
same sign as the previous day’s return

Set HARD dummy to one on these days if the
2-day cumulative return exceeds the 20-day
historical mean by at least one standard
deviation

Then interact with AT as before
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
More AT is still associated with higher liquidity, but this is significantly
less than on regular days
 Greater information content of trades (RPI)
 Smaller reward for providing liquidity (RRS)

More AT, higher volatility and efficiency

If AT use MM strategies on average, they tend to resort to other
strategies when market making is unusually difficult.
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
The importance of traditional vs. HFT market
making should increase with AT.

Compare low-AT tercile (“traditional MM”)
with high-AT tercile (“new MMs”) by
estimating the HARD interactions separately
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


AT benefits are concentrated in traditional
MM stocks, especially on HARD days.
Negative AT association (higher volatility, no
liquidity improvement) are concentrated in
new-MM stocks where traditional MMs are
less important.
Not clear that HFT MM are substitutes for
traditional MMs, consistent with Anand and
Venkataraman (2012).
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

Use co-location within each market as an
instrument for AT
Estimate IV regression at the market level
1. compute value-weighted daily averages for each
market
2. estimate first stage regressions of AT on colocation dummy variable with market and day
fixed effects
3. estimate second-stage IV model using predicted
values from 2
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Dependent variable
RQS
RES
Amihud
Shortfall
AT coefficient
-0.023
-0.045
-0.003
-0.024
t
-4.06
-7.12
-0.32
-1.95
|AR10|
-0.041
-4.00
PriceRange
ln(Ret10_Var)
|Ret|
0.060
0.076
0.066
9.99
15.62
9.16
Results are largely unchanged with IV.
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
Results maintain if we
 control for news announcements
 exclude financial crisis period
 use Fama-McBeth regression for weekly or
monthly aggregation periods
 run time-series regression at firm level first, then
aggregate across firms
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
Algo trading
 improves liquidity and informational efficiency
 increases volatility, even when controlling for efficiency
and liquidity

But
 Little liquidity effect in the smallest third of firms in each
market
 AT increases volatility the most for small firms that are
small.
 On days when market making is more difficult, AT
provides less liquidity, increases information content of
trades, and increases volatility more.
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
Volatility increases with more AT – what
exactly are the implications?

In assessing the current market structure,
market observers should take into account
that
 the effects of AT are not uniform across markets,
across stocks, and over time.
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