Computer-based trading in the cross-section∗
Torben Latza, Ian Marsh and Richard Payne†
June 15, 2012
Abstract
We investigate low-latency, computer-based trading in almost 300 stocks on the
London Stock Exchange. We demonstrate that the proportion of trades that we
identify as computer-based is increasing in stock liquidity and decreasing in the
ratio of tick size to price. Signed computer-based trading activity leads other
signed trades. Computer-based trading results in lower execution costs for the
least active stocks and also for stocks with high tick to price ratios. Computerbased trades enhance trading efficiency in this regard. The information content
of computer-based trades is high relative to non-computer-based trades but
this advantage decreases with stock liquidity and with tick to price ratios.
Computer-based trades contain relatively high levels of information in hard to
trade stocks, but it is not clear whether this is value-related information or,
given their ability to predict subsequent flows, whether it reflects an ability to
predict the evolution of the order book.
Keywords: Market microstructure; computer-based trading; high-frequency trading; transactions costs; asymmetric information; London Stock Exchange.
∗
Thanks to the London Stock Exchange for providing the data used in this study and to Cass
and Warwick Business Schools for supporting this work with research pump-priming grants. All
errors are our own.
†
Faculty of Finance, Cass Business School, City University, London.
Correspondence:
richard.payne@city.ac.uk.
1
Introduction
In recent times, the effects of automated trading and quoting by computers on price
dynamics and execution costs in equity markets has been put firmly under the spotlight. The US ‘flash crash’ of May 6th 2010 where, in a matter of minutes, US stock
indices and single stock prices dropped precipitously before quickly recovering has
been blamed by many on computer-generated trading and quoting activity. Regulators around the world are looking closely at how computer-based trading (CBT)
affects the quality of markets and trying to decide whether and, if so how, market
microstructures should be altered so as to limit the scope of CBT.1
Much of the discussion of CBT seems to treat it as synonymous with ‘high-frequency
trading’ (HFT) activities of pure trading firms, proprietary trading desks in investment banks and hedge funds. Broadly speaking, high-frequency traders look to profit
from correcting small, transient mis-pricings in markets using technology that allows
them to identify trading opportunities at millisecond timescales.2 However, an enormous quantity of computer-based trades emanates from a completely different source.
Investment banks use algorithmic trading engines to fill the orders of their clients.
Here the motivation is to execute efficiently, usually dynamically across time, an order
for stock in the sense of minimising transactions costs relative to some benchmark.3
The original client orders are unlikely to be related to high-frequency mis-pricings in
markets, although they may be designed to exploit longer-run forecasting rules.
Given the preceding definitions, one would expect high-frequency trading and algorithmic execution to have very different effects on markets. HFT activity is likely to
carry more information relevant at very short timescales and thus contribute more
1
In the UK, which is the focus of this study, the Department for Business, Innovation and Skills
has set up a Foresight programme to investigate how CBT is impacting upon UK markets. See
http://www.bis.gov.uk/foresight/our-work/projects/current-projects/computer-trading.
2
Note that this does not only entail liquidity demand. If the bid-ask spread is greater than the
level justified by microstructure fundamentals then a high-frequency trader can tighten the spread
and thus profit through liquidity supply.
3
One such benchmark might be the volume weighted average price of the stock over the interval
that the order was traded. Another might be the price of the stock at the time the oerder was
submitted to the broker.
1
towards price discovery as measured on a millisecond timescale. Algorithmic trades
will likely carry less high-frequency information, but will likely be timed to take advantage of temporarily favourable trading conditions. Previous work in this area has
verified that high-frequency trading carries information and contributes towards market quality. Brogaard (2010) identifies quoting and trading by high-frequency trading
firms for 120 Nasdaq stocks observed in February 2010. He shows that high-frequency
trades contribute more to efficient prices than do other trades and argues that, overall, high-frequency trading firms are beneficial to market quality. Hendershott and
Riordan (2011) show that computer-based traders in DAX stocks contribute to price
discovery, both through liquidity supply and liquidity demand.
Our aim is to understand whether the positive and negative effects of CBT are uniform
in the cross-section of stocks. If algorithmic trading brings execution cost benefits
to clients, is it able to do so for both the most heavily and least heavily traded
stocks? Our prior is that CBT is most likely to be useful in less liquid markets where
being the first to spot fleeting favourable trading conditions has value. Conversely,
in more liquid stocks, with low tick sizes, trading costs are likely to be uniformly
low such that even a high-latency trader can execute reasonably well. Similarly,
we proceed to ask whether HFT engines identify profitable mis-pricings in even the
most liquid of stocks, despite the enormous quantity of attention focussed on such
stocks? Alternatively, are the gains from HFT trading less liquid stocks large enough
to outweigh the increased costs of aggressive trading? Is the information content of
HFT activity uniform in the cross-section? These are empirical questions and we let
the data speak.
In order to investigate how the effects of CBT change in the cross-section, we are
left with the problem of identifying computer-based trades. Unfortunately our quote
and trade data do not contain any identifiers for the traders involved and thus we
cannot separate activity based on the identities of the counter-parties. Thus we use
an alternative mechanism. We examine each marketable order in the data and classify
it as being generated by a computer if the order that it executes against is less than
2
100 milliseconds old. Essentially we argue that the small reaction time of computers
relative to humans means that incredibly quick executions of new limit orders must
come from a computer. Of course, this is not guaranteed. Just by chance, two human
orders may be entered within milliseconds of one another and match. At the other
end of the spectrum, a computer algorithm might decide to hit an order much older
than 100ms (if other market conditions have changed). Thus our classification scheme
is clearly not perfect. However, we feel that it is very likely to separate out a set of
computer-based trades from all others. Our data-based approach to the identification
of computer-based trading resembles the analysis in Hasbrouck and Saar (2011).
We apply our CBT identification mechanism to data on all stocks electronically traded
on the London Stock Exchange over the period from July to December 2008. In the
cross-section we have just under 300 stocks. For each stock, we reconstruct the order
book tick-by-tick for each trading day in the sample and then apply our scheme for
identifying CBT to all market orders and all marketable limit orders.
Based on this classification of trades, we show that;
• The proportion of trades that we identify as CBT is increasing in stock liquidity
and strongly decreasing in the ratio of tick size to price.
• The information content of computer-based trades relative to non-computer
based trades decreases with stock liquidity and decreases with tick size.
• CBT leads to reduced trading costs in low activity stocks and stocks with large
tick sizes.
Thus, the effects of computer-based trading alter in very clear fashion in the crosssection: CBT does carry more information and can also lead to more efficient execution than other trades, but only for less liquid stocks. Attempts to control or
limit computer-based trading should take note of this, in that in making it harder
for high-frequency trading firms to do business, will likely impede information flow
into less liquid securities and make algorithmic trading of such securities less efficient.
3
This could have the effect of increasing implementation shortfall for mutual funds,
for example, and thus reducing returns to ultimate investors.
A small point to note from our analysis is that if, however, one desires a mechanism
to control the scale of CBT then tick size is an obvious candidate. Increased tick sizes
would see CBT reduced. However, increasing tick sizes leads to the price impact of
CBT rising relative to that of other trades. Thus while increasing tick size might
discourage CBT, it seems that the computer-based trades that remain carry more
information.
The rest of the paper is set out as follows. The next section provides a brief review
of relevant literature and our data is described in Section 3. Section 4 describes our
empirical analysis and our results. Section 5 concludes.
2
Literature Review
Prompted by market developments such as the May 2011 Flash Crash in the US,
regulatory interest and an emerging empirical literature, the last few years has seen
the growth of a body of work that theoretically analyses the impact of computerbased trading on market outcomes (i.e. prices, volumes and liquidity). These papers
differ in their characterisation of computer-based traders such that their predictions
also differ quite dramatically.
Some papers offer a largely positive view of computer-based trading. Gerig and
Michayluk (2010) model an automated market-maker that, unlike human marketmakers, can trade multiple securities and process information from related markets
and embed such a trader in a standard Glosten and Milgrom (1985) sequential trade
model. The existence of the automated market-maker leads to greater informational
efficiency, larger volumes and lower trading costs for liquidity traders. Martinez and
Rosu (2011) also conclude that computer-based traders may be beneficial to markets.
They model them as aggressive traders who exploit a speed advantage and in doing
4
so ensure that information is more quickly reflected in prices.
Another subset of papers suggests that computer-based trading can be harmful for
markets. Cartea and Penalva (2011), for example, introduce a class of parasitic
computer-based traders who are quick enough to trade in between retail traders and
professional dealers. In a Grossman-Miller style world this leads to retail traders
paying higher costs as the computer-based traders extract some surplus from trading
activity. Jarrow and Protter (2011) argue that, in aggregate, the activity of CBTs,
who possess advantages in trading speed and trade independently but on correlated
signals, may create mis-pricings in securities that increase volatility and which damage
the welfare of non-automated traders.
4
Hoffmann (2011) incorporates speed differentials into the Foucault (1999) framework
where the key advantage of speed is the fact that, after revelation of public information, fast traders’ orders are less likely to be picked off than those of slow traders.
Hoffman’s model predicts that, in certain scenarios, the advantage enjoyed by HFTs
leads to non-HFTs suffering higher execution costs and also that total social welfare
may be reduced. Similar results are obtained by Jovanovic and Menkveld (2011).
In contrast to the variation in the results of theory work on the effects of CBT on
market quality, empirical work on the issue by and large suggests a positive relationship between the two. We have mentioned above the contributions of Brogaard
(2010) and Hendershott and Riordan (2011), which demonstrate, using Nasdaq and
Xetra data respectively, that high-frequency trading enhances price discovery and informational efficiency. Hendershott, Jones, and Menkveld (2011) measure the extent
of CBT using the level of message traffic on the NYSE and argue that CBT leads
to more informative price quotes, increased liquidity and smaller asymmetric information problems. Menkveld (2012) studies the entry of a single new high-frequency
trading player to the market for Dutch stocks in 2007-2008, and shows that this new
player behaves as a market-maker, with the majority of its trades passive and thus
4
Cohen and Szpruch (2011) build on the Jarrow and Protter (2011) paper to show how HFTs
may profitably front-run slower traders and how this may reduce market efficiency. They go on to
analyse whether a Tobin Tax might limit the effects of HFTs on market quality.
5
contributing to lower bid-ask spreads. Finally, Kirilenko, Kyle, Samadi, and Tuzun
(2011) analyse the role played by high-frequency traders during the US flash crash of
May 2010. Looking at detailed trading data for the CME’s S&P E-mini contract they
argue that HFTs did not generate the flash crash, attributing the decline in prices
to the actions of fundamental sellers. They do, however suggest, that after the start
of the decline in prices, HFTs became net sellers of futures also, exacerbating the
overall size of the crash.
One thing to note about both the theoretical and empirical work above is that there
is no focus on how the effects of CBT might differ in the cross-section of stocks.
Theoretically, there are no clear predictions as to how to expect CBTs to behave in
less versus more liquid stocks and empirically there has, thus far been no work that
seeks to compare the interactions of CBT and market quality in the cross-section.
3
3.1
Data
Data sources and coverage
We base our analysis on order level data from the London Stock Exchange for the
final six months of 2008. The data provided by the exchange contains every message
arriving at the SETS order book. Our starting set of securities contains 313 stocks
that were SETS traded during this interval. These range from the most liquid Blue
Chip stocks (e.g. BP PLC or Rio Tinto PLC) which see thousands of trades on the
average trading day, to much smaller issues that trade relatively infrequently.
As the data contains every order level message we can build the order book precisely,
millisecond by millisecond, and we can measure the lifetimes of orders precisely (to
the millisecond). Moreover, we can compute the age of any limit order at the time of
its (partial or complete) execution.
We exclude the first five minutes of trading each day to remove the impact of the
6
opening auction. Further, since financial stocks in the UK were subject to restrictions
on short selling in the second half of our sample we also exclude all financial stocks
from the majority of our analysis. We consider the impact of the short sales ban
separately. Finally, some outlier stocks are removed where they have been affected
by mergers and other unusual corporate activities. Finally, we are left with just less
than 300 stocks in our analysis.
3.2
Identifying computer-generated orders
We use a very simple scheme to classify executions as being computer generated. Any
execution that results from a new order filling a standing order that is less than 100
milliseconds old is assumed to be computer generated.5 Essentially, we argue that
100 milliseconds is too short a time interval for a human to identify and act upon
an order book event and thus that the preponderence of the events we isolate must
come from automated trading systems.6
Of course, our classification scheme is not foolproof. A market order emanating from
a human may, by chance, execute against a limit order that was entered only a few
microseconds previously. However, the chances of this happening are slight relative
to the probability of such a trade resulting from computer-based interaction. At the
other end of the spectrum, a computer may decide to hit an order that is multiple
seconds old, perhaps due to changes in markets conditions in a related stock. This
is much more likely and probably happens quite frequently in our data. On balance,
however, we feel that our trade classification scheme is likely to do a reasonable job
of separating out the form of low-latency computer-generated trades that regulators
seem to be focussed on at present and thus is both empirically and economically
interesting.
5
Note that trades are aggregated so that executions at the same price, with the same direction
and at the same millisecond are assumed to be one execution.
6
A time interval of 0.1 seconds is regarded by many to be the lower bound of human reaction
time. For example, in the 100 metres sprint, athletes reacting to the starting gun within 100ms of
it firing are deemed to have false started.
7
Some graphical evidence on the time between order entries and execution times are
given in Figures 1 and 2. These figures show that around 15% of executions meet
our definition of a CBT. This percentage is slightly larger for more liquid stocks and
somewhat smaller for stocks with larger tick sizes relative to price. The figures also
show that there is a spike of probabililty mass for orders less than 100 ms old.
3.3
Basic statistics for computer-based and other trades
We briefly consider the simple cross-section variation in CBT-activity and some basic
relationships between CBT activity and market conditions. We observe the following:
• Figure 3 and Table 1: CBT activity monotonically increases with stock liquidity
• Figure 4 and Table 1: CBT declines as the ratio of tick size to price increases
Computers appear to find high tick size stocks, which tend to have larger depths and
more sticky prices, less easy to trade.
Table 2 suggests the following realtionships between CBT activity and spreads, volume and volatility when we condition on both the normal activity level (characterised
by ADV) and expensiveness to tarde (tick to price ratios):
• there is no clear correlation between CBT and average bid-ask spreads at either
a 5 min or 30 min sampling frequency and regardless of whether one focusses
on particularly liquid or illiquid stocks or high or low tick size stocks.
• there is a tendency, most clear for the most liquid and lower tick size stocks,
for the proportion of CBT activity to increase in high volume times.
• there is a similar result for the relationship between the share of CBT in overall
trading and volatility.
8
• finally, the relationship between CBT and volatility is stronger for low tick size
stocks: perhaps low tick sizes make it easier for CBT to pick off stale limit
orders in volatile times and thus their share of activity is increased.
These last three realtionships are interesting because, as we will show below, the
advantages of CBT over non-CBT is minimised (and may even be negative) in periods
of high trading activity and high volatility. Thus, at the time when CBT activity
levels are highest the advantages they bring are minimised.
4
4.1
Empirical analysis
Order flows: auto and cross correlations
For each stock we compute ‘fast’ and ‘slow’ order flow measures defined as the sum of
signed traded quantities within a one minute interval resulting from CBT and other
trades. Note that the LSE data allows us to sign trades precisely. We first present
auto- and cross-correlations in these flow measures.
Both fast and slow order flows are positively autocorrelated (Figure 5). Averaging
across all stocks, the first order autocorrelation coefficient is 0.16 for slow flow and
0.06 for fast flow. Small though significantly positive autocorrelations in both types
of flow remain even when considering lags of up to ten minutes. On average, slow
flows are always more positively correlated than fast flows. However, for stocks with
high ADV the difference between autocorrelations in fast and slow stocks is very small
after the first lag. Based on these results, it is tempting to regard our low-latency,
CBT order flow as being more opportunistic than ‘slow’ flows. The latter are easier
to classify as resulting from traders splitting large orders and feeding them into the
market over time.
To facilitate cross-stock analysis we proceed to standardise flows by dividing fast
(slow) flow by fast (slow) volume to give an imbalance measure. We then compute
9
cross-correlations between fast and slow flows for a stock at various displacements.
Averaging across stocks we find that strongly positive cross-correlations are found
between slow flows and lagged fast flows (Figure 6). The peak cross-correlation of
+0.37 is found between minute t slow flows and t − 1 fast flows, but correlations
are greater than +0.10 even when fast flows are lagged by five minutes. The one
minute-lag cross-correlation is even higher than the contemporaneous correlation of
0.20. Conversely, correlations between fast flows and lagged slow flows are much
lower, typically less than 0.05, though still positive on average.
Splitting stocks according to ADV reveals some clear patterns. All cross-correlations
are lower than average for high volume stocks, and higher than average for low volume
stocks. For high volume stocks, correlations between fast flows and lagged slow flows
are negative, though magnitudes are very small. Nevertheless, the asymmetry in
cross-correlations remains and fast flows appear to lead slow flows for all three activity
subsamples. Peak cross-correlations are always at the -1 displacement, and are very
large for the less active stocks.
It is important to emphasise the directional element to these results. The results
do not just say that fast trades precede slow trades, they indicate that fast buys
(sells) tend to precede slow buys (sells). These results suggest that CBT anticipate
non-CBT flows. This might be at the core of what CBT does, in the sense that they
make money by executing in front of slower traders in a Brunnermeier and Pedersen
(2005) predatory trading sense. Alternatively, it may simply be explained by CBT
being quicker to react to public revelation of stock or market level news.
In order to better understand the nature of fast and slow order flows we run a regression of stock i’s standardised fast order flow on the contemporaneous market fast
order flow (standardised fast order flow in all stocks excluding stock i). Results suggest that fast flows are positively correlated across stocks - fast buy (sell) orders in
stock i take place at the same time as fast buy (sell) orders for other stocks. We find
the same for slow orders but the regression coefficients and goodness of fit measures
are much higher. Thus, the commonality of order flow found elsewhere in the litera-
10
ture (Hasbrouck and Seppi, 2001) may be driven by commonality in slow flows but is
also true for fast flows. There is significant cross-section variation in these findings as
the least liquid stocks have more strongly correlated fast flows than the most liquid
stocks, while slow flow cross-stock correlations are highest among the more liquid.
4.2
Execution Costs
For each stock we assign every trade an execution cost. This cost is equal to the
distance, in basis points, between the trade price and the midquote just prior to
the execution of the trade. For buy orders this measure (in an electronic market) is
guaranteed to be positive and for sell orders it is always negative. Thus, we take the
negative of the measure for sell trades. Denote that measure for trade t in stock i
by zi,t . We then regress our measure of execution costs on a constant, a dummy to
indicate whether the trade was computer-generated and controls for the size of the
trade, overall stock volume in the 10 minutes preceding the current trade and realised
stock volatility over the preceding 10 minutes;
zi,t = β0,i + β1,i CBTi,t + β2,i T radeSizei,t + β3,i V olumei,t + β4,i σi,t + ui,t
(1)
Given that SETS is a pure limit order book, these execution cost regressions are
simply asking whether, controlling for market conditions, there are systematic differences in bid-ask spreads at times of low latency CBT versus the times of other trades.
Alternatively, do computer-based executions tend to capture smaller spreads than do
human executions? Table 4 presents the results.
Consider first the control variables. As expected, execution costs are higher in more
volatile periods and for larger transactions. Conversely, but still as expected, execution costs are generally lower in high volume periods. The magnitudes of these
effects all decrease as we consider more liquid stocks as proxied by average daily volume (ADV) measures. The constant terms indicate that execution costs for stocks
11
with high tick to price ratios (T/P) are higher than for low T/P stocks within ADV
categories, however there are no systematic patterns when we compare estimated
coefficients for control variables.
Moving to the key variable in the analysis, the results show that CBT execute at
significantly lower costs than non-CBT in all regressions. The gain is between onequarter and two-thirds of a basis point for high ADV stocks and over 3bp for low
ADV stocks. The gains fall as a proportion of the non-CBT execution costs (i.e. the
constant) for more liquid stocks.
These results suggest that algorithms trading more liquid stocks do not offer that
many advantages in execution cost terms over human trades. Of course we don’t
have dynamics in the analysis and so we cannot distinguish worked CBT orders
from worked human orders. In less liquid stocks, though, it appears that the speed
advantage offered by CBT translates directly into them picking off better prices.
When trading and quoting is less continuous (due to low natural trading interest, or
high tick sizes), CBTs can jump in and take good prices as they appear and before
humans can react. CBTs still execute on better terms than do non-CBTs in the most
liquid stocks, but their advantage is much smaller.
4.3
Information contents
We estimate price impacts for CBT and other trades with the following simple regression which is run in transaction time:
rt = α 0 +
P
X
i=1
αi rt−i +
P
X
βj xCBT
t−j
j=0
+
P
X
γk xOther
t−k + t
(2)
k=0
where now xCBT
is a signed, transaction indicator for computer-based trades and
t
xOther
is a similar signed indicator for non-CBT activity. The price impacts for CBT
t
P
P
and other trading respectively are given by Pj=0 βj and Pk=0 γk . The regressions
12
use K = 10 for all stocks.7
Table 5 presents our results. The first two panels show that, as expected, price impact
(PI) is higher for less liquid stocks, irrespective of whether it is a CBT or non-CBT.
Price impacts are also higher for high T/P stocks than for low T/P stocks.
Panel C demonstrates three key findings. First, the ratios of CBT price impact to that
of other trades are all greater than unity, suggesting that on average CBT contain
more information than do other trades. Second, this price impact ratio decreases
with liquidity. CBT in the least liquid stocks contains more information (relative to
other trades) than it does in more liquid stocks. Third, the price impact ratio is lower
for low T/P stocks than for high T/P stocks within a given ADV liquidity category.
That CBT brings information to the market is worth emphasising. CBT is often
characterised as predatory and of no social value. However, our results suggest that
it brings, on average, more information to the market than do other trades. From this
perspective, our results accord very clearly with those of Hendershott and Riordan
(2011) who examine one month of trading in German DAX stocks.
Our key contribution is to examine how this informational advantage of CBT varies
across stocks. The results suggest that price impacts of CBT or non-CBT in the
most liquid stocks are essentially the same, probably since these are highly researched
and very actively traded assets. Conversely, and arguably as expected, CBT has a
clear edge over non-CBT in less liquid stocks with higher degrees of informational
asymmetry.
Per trade, therefore, CBT contains less information for more liquid stocks than it
does for less liquid stocks. And compared to an average non-CBT trade, a CBT
trade contains relatively more information in the less liquid stocks than it does in
more liquid stocks. At least two explanations for this suggest themselves. First, it
is possible that better information about asset payoffs in less liquid stocks give CBT
7
Results are robust to running a full VAR as in Hasbrouck (1991) and estimating price impacts
using impulse response functions. Note that this entails causally ordering the trade variables, but
we have tried this both ways.
13
this pattern of advantage. Second, we might hypothesise that CBT benefits from
better information about the state of the order book for a stock, and the shallower
the order book the better this advantage becomes.8 This is supported by our earlier
findings that fast trades lead slow trades on the same side of the order book. We
explore this further below.
The other aspect to the findings in Table 5 is the role of T/P. The absolute level
of information content of CBT (and non-CBT) increases with T/P and in relative
terms the advantage of CBT over non-CBT is higher for high T/P firms. Again this
is suggestive of CBT carrying more information for more difficult to trade stocks.
Table 6 presents similar statistics where the sample period is split in two. The first
half of our sample was relatively stable, while the second half, corresponding to the
last quarter of 2008, was both very volatile and saw a higher than usual trade volume
as the financial crisis developed rapidly and spilled outside the financial sector.
9
We
observe that, as expected, price impacts of CBT and non-CBT are both higher in
the volatile period. More surprisingly, the relative price impact of CBT to non-CBT
is lower during the volatile second half of the sample. Indeed, for the most easily
traded stocks with high ADV and low T/P, the price impact of CBT is lower than
that of non-CBT.
This might suggest that the CBT advantage is not simply news flow related since the
last three months of 2008 was characterised by a high level of important economywide and value-relevant news. Our results would be consistent with the hypothesis
that in non-turbulent times, CBT brings information to market by quickly exploiting
high frequency predictabilities in markets. However, in very turbulent times the
patterns that CBT trade on become clouded by more important informational issues,
such that the statistically regularities they trade on may actually break down. This
8
It is hard to imagine that CBT has a material advantage over non-CBT in understanding the
evolution of the order book for the most liquid stocks since the bid and ask sides resemble queues
with exceptionally large depth available at the inside spread. An advantage is more likely in more
complex books where liquidity drain and replenish cycles, for example, might be exploitable by a
fast-acting algorithm.
9
Remember that we have excluded financial sector stocks from our analysis.
14
leads to their information advantage falling. We explore this hypothesis by examining
conditional information contents
4.4
Conditional information contents
The cross-sectional and time-series variation of information contents of CBT versus
other trades highlighted in the previous section demands further analysis. In this
section we compute conditional price impacts, first allocating stocks into one of six
categories based on three ADV and two T/P buckets, and then conditioning on
(i) time of day, (ii) trading volume in the preceding 10 minutes and (iii) midquote
return volatility in the preceding 10 minutes. We present these results graphically
[regression-based analysis to follow].
Conditioning by time-of-day reveals some interesting patterns (Figure 7). First, it
is clear that price impacts for CBT and non-CBT are highest at the opening. The
opening is generally considered to be a period of higher than usual information asymmetry (driven by out of trading hours news flow, for example, generating information
differences between traders). On many exchanges it is also a period of higher than
usual trading activity but the LSE is an exception to this rule. Outside of the opening call auction (which is excluded from our sample), trading volume and quoting
activity are both relatively low in the first hour of trading, especially for less liquid
stocks. Second, CBT have much higher price impacts for the least actively traded
stocks, irrespective of T/P (top row). Third, as we consider more actively traded
stocks the information advantage of CBT declines. For the most easily traded stocks
with high ADV and low T/P (bottom left graph) there is essentially no difference
between the information content of CBT and non-CBT.
Figure 8 plots price impacts conditioning on recent trading volume. In the lowest
volume quartiles (quartile 1) CBT contain more information than non-CBT. The
advantage is largest for less liquid stocks and for stocks with higher T/P ratios, but
is present for all categories. In periods of high trading volume (quartile 4) there is
15
essentially no difference between the information content of CBT and non-CBT.
Finally, Figure 9 shows that conditioning on quote activity also indicates that CBT
have relatively high information content in low activity periods (quartile 1) but,
if anything, non-CBT contain more information in periods of high activity levels
(quartile 4).
It is important to note that these findings hold across the spectrum of usual trading
activity as captured by ADV. When stocks trade a lot relative to normal volume, or
trade in a volatile fashion, the informational advantage of CBT seems to disappear.
In summary, for the most liquid, low tick size stocks it is not clear that CBT have
any information advantage at all. Therefore, in such stocks they bring very little to
the table in terms of either execution quality or information. It is in the less liquid
stocks that CBT matters. CBT can reduce trading costs relative to non-CBT, and
fast trades appear to have more information (or can react quicker to information) in
these stocks. However, even this advantage can be eroded by high levels of trading
activity and/or high levels of price volatility.
5
Conclusion
We study how the effects of computer-based trading vary in the cross-section of stocks.
Using a data-based definition of computer-based trading activity we show that;
Based on this classification of trades, we show that;
• The proportion of CBT that we identify is increasing in stock liquidity and
decreasing in the ratio of tick size to price.
• Signed CBT leads signed other activity. CBTs are not only quicker than nonCBTs, but their buys (sells) precede the buys (sells) of other traders.
– Are they predatory, as in Brunnermeier-Pedersen, or parasitic as in CarteaPenalva?
16
• CBT leads to greater reductions trading costs for low activity stocks and for
stocks with large tick sizes than for more active stocks with small tick sizes.
– This is evidence of the trading efficiency that CBT can deliver in hard
to trade stocks and, indirectly, suggests that CBT might be generating
positive effects for those clients who use algorithmic execution systems.
• The information content of computer-based trades relative to non-computer
based trades decreases with stock liquidity and with tick size.
– The basic result echoes earlier work, but the fact that the advantage is
larger in least liquid stocks deserves thinking about.
– Surely these are the stocks with the largest mis-pricings, and so it is conceivable that CBT brings value-related information to the market place.
– However, these are also stocks where knowledge of likely developments in
the order book are of most value. In liquid cheap to trade stocks, order
books are generally deep and stable. Knowledge of likely evolutions in this
type of order book are unlikely to be of value for trading. Conversely, in
less liquid, and more expensive to trade stocks, order books are thin and
potentially volatile. Computer-based traders able to predict changes in
the state of such order books have the potential to gain.
– The relatively high information content of CBT may therefore reflect both
value-related information and/or order flow information.
Policy: tick size is a possible lever. It reduces CBT by our definition and doesn’t
seem to damage the ability of CBT to save on execution costs all that much. It
systematically increases the information advantage of CBT trading relative to other
trades, though. While the former might be something that regulators quite like, the
latter is arguably a nasty side effect.
17
References
Brogaard, J., 2010, “High Frequency Trading and Market Quality,” SSRN eLibrary.
Brunnermeier, M., and L. Pedersen, 2005, “Predatory Trading,” Journal of Finance,
60.
Cartea, Ã., and J. Penalva, 2011, “Where is the Value in High Frequency Trading?,”
SSRN eLibrary.
Cohen, S. N., and L. Szpruch, 2011, “A Limit Order Book Model for Latency Arbitrage,” SSRN eLibrary.
Foucault, T., 1999, “Order flow Composition and Trading Costs in a Dynamic Limit
Order Market,” Journal of Financial Markets, 2, 99–134.
Gerig, A., and D. Michayluk, 2010, “Automated Liquidity Provision and the Demise
of Traditional Market Making,” SSRN eLibrary.
Glosten, L. R., and P. Milgrom, 1985, “Bid, ask, and transaction prices in a Specialist
arket with heterogeneously informed agents,” Journal of Financial Economics, 14,
71–100.
Hasbrouck, J., 1991, “Measuring the Information Content of Stock Trades,” Journal
of Finance, 46(1), 179–206.
Hasbrouck, J., and G. Saar, 2011, “Low-Latency Trading,” SSRN eLibrary.
Hasbrouck, J., and D. Seppi, 2001, “Common Factors in Prices, Order Flows and
Liquidity,” Journal of Financial Economics, 59.
Hendershott, T., C. M. Jones, and A. J. Menkveld, 2011, “Does Algorithmic Trading
Improve Liquidity?,” Journal of Finance, 66, 1–33.
Hendershott, T. J., and R. Riordan, 2011, “Algorithmic Trading and Information,”
SSRN eLibrary.
Hoffmann, P., 2011, “A Dynamic Limit Order Market with Fast and Slow Traders,”
SSRN eLibrary.
Jarrow, R. A., and P. Protter, 2011, “A Dysfunctional Role of High Frequency Trading
in Electronic Markets,” SSRN eLibrary.
Jovanovic, B., and A. J. Menkveld, 2011, “Middlemen in Limit-Order Markets,” SSRN
eLibrary.
Kirilenko, A. A., A. P. S. Kyle, M. Samadi, and T. Tuzun, 2011, “The Flash Crash:
The Impact of High Frequency Trading on an Electronic Market,” SSRN eLibrary.
18
Martinez, V. H., and I. Rosu, 2011, “High Frequency Traders, News and Volatility,”
SSRN eLibrary.
Menkveld, A. J., 2012, “High Frequency Trading and the New-Market Makers,” SSRN
eLibrary.
19
Table 1: Proportion of computer-based trading: sorted by stock liquidity and
tick/price
Low T/P High T/P
Low ADV 0.131 (55) 0.115 (48)
Med ADV 0.169 (42) 0.143 (62)
High ADV 0.184 (61) 0.147 (45)
Notes: for each of our stocks, we estimate the proportion of trades that are computer-generated.
Stocks are split into 3 ADV based groups (at the 33rd and 66th percentiles of the ADV distribution)
and 2 tick size groups (stocks above and below the median value of tick to price). The median
proportion of computer-generated trades in each of the 6 bins is presented above. In parentheses,
the number of stocks in each group is presented
20
Table 2: Correlations between computer-based trading activity and other variables:
5 min sampling : sorted by stock liquidity and tick/price
Panel A: bid-ask spreads
Low T/P High T/P
Low ADV
-0.022
-0.027
Med ADV
-0.008
-0.005
High ADV
0.042
0.021
Panel B: volume
Low T/P High T/P
Low ADV
0.049
0.048
Med ADV
0.086
0.087
High ADV
0.118
0.103
Panel C: volatility
Low ADV
Med ADV
High ADV
Low T/P High T/P
0.015
-0.005
0.069
0.049
0.180
0.117
Notes: for each of our stocks, we compute the correlation between the proprtion of trades generated
by a computer in a 5 minute interval and spreads, volumes and volatilities respectively at the same
sampling frequency. Stocks are split into 3 ADV based groups (at the 33rd and 66th percentiles of
the ADV distribution) and 2 tick size groups (stocks above and below the median value of tick to
price). The median correlation in each of the 6 bins is presented above.
21
Table 3: Correlations between computer-based trading activity and other variables:
30 min sampling : sorted by stock liquidity and tick/price
Panel A: bid-ask spreads
Low T/P High T/P
Low ADV
0.001
-0.028
Med ADV
0.046
0.018
High ADV
0.002
0.021
Panel B: volume
Low T/P High T/P
Low ADV
0.077
0.084
Med ADV
0.117
0.122
High ADV
0.171
0.122
Panel C: volatility
Low ADV
Med ADV
High ADV
Low T/P High T/P
0.048
0.047
0.179
0.114
0.326
0.225
Notes: for each of our stocks, we compute the correlation between the proprtion of trades generated
by a computer in a 30 minute interval and spreads, volumes and volatilities respectively at the same
sampling frequency. Stocks are split into 3 ADV based groups (at the 33rd and 66th percentiles of
the ADV distribution) and 2 tick size groups (stocks above and below the median value of tick to
price). The median correlation in each of the 6 bins is presented above.
22
23
-3.248
-3.174
Trade size
(8.798)
(8.678)
[RGP comment: we needs regression R-squared for this table. Also, we need to remove MECOM from these results.]
Notes: results from regressions of executions costs for individual trades on a constant, a dummy to indicate whether a trade was computer generated
or not, trade size (in 1000s of shares), total market volume and stock return volatility. Regressions are run separately for each stock and then split
into 6 groups based on a bivariate sort on ADV and the ratio of tick size to price. Stocks are split into 3 ADV based groups (at the 33rd and 66th
percentiles of the ADV distribution) and 2 tick size groups (stocks above and below the median value of tick to price). The results above are the
mean coefficients and t-values in each group.
Low T/P
4.772 (79.560) -0.271 (-7.250) 0.015 (7.019) -0.004 (-12.684) 0.148 (20.221)
High T/P 10.340 (70.905) -0.662 (-2.490) 0.012 (6.415) -0.003 (-7.339) 0.156 (12.701)
Panel C: High ADV
(-4.264) 0.142
(-1.757) 0.176
(7.561)
(5.527)
Volatility
(-1.590) 0.325
(0.333) 0.316
Volume
(-4.057) 0.378 (4.435) -0.147
(-3.734) 0.156 (3.975) 0.095
CBT dummy
Low T/P
9.096 (56.082) -0.978 (-7.965) 0.042 (5.515) -0.010
High T/P 13.633 (52.610) -1.625 (-4.007) 0.037 (5.442) -0.004
Panel B: Med ADV
Low T/P 15.280 (33.022)
High T/P 20.243 (28.349)
Panel A: Low ADV
Constant
Table 4: Trade by trade execution cost regressions: CBT versus non-CBT
Table 5: Price impacts of computer-based versus other trades: sorted by stock liquidity and tick/price
Panel A: Price impact of CBT
Low ADV
Med ADV
High ADV
Low T/P
9.54
5.20
2.91
High T/P
10.61
7.78
6.97
Panel B: Price impact of other trades
Low ADV
Med ADV
High ADV
Low T/P
6.66
4.70
2.66
High T/P
7.46
5.44
5.17
Panel B: Price impact ratio
Low T/P
Low ADV
1.44
Med ADV
1.15
High ADV
1.12
High T/P
1.59
1.46
1.34
Notes: for each of our stocks, we estimate the price impacts of computer-based trades and other
trades using the regression model in equation(2). Price impacts should be interpreted as the basis
point movement in price associated with a trade of median size (in shares). Stocks are split into
3 ADV based groups (at the 33rd and 66th percentiles of the ADV distribution) and 2 tick size
groups (stocks above and below the median value of tick to price). The mean price impacts of the
two trade groups and the ratio of the price impacts is presented above.
24
Table 6: Price impacts of computer-based versus other trades across sub-samples:
sorted by stock liquidity and tick/price
First Half
Second Half
Panel A: Price impact of CBT
Low T/P High T/P Low T/P High T/P
Low ADV
80.7
10.67
10.39
11.76
Med ADV
3.93
6.36
6.28
10.01
High ADV
2.35
5.27
3.29
9.49
Panel B: Price impact of other trades
Low T/P High T/P Low T/P High T/P
Low ADV
5.22
6.37
8.38
8.92
Med ADV
3.21
3.94
6.49
7.51
High ADV
1.84
3.62
3.46
7.18
Panel B: Price impact ratio
Low ADV
Med ADV
High ADV
Low T/P High T/P Low T/P High T/P
1.56
1.92
1.33
1.62
1.27
1.69
1.02
1.37
1.33
1.54
0.98
1.26
Notes: for each of our stocks, we estimate the price impacts of computer-based trades and other
trades using the regression model in equation(2). The sample is split into two halves. Price impacts
should be interpreted as the basis point movement in price associated with a trade of median size
(in shares). Stocks are split into 3 ADV based groups (at the 33rd and 66th percentiles of the ADV
distribution) and 2 tick size groups (stocks above and below the median value of tick to price). The
mean price impacts of the two trade groups and the ratio of the price impacts is presented above.
25
Figure 1: Time from order entry to execution: sorted by ADV
0.2
Low ADV
Med ADV
High ADV
0.18
0.16
Propn of trades
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
100
200
300
400
500 600 700
Milliseconds
800
900 1000
Notes: ADV is average daily volume.
Figure 2: Time from order entry to execution: sorted by ratio of tick size to price
0.2
Low T/P
Med T/P
High T/P
0.18
0.16
Propn of trades
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
100
200
300
400
500 600 700
Milliseconds
26
800
900 1000
Figure 3: The proportion of CBT activity: sorted by ADV
0.35
0.3
Propn of trades
0.25
0.2
0.15
0.1
0.05
0
0
50
100
150
200
Stocks: ADV rank
250
300
350
Notes: ADV is average daily volume.
Figure 4: The proportion of CBT activity: sorted by ratio of tick size to price
0.35
0.3
Propn of trades
0.25
0.2
0.15
0.1
0.05
0
0
50
100
150
200
Stocks: T/P rank
27
250
300
350
Figure 5: Autocorrelations of flows: sorted by ADV
Autocorrelations: fast and slow flows: all stocks
Autocorrelations: large ADV
0.16
0.16
Fast
Slow
0.14
0.12
0.12
0.1
0.1
0.08
0.08
0.06
0.06
0.04
0.04
0.02
0.02
0
Fast
Slow
0.14
1
2
3
4
5
6
7
Displacement
8
9
0
10
1
2
3
Autocorrelations: medium ADV
4
5
6
7
Displacement
8
9
10
Autocorrelations: small ADV
0.18
0.16
Fast
Slow
0.16
Fast
Slow
0.14
0.14
0.12
0.12
0.1
0.1
0.08
0.08
0.06
0.06
0.04
0.04
0.02
0.02
0
1
2
3
4
5
6
7
Displacement
8
9
0
10
Notes: ADV is average daily volume.
28
1
2
3
4
5
6
7
Displacement
8
9
10
Figure 6: Cross autocorrelations of flows: sorted by ADV
Cross−correlations: fast and slow flows (negative displacment = fast lead): all stocks
Cross−correlations: large ADV
0.4
0.3
0.35
0.25
0.3
0.2
0.25
0.15
0.2
0.1
0.15
0.05
0.1
0.05
0
0
−0.05
−5
−4
−3
−2
−1
0
1
Displacement
2
3
4
5
−5
−4
−3
Cross−correlations: medium ADV)
−2
−1
0
1
Displacement
2
3
4
5
3
4
5
Cross−correlations: small ADV
0.4
0.7
0.35
0.6
0.3
0.5
0.25
0.4
0.2
0.3
0.15
0.2
0.1
0.1
0.05
0
−5
−4
−3
−2
−1
0
1
Displacement
2
3
4
0
5
Notes: ADV is average daily volume.
29
−5
−4
−3
−2
−1
0
1
Displacement
2
Price impact of a buy trade
Price impact of a buy trade
9
9
9
10
10
10
12
13
Hour of day
14
15
12
13
Hour of day
14
15
11
12
13
Hour of day
14
15
Price impacts by time of day: ADV SS 3, Tick SS 1
11
Price impacts by time of day: ADV SS 2, Tick SS 1
11
Notes: ADV is average daily volume.
0
1
2
3
4
5
0
2
4
6
8
10
0
5
10
15
20
Price impacts by time of day: ADV SS 1, Tick SS 1
16
16
16
16.5
16.5
16.5
Fast
Slow
Fast
Slow
Fast
Slow
0
2
4
6
8
10
0
2
4
6
8
10
12
14
16
0
5
10
15
20
25
30
35
9
9
9
10
10
10
12
13
Hour of day
14
15
12
13
Hour of day
14
15
11
12
13
Hour of day
14
15
Price impacts by time of day: ADV SS 3, Tick SS 2
11
Price impacts by time of day: ADV SS 2, Tick SS 2
11
Price impacts by time of day: ADV SS 1, Tick SS 2
Figure 7: The price impact of a trade, sorted by ADV and T/P, conditional on time of day
25
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
30
16
16
16
16.5
16.5
16.5
Fast
Slow
Fast
Slow
Fast
Slow
1
1
1
aggVolume quartile
3
aggVolume quartile
3
2
aggVolume quartile
3
Price impacts by aggVolume subsample: ADV SS 3, Tick SS 1
2
Price impacts by aggVolume subsample: ADV SS 2, Tick SS 1
2
Price impacts by aggVolume subsample: ADV SS 1, Tick SS 1
Notes: ADV is average daily volume.
0
0.5
1
1.5
2
2.5
3
3.5
4
0
1
2
3
4
5
6
7
0
2
4
6
8
10
4
4
4
Fast
Slow
Fast
Slow
Fast
Slow
0
2
4
6
8
10
0
2
4
6
8
10
12
0
2
4
6
8
10
12
1
1
1
aggVolume quartile
3
aggVolume quartile
3
2
aggVolume quartile
3
Price impacts by aggVolume subsample: ADV SS 3, Tick SS 2
2
Price impacts by aggVolume subsample: ADV SS 2, Tick SS 2
2
Price impacts by aggVolume subsample: ADV SS 1, Tick SS 2
Figure 8: The price impact of a trade, sorted by ADV and T/P, conditional on trading volume
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
31
4
4
4
Fast
Slow
Fast
Slow
Fast
Slow
1
Fast
Slow
1
Fast
Slow
1
Fast
Slow
quoteVol quartile
3
quoteVol quartile
3
2
quoteVol quartile
3
Price impacts by quoteVol subsample: ADV SS 3, Tick SS 1
2
Price impacts by quoteVol subsample: ADV SS 2, Tick SS 1
2
Price impacts by quoteVol subsample: ADV SS 1, Tick SS 1
Notes: ADV is average daily volume.
0
1
2
3
4
5
0
2
4
6
8
10
0
2
4
6
8
10
12
14
4
4
4
0
2
4
6
8
10
12
0
2
4
6
8
10
12
0
5
10
15
1
Fast
Slow
1
Fast
Slow
1
Fast
Slow
quoteVol quartile
3
quoteVol quartile
3
2
quoteVol quartile
3
Price impacts by quoteVol subsample: ADV SS 3, Tick SS 2
2
Price impacts by quoteVol subsample: ADV SS 2, Tick SS 2
2
Price impacts by quoteVol subsample: ADV SS 1, Tick SS 2
Figure 9: The price impact of a trade, sorted by ADV and T/P, conditional on quote price volatility
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
Price impact of a buy trade
32
4
4
4