Ekkehart Boehmer, EDHEC Business School
Kingsley Fong, UNSW
Julie Wu, University of Georgia

Using computer algorithms
 to generate, monitor, and cancel orders
 automatically process prices, LOB, news
 predict flows, liquidity, prices

Has been around for at least 25 years (ATD)
 but saw enormous growth over last decade
 milestones: Limit order display rules (NYSE 1993,
Nasdaq 1997), Reg ATS (1998), Reg NMS (2005)
Trading centers and estimated % of share volume in NMS stocks (September 2009)

Agency algos – used by buy-side to minimize
costs of portfolio turnover

Proprietary algos
 goal is to profit from trading environment rather
than investing
 a subset of these proprietary algo users we
consider high frequency trading (HFT) – traders
with response times measured in milliseconds

Proprietary trading firms, potentially
organized as B/D

Proprietary trading desk of large B/D firm

Hedge funds

Passive market making strategies – provide liquidity,
earn the spread

Arbitrage strategies – seek short-horizon patterns

Structural strategies – exploit features of trading
protocol or regulation

Directional strategies – order anticipation,
momentum ignition, …
None of these strategies is new, but the technology to
implement them is.
HFT is for real – between 60% and 80%
of volume, depending on the market
 Proportion of good vs bad strategies is
unknown
 What are consequences of HFT for

•
•
•
market quality?
welfare (of traders, society, …)?
systemic risk?
Depends on strategies
 passive market making should improve liquidity
 stat arb should improve efficiency
 structural and directional strategies could be wealth
transfers
WE DON’T KNOW ACTUAL STRATEGIES, SO THE BEST WE
CAN DO IS LOOK AT THE AGGREGATE EFFECT of AT/HFT
(Ideally, we should care about aggregate welfare: competition
among HFT and with other traders may be desirable, IT arms
races are probably not)

Standard approach:
 design a model with (typically slow) liquidity traders
and/or market makers, add HFT
 typically assume that HFT is informed or sees
information first
 not surprisingly, result is a wealth transfer from slow
to fast traders (e.g., Cartea and Penalva 2011, Jarrow
and Protter 2011)

Instead, we may need models where the choice
to become HFT is endogenous or liquidity
traders can penalize HFT

Probably most important: under what
conditions can HFT increase welfare
 Hoffman 2012 lets traders endogenously invest in fast
trading technology. Welfare gains when markets are
sufficiently efficient. Otherwise IT overinvestment.
 Biais, Foucault, and Moinas 2010 show that HFT can
generate gains from trade and gains from adverse
selection, but a social planner would only consider
gains from trade, not from adverse selection. Again,
HFT overinvest in technology.

Ambiguous effects in Jovanovic and Menkveld
2011

Transactions data where we infer the
presence of AT/HFT indirectly from periods of
very high message traffic

Transactions data with trader category
information that have a set of transactions
attributed to AT/HFT

Transactions data with trader account
information

Hendershott, Jones, and Menkveld 2011 use
order-level message counts as a proxy for AT
 sample covers NYSE activity 2001-2005
 algo trading is positively related to market quality
(improves spreads and price discovery, and
reduces information asymmetry)

AT improve price discovery (Hendershott and
Riordan 2009)

Hasbrouck and Saar 2011 use 2007/2008 INET
Itch data
 infer HFT from millisecond responses to market
events such as quote updates
 identify fast trades that are linked in time to
identify episodes of HFT
 find that HFT improves short-term volatility,
spreads, and depth

Egginton et al. 2011 use 2010 TAQ data
 select one-minute intervals where quotes-per-
minute exceed 20 historical 20-day s.d. (on days
where daily quoting is less than two s.d. away
from the mean)
 discard days with information events

Episodic spikes of (TAQ) quoting activity
 are quite frequent in many stocks
 are associated with degraded liquidity and
elevated short-term volatility

Brogaard (2010) uses a 2008-2009 Nasdaq
sample
 covers 120 Nasdaq stocks (selected by academics)
 26 HFT firms/traders that account for 77% of
trades/74% of dollar volume in this sample (selected
by Nasdaq, excludes large prop desks)

HFT activity is associated with better liquidity,
mixed effect on volatility, better price discovery
(Brogaard 2010, Hendershott and Riordan 2011)

Problem with the Nasdaq sample: potential
selection bias
 less than sparkling clean traders would rationally veto
reporting
 included transactions do not include same-trader activity
on other markets and may not include all their activity on
Nasdaq (?)
 exchange may purposely select liquidity suppliers

This sample probably contains HFT activity
that is more benign than that in a random
sample of HFT

Kirilenko, Kyle, Samadi, and Tuzun 2011
 see individual strategies in S&P500 e-minis
 find that HFT worsened (but did not cause) the
Flash Crash of 2010.

Only with trader IDs
 can we track each trader’s orders across stocks and
markets and infer actual strategies
 overcome endogeneity problem
 accurately determine triggers and consequences of
HFT activity

Broad samples (with inferred AT/HFT activity)
 imply mostly positive effects on average (MQ)
 mixed results on volatility
 negative effects during periods of extremely high
message traffic and around quote updates

Trader type samples
 positive effect on MQ and efficiency
 mixed results on volatility
 selection concerns
What are the consequences of AT/HFT for
markets, traders, firms, countries?
To address gaps in the literature, to answer
market structure questions, to evaluate
whether LTI or AT/HFT are more important:



we need more and broader evidence.
from new samples.
this paper is a first step in that direction.

Use an international sample to measure the
impact of AT/HFT on
 execution cost for different types of traders
 price discovery
 volatility

Document differences across firms, trading
protocols, and countries in these measures
(incomplete)
• Intraday quote and trade data from Thomson-
Reuters Tick History (TRTH)
 39 exchanges, 36 countries, 2001-2009
 on average about 13,000 stocks
 will add NYSE and Nasdaq data from TAQ

Daily data on returns, volume, high-low prices
from Datastream

Information about trading protocols from Speedguide,
Exchange Handbook, WFE

Accounting data from Datastream and WorldScope

AT = -trading volume / # messages
 measures negative of trading volume in
USD 100 per message
 follows Hendershott, Jones, and Menkveld
2011
 messages include trades and quote updates

Time weighted quoted spreads
RQS=(ask-bid)/MQ

Trade weighted effective spreads
RES=2*|P-MQ|/MQ

Amihud = |Return|/volume

Ancerno (formerly Abel-Noser) buy-side
trading costs
RES captures total price impact of a trade.
Decompose RES into
 RPI, the permanent component, the change in
MQ from trade to 5 minutes later, measures
information content (“toxicity”)
 RRS, the transient component, measures reward
to liquidity providers, RES = RRS + 2* RPI

|autocorrelation| for 10-60 minutes intervals
 for each stock, compute midquote returns for 5,
10, 20, 30 , 60 minute intervals
 then compute autocorrelation of those returns
 ignore overnight and zero returns
 note that there is no bid-ask bounce in this
measure

The more efficient the stock price (the closer
it is to a random walk), the smaller is |ARnn|

Use several standard volatility measures
 |R|, for raw and market-adjusted return
 R^2, for raw and market-adjusted return
 Log (Ret10_Var), Log (Ret30_Var)
 daily relative price range = (High-Low)/Close

Have three-dimensional unbalanced panel
 39 markets, about 2250 days, about 330 stocks per
market
 standard unobservable firm-level and time effects
 unobservable market or country effects

Use daily standardization of all variables so
coefficients are comparable across markets

Regress variable of interest on AT and controls

All regressions control for
 Volume (share turnover)
 Volatility (relative price range, excluded from volatility
regressions)
 price level (1/price)
 firm size (ln market cap)

Volatility regressions additionally control for RES and |AR30|

Estimation strategy
 estimate firm/day panel regression for each market
 then aggregate across markets

Use two different approaches within markets
 Two-way dynamic panels within each market
▪ use firm and day fixed effects
▪ use Arellano and Bond (1991) standard errors for marketlevel inference
 Fama-MacBeth within each market (same results,
not reported)
▪ For global inference, compute means across
markets, use cross-sectional t-test for inference
Mean
coefficient
on AT
t-stat
%positive
RQS
RES
RPI
RRS
Amihud
-0.013
-9.1
5%
-0.013
-7.4
8%
-0.002
-0.7
54%
-0.018
-9.2
5%
-0.009
-6.5
15%
More AT activity is associated with lower spreads and
smaller temporary and permanent price impacts.

How does the effect of AT differ for stocks
with different characteristics?

Sort within each market according to each
characteristic

Create dummies LOW and HIGH for low and
high tercile

Include interactions with AT in regression
models
0.070

0.060
0.050
0.040
Solid colors
indicate
significance at
the 5% level
Small cap  More AT reduces
liquidity in small
Mid cap
stocks
Large cap
0.030
0.020
0.010
0.000
-0.010
-0.020
RQS
RES
Amihud
0.020

Pale colors
indicate no
significance at the
10% level

AT does not
benefit low-priced
stocks

According to
Amihud, liquidity
even declines
significantly for
low priced stocks
0.015
0.010
0.005
Low price
Mid price
High price
0.000
-0.005
-0.010
-0.015
RQS
RES
Amihud
0.005

Volatility is SD of
the 20 most rcent
daily returns

AT only benefits
liquidity of low
and mid volatility
firms

There is no AT
effect on high
volatility firms
0.000
-0.005
-0.010
-0.015
-0.020
Low volatility
Mid volatility
High volatility
Mean coefficient on
AT
t-stat
Percent positive
|AR10|
|AR30|
-0.017
-6.1
5%
-0.006
-4.5
23%
More AT activity is associated with better
informational efficiency.
0.010

Pale colors indicate
no significance at
10%

AT does not
improve efficiency
for small, low priced
firms
0.008
0.006
0.004
0.002
Small
Mid
High
0.000
-0.002
-0.004
-0.006
-0.008
-0.010
Market
cap
Price
Volatility
Mean effect
of AT
t-stat
%positive
|ret|
Ret^2
0.033
7.4
87%
0.025
6.6
85%
Ln(Ret10_ Ln(Ret30_
PriceRange
Var)
Var)
0.045
7.9
87%
0.017
3.4
67%
0.029
4.9
79%
• More AT activity is associated with higher volatility.
• We control for efficiency and liquidity of each stock.
• Thus, the volatility increase does not represent “good”
volatility that may arise with very efficient markets.

Effect on relative intraday price range (Results
are robust for several other volatility measures)
0.090
0.080
0.070
0.060
0.050
0.040
0.030
0.020
0.010
0.000
Small
Mid
High
Market cap
Price
Volatility

Volatility
increases
most for
stocks that are
small, low
priced, or
have volatile
returns to
begin with

We do not observe what strategies algo
traders use

Liquidity provision for mid and large cap
stocks implies that at some AT supply
liquidity

How resilient is this liquidity supply over
time?

MMs dislike one-sided order flow that moves
price. E.g., consider sell imbalance: price
moves down, MM is long, faces inventory
losses

Will cut back on liquidity provision on such
one-way days

Will cut back more when imbalance continues
through the next day

Select all days where daily return has the
same sign as on the previous day

Set HARD dummy to one on these days if the
cumulative return exceeds one 20-day
standard deviation

Interact with AT as before
Price Range

AT increases volatility and
efficiency more

AT still increases liquidity, but
significantly less than on
regular days

AT significantly increases the
information content of trades

If AT use MM strategies on
average, they tend to resort to
other strategies when market
making is unusually difficult

Contrasts to traditional MM
with affirmative obligations
|AR30|
RRS
RPI
Amihud
RES
-0.020
0.000
0.020
Change on HARD days
Mean coefficient on AT
0.040


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 CL
dummy and market-day fixed effects
3. estimate second-stage IV model with market
and day fixed effects using predicted values
from (2)
Liquidity
Dependent
RQS
RES
RPI
RRS
Amihud
Efficiency
Estimate
-1.0087
-1.1546
-0.9285
-0.2718
-0.0001
t
-6.6
-6.2
-7.4
-1.8
-6.0
Dependent
|AR10|
|AR30|
Estimate t
-0.0006 -4.5
-0.0001 -0.5
Volatility
PriceRange
|Ret|
Ret^2
0.0002 10.0
0.0001 3.2
0.0000 0.1
Results are largely unchanged with IV –
evidence that causality originates with AT.

We contribute evidence from 39 countries to
shed some light on how algo trading affects
market quality

Algo trading
 improves liquidity and informational efficiency
 worsens volatility, even when controlling for
efficiency and liquidity

But
 AT worsens liquidity of the smallest third of firms
in each market
 AT increases volatility the most for firms that are
small, low priced, and more volatile
 on days when market making is more difficult, AT
provides less liquidity, increases information
content of trades, and increases volatility more.

Results are amazingly consistent across
markets

Volatility increases with more algo trading –
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.


Will higher volatility discourage investors in
the long run and thus increase firm’s cost of
capital? Or affect its ability to raise new
capital?
We will use our 11-year panel to assess
 longer-term effects of AT on firm/country
characteristics
 distribution of benefits and costs over
good/bad times and across stocks