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High-Frequency Trading in the U.S. Treasury Market: Liquidity and Price
Efficiency around Macroeconomic News Announcements
George J. Jiang
Ingrid Lo
This draft: August 12, 2015
Giorgio Valente
High-Frequency Trading in the U.S. Treasury Market: Liquidity and Price
Efficiency around Macroeconomic News Announcements
Abstract
This paper investigates high-frequency (HF) trading in the U.S. Treasury market around major
macroeconomic news announcements. After identifying HF market and limit orders on the basis of
the speed of their placement, alteration and cancellation deemed beyond manual ability, we use the
introduction of the co-location facility (i-Cross) by BrokerTec at the end of 2007 as an exogenous
instrument to assess the impact of HF trading on market liquidity and price efficiency. We find that
HF trading activity substantially increases after news announcements and generally improves price
efficiency. However, it has a negative impact on liquidity, as it widens spreads before announcements and lowers depth of the order book after announcements.
JEL classification: G10, G12, G14.
Keywords: High-frequency Trading; Macroeconomic News Announcements; U.S. Treasury
Market; Market Liquidity; Price Efficiency.
*
George J. Jiang is from the Department of Finance and Management Science, Carson College of Business,
Washington State University, Pullman, Washington 99164. Email: [email protected]. Ingrid Lo is from the
University of Victoria, Wellington, New Zealand. Email: [email protected]. Giorgio Valente is from the Department
of Finance and Economics, City University of Hong Kong, Hong Kong. E-mail: [email protected]. The authors
wish to thank Gordon Dash, Scott Hendry, Corey Garriot, Charles Jones (the AFA discussant), Ryan Riordan, Joshua
Slive, Andriy Shkilko, Tong Yu, Sarah Zhang, and the seminar participants at the 8th Central Bank Workshop on
the Microstructure of Financial Markets, Ottawa; the 2012 International Conference on Finance at National Taiwan
University, Taipei; the 2014 American Finance Association Meetings; the Chinese University of Hong Kong; the Hong
Kong Polytechnic University, and the University of Rhode Island for helpful comments and suggestions. The study is
supported by a grant from the Research Grants Council of Hong Kong (Project No. 4478712). This work was partly
written while Giorgio Valente was visiting the Bank for International Settlements and the University of Essex. The
views expressed here are solely our own and do not necessarily reflect those of the Bank for International Settlements.
1
1
Introduction
High-frequency (HF henceforth) trading1 carried out by computer programs has become prevalent
in financial markets during the past decade. As reported in financial media, trading records have
routinely been broken in recent years, and millions of data messages are regularly sent every second
to various trading venues.2 This anecdotal evidence is coupled with the hard fact that trading
latency in financial markets has decreased by about two orders of magnitude over the past decade
(Moallemi and Saglam, 2011) and the number of quotes per minute has increased over the same
period of time by a factor of 100, reaching even higher levels during the recent financial crisis
in 2007-2008 (Angel, Harris and Spatt, 2011). As a result of this market innovation, trading
and quoting activities now regularly take place within a fraction of a second (e.g., Clark, 2011;
Hasbrouck, 2012; Hasbrouck and Saar, 2013).
One of the main advantages of trading at a very high speed is that computers, with the capacity
to rapidly process a large amount of information, are well positioned to execute multiple actions
in response to information arrival. Recent theoretical studies have explored the interplay between
speed and information processing with mixed results. Some studies show that HF traders, who act
as liquidity suppliers, are able to update quotes quickly after news arrival and thus reduce adverse
selection risk (Jovanovic and Menkveld, 2011; and Hoffman, 2014). Others argue that HF traders
are likely to place market orders to take advantage of their information-processing capacity and
speed. These faster orders, which are based on updated information, pick off manual orders that
react more slowly to information arrival and, as a result, increase adverse selection and have a
negative impact on market liquidity (Biais, Foucault and Moinas, 2011; Foucault, Hombert and
Rosu, 2013; and Martinez and Rosu, 2013). Menkveld and Zoican (2014), extend these early
1
In the spirit of SEC (2010), we define, and use throughout this study, HF trading as the range of automated
proprietary trading characterized by the following distinctive attributes: 1) the use of extraordinarily high-speed and
sophisticated computers programs to generating, routing and executing orders 2) the use of co-location services and
individual data feeds offered by exchanges and others to minimize network and other time of latencies 3) very short
time-frames for establishing and liquidating positions 4) the submission of numerous orders that are cancelled shortly
after submission. See also Chordia, Goyal, Lehmann and Saar (2013) and the studies included in the same special
issue of the Journal of Financial Markets.
2
See “Speed and market complexity hamper regulation” Financial Times, October 7, 2011.
2
results and show that increased trading speed allows HF traders who provide liquidity to update
their quotes more quickly on incoming news, but it also allows more frequent trading with HF
speculators (‘bandits’) that can hit the quotes of rivals faster on news. The net effect depends on
the relative strengths of the two channels: market liquidity can decrease with trading speed around
news events if HF liquidity suppliers’ losses are large because of more frequent transactions with
quicker HF speculators. As a result, HF liquidity suppliers will increase spreads to recover the
increased adverse-selection cost. A corollary of this result is that HF traders, especially speculators,
will engage in an arms race to outpace their rivals when processing information and hit quotes at
a progressively higher speed. Budish, Cramton and Shim (2015) explore this specific issue and
show that, because of the predominant market design characterized by a continuous limit-order
book, HF trading naturally leads to mechanical deviations from the Law-of-One-Price even in the
presence of symmetrically-observed public information.
Despite the mounting theoretical literature and the ongoing policy debate on the role of computer traders in financial markets, there has been little empirical research on the impact of trading
speed on liquidity and price efficiency around news releases. We aim at filling this gap by investigating the effect of HF trading in the U.S. Treasury market around macroeconomic news
announcements. As one of the largest financial markets in the world, with daily trading volume
nearly five times that of the U.S. equity market, the U.S. Treasury market has a unique market microstructure, operating as both an interdealer market and a limit order market with no intervention
of market makers. It is open virtually around the clock, with active trading taking place around
pre-scheduled macroeconomic news releases. These news announcements are the main drivers of
Treasury security prices and they are arguably the most significant events in the this market3 , unlike equity markets where macroeconomic news announcements do not generate the largest price
3
A vast literature has examined the effect of macroeconomic news announcements on the U.S. Treasury markets.
Fleming and Remolona (1997) and Andersen et al. (2003, 2007) find that the largest price changes are mostly associated with macroeconomic news announcements in the Treasury spot and futures markets. Balduzzi, Elton and
Green (2001), Fleming and Remolona (1999), Green (2004) and Hoerdahl, Remolona and Valente (2015) point out
that the price discovery process for bond prices mainly occurs around major macroeconomic news announcements.
Menkveld, Sarkar and van der Wel (2012) record similar findings for 30-year Treasury bond futures. Pasquariello and
Vega (2007) find that private information manifests on announcement days with larger belief dispersion.
3
movements (Cutler, Poterba and Summers, 1989 and the references therein). Because of these
important features, we argue that pre-scheduled macroeconomic announcements in the U.S. Treasury market provide a unique setting to analyze the relationship between HF trading, information
arrival and market quality. In fact, pre- and post-announcement periods are very different informational environments in that the former is characterized by a relatively quiet market with pending
information arrival and uncertainty about the information content of news releases, whereas the
latter is characterized by the disclosure of information about macroeconomic fundamentals and
the resolution of information uncertainty.
Some recent studies have investigated the impact of HF trading on market quality without taking explicitly into account different informational environments (see, among others, Hendershott,
Jones, and Menkveld, 2011; Hasbrouck and Saar, 2013; Boehmer, Fong and Wu, 2012; for the equity market, Chaboud, Chiquoine, Hjalmarsson and Vega, 2014; for foreign exchange markets).4
One common finding of these studies is that HF trading generally improves both market liquidity
and price efficiency. Our study reassesses these results and extends the existing literature along
the following dimensions: First, we analyze the effect of HF trading on market liquidity and price
efficiency around significant information events. Second, we examine the differential impact of HF
market orders and limit orders on market liquidity and price efficiency. Third, and most important,
we take into account the fact HF trading and market liquidity and price efficiency could be endogenously determined. Hence, to meaningfully assess causality in this context, in line with Hendershott, Jones and Menkveld (2011), Boehmer, Fong and Wu (2012) and Brogaard, Hagstromer,
Norden and Riordan (2014), we use the introduction of a co-location service facility (i-Cross) by
one of the major electronic brokers on the US Treasury secondary market (ICAP BrokerTec), as
an exogenous instrument in the empirical analysis.5 In fact, it is reasonable to hypothesize that the
4
Notable exceptions are represented by Brogaard, Hendershott and Riordan (2014) and Scholtus, van Dijk and
Frijns (2014) who examine the impact of HF trading and trading speed on price discovery and market quality around
macroeconomic news announcements in the US equity market, respectively.
5
i-Cross was introduced on the BrokerTec platform by ICAP at the end of 2007. According to ICAP,
http://www.icap.com/what-makes-us-different/in-and-on-the-news/ /media /Files/I/Icap-Corp/pdfs/i-cross-sheet.pdf,
“i-Cross is a premium connectivity service from ICAP that provides API customers with a low-latency, high-speed
connection...., i-Cross facilitates the housing of customers’ hardware at a common data facility with ICAP. i-Cross
4
introduction of the co-location service allows HF traders to react even faster to information arrival
and reduce latency. Such improvement in speed can be considered exogenous, as it is not due to
changes in market liquidity or price efficiency.
The data used in our study are obtained from ICAP BrokerTec. The data contain tick-by-tick
observations of transactions and limit order submissions, alterations, and cancellations for 2-, 5and 10-year notes. Since there is no readily available identifier in the data to distinguish HF trading
from manual trading, we propose a procedure to identify HF market orders and limit orders based
on the speed of order placement or the subsequent order alterations. Specifically, using information
on the time of order submission or alterations in response to changes in market conditions, we
classify HF market and limit orders as those that are placed at a speed deemed beyond manual
ability. This identification procedure is a variant of the ones proposed by Hasbrouck and Saar
(2013), Scholtus and van Dijk (2012) and Scholtus, van Dijk and Frijns (2014) who use the speed
of order submissions/cancellations after changes in market conditions to identify empirical proxies
for HF trading activities. After identifying HF market orders and limit orders, we examine their
causal effect on market liquidity and price efficiency of the US Treasury secondary market.
We find a host of interesting results: First, both HF market orders and limit orders increase substantially following pre-scheduled news announcements. The increased HF trading activities during the post-announcement period relative to the pre-announcement period is significantly higher
than the increment recorded for the overall trading activities during the post-announcement period
relative to the pre-announcement period. This is consistent with the predictions of theoretical models suggesting that the participation rate of HF traders increases with the arrival of news (Foucault,
Hombert and Rosu, 2013; Hoffmann, 2014; Jovanovic and Menkveld, 2011; and Martinez and
Rosu, 2013).
Second, we find that an increase in HF trading leads to significant widening of spreads during
the pre-announcement period. The positive impact on spreads mainly comes from HF market orders. HF trading also leads to an increase in depth behind the best quotes, suggesting that limit
provides a co-location solution for U.S. Treasury trading via BrokerTec in North America (Secaucus, NJ).”
5
orders are submitted at less aggressive levels. These findings are in contrast with those suggesting that HF trading narrows spreads (Jovanovic and Menkveld , 2011; Hendershott, Jones and
Menkveld, 2011; and Menkveld, 2013), but they are in line with others recording that HF market
orders negatively impact liquidity when information uncertainty is high (Brogaard, Hendershott
and Riordan, 2014). We also find, consistent with the predictions of the theoretical literature, that
HF trading has a negative impact on liquidity due to faster reaction to public information arrival
(Biais, Foucault and Moinas, 2011; Foucault, Hombert and Rosu, 2013; and Martinez and Rosu,
2013). Specifically, HF trading significantly reduces both depth at the best quotes and depth behind
the best quotes upon news release. HF market orders have a negative impact on depth at the best
quotes but a positive impact on depth behind the best quotes, whereas HFLO have the opposite impact. One potential explanation, as recently discussed in Hendershott and Riordan (2013), is that
HF market orders react quickly to information arrival and are executed against stale limit orders
upon news release, leading to reduction of depth in the best quotes and more limit orders being
placed at less aggressive levels during the post-announcement period to avoid being picked off.
Third, our analysis shows that HF trading improves price efficiency, measured by the absolute
serial correlation of bond returns (Boehmer and Kelley, 2009; Boehmer, Fong and Wu, 2012),
during both pre- and post-announcement periods. The positive impact on price efficiency mainly
comes from HF market orders, especially during the post-announcement period. Our results lend
support to the predictions of theoretical studies, such as Martinez and Rosu (2013), that HF market orders quickly incorporate information into prices upon information arrival. Our findings are
also consistent with Chaboud, Chiquoine, Hjalmarsson and Vega (2014) and they echoe the empirical results of Brogaard, Hendershott and Riordan (2014) who show that HF trading improves
price efficiency and the improvement mainly comes from HF market orders. However, we document that also HF limit orders have a positive impact on price efficiency especially after news
announcements.
Our study is closely related to Brogaard, Hendershott and Riordan (2014) and Scholtus, van
Dijk and Frijns (2014) who explore the effect of HF trading on price discovery, liquidity and
6
volatility in the US equity market around macroeconomic news announcements. Our analysis
differs from these studies in several important respects. First, we investigate HF trading in the
US Treasury market that is considerably different from the US equity market. In fact, apart from
the obvious institutional differences (Fleming and Remolona, 1999), information that is relevant
to bond prices originates nearly exclusively from macroeconomic fundamentals, unlike the equity
market where information “comes from many sources and in many forms” (Brogaard, Hendershott
and Riordan (2014, p. 27). Hence, assessing the impact of HF trading around macroeconomic
news announcements in the US Treasury market allows us to identify, in a more accurate way,
periods of information uncertainty from periods where the uncertainty is resolved. Furthermore, in
comparison with the two earlier studies we explore a considerably larger panel of macroeconomic
variables, spanning a total of 31 major US news announcements.6 Second, when assessing the
causality between HF trading and liquidity and price efficiency, we take into account the potential
endogeneity among the variables used in the empirical investigation. Such potential endogeneity
bias is not explicitly considered in both Brogaard, Hendershott and Riordan (2014) and Scholtus,
van Dijk and Frijns (2014). Third, our proposed identification procedure allows us to distinguish
the effect of HF market orders from the one exerted by HF limit orders on the same variables of
interest. This richer characterization, allows us to uncover important patterns that are not recorded
in studies that use aggregate measures of HF trading activity.
The remainder of the paper is structured as follows. Section 2 introduces the data set used in
our analysis and describes in detail the procedure of identifying HF market orders and limit orders.
Section 3 presents the main empirical results. Section 4 performs robustness checks and further
analysis, and the final section concludes.
6
Scholtus, van Dijk and Frijns (2014) and Brogaard, Hendershott and Riordan (2014) investigate 20 and 8 major
macroeconomic announcements, respectively.
7
2
Data
2.1
Summary Statistics
Data on pre-scheduled macroeconomic news announcements and the survey of market participants
are obtained from Bloomberg. Following Pasquariello and Vega (2007), the list of announcements
was compiled to ensure that all important news items are included in our analysis. The full list
contains 31 pre-scheduled announcements. Table 1 reports the day and time of announcement
for each news item. The majority of announcements occur at 8:30 a.m. ET and 10:00 a.m. ET.
Following Balduzzi, Elton and Green (2001), Andersen et al. (2003, 2007), and Pasquariello and
Vega (2007), we compute the standardized announcement surprises for each news item as follows:
SURk,t =
Ak,t − Ek,t
, k = 1, 2, · · · , K, t = 1, 2, ...T
σk
(1)
where Ak,t is the actual value of announcement k on day t, Ek,t is the median forecast of the
announcement k and σk is the time-series standard deviation of Ak,t − Ek,t , t = 1, 2, · · · , T . The
standardized announcement surprise is used in our study as a measure of unexpected information
shock. As shown in Balduzzi, Elton and Green (2001), professional forecasts based on surveys are
neither biased nor stale.
The data on U.S. Treasury securities used in our study are obtained from BrokerTec, an interdealer Electronic Communication Network (ECN) platform of the U.S. Treasury secondary market,
owned by the largest interdealer brokerage (IDB) firm, ICAP PLC. Prior to 1999, the majority of
interdealer trading of U.S. Treasuries occurred through interdealer brokers. Since then, two major
ECNs have emerged: eSpeed and BrokerTec. Trading of on-the-run U.S. Treasury securities has
mostly, if not completely, migrated to electronic platforms.7 According to Barclay, Hendershott
and Kotz (2006), the electronic market accounted for 75.2%, 83.5% and 84.5% of the trading of
the 2-, 5- and 10-year notes, respectively, during the period from January 2001 to November 2002.
By the end of 2004, over 95% of interdealer trading of active issues occurred on electronic plat7
For an excellent review of the transition to ECNs in the secondary U.S. Treasury market, please refer to Mizrach
and Neely (2006).
8
forms. BrokerTec is more active in the trading of 2-, 3-, 5- and 10-year notes, while eSpeed is
more active in the trading of 30-year bonds. The BrokerTec data used in our study contain tickby-tick observations of transactions as well as limit order submissions and subsequent alterations
and cancellations for on-the-run 2-, 5- and 10-year U.S. Treasury notes. It includes the time stamp
of transactions and limit order submissions as well as their subsequent alterations, the quantity
entered and/or cancelled, the side of the market involved and, in the case of a transaction, an aggressor indicator indicating whether the transaction is buyer- or seller-initiated. The sample period
is from January 3, 2006 to December 29, 2011.
In our empirical analysis, we focus on HF trading around news announcements. We define the
15-minute interval prior to the announcement as the pre-announcement period and the 15-minute
interval following the announcement as the post-announcement period. For all three maturities, we
first compute the average relative bid-ask spread and the average depth of the limit order book, both
at the best quotes and behind the best quotes ($ million) at the end of each 1-minute interval. These
variables are then averaged each day within the pre-announcement and post-announcement periods. The summary statistics of the variables around announcements are reported for completeness
in Table 2.
Figure 1 plots the patterns of some of these variables around news announcements for the
2-year note. The patterns for other maturities are similar and thus are not reported for the sake
of brevity. For the purpose of comparison, the value of the variables are reported in comparison
with the ones recorded at the same time on days without announcements. Compared with nonannouncement days, the bid-ask spread on announcement days peaks right before the announcement. Both depth at the best quotes and depth behind the best quotes start to drop substantially
before announcement time. The drop is more pronounced for depth at the best quotes. This indicates that dealers withdraw their orders to avoid being picked off right before public information
arrival. This finding is consistent with the evidence from an earlier sample period documented in
Fleming and Remolona (1999) and Jiang, Lo and Verdelhan (2011). After public information arrives, bid-ask spread reverts quickly to the pre-announcement level. Both depth at the best quotes
9
and depth behind the best quotes increase gradually after a news announcement and almost return
to the level of non-announcement days.
2.2
The Empirical Identification of HF Market and Limit Orders
The BrokerTec data include reference numbers that provide information on the timing of an order
submission and its subsequent execution, alteration or cancellation. Using this information, we
identify HF market and limit orders based on the reaction time to changes in market conditions
deemed beyond manual ability. Specifically, the following criterion is used to identify HF market
orders/trades (HFMO hereafter):
• HFMO – Market orders (buy or sell) that are placed within a second of a change in the best
quote on either side of the market (highest bid or lowest ask).
The following criteria are then used to identify HF limit orders (HFLO hereafter) in three
different categories:
• HFLO1 – Limit orders (buy or sell) that are cancelled or modified within one second of their
placements, regardless of changes in market conditions;
• HFLO2 – Limit orders (buy or sell) at the best quotes that are modified within one second of
a change of the best quotes on either side of the market (highest bid or lowest ask);
• HFLO3 – Limit orders (buy or sell) at the second-best quote that are modified within one
second of a change of the best quote on either side of the market (highest bid or lowest ask).
The above procedure is specifically designed to identify HFMO and HFLO on the basis of the
reaction time within which orders are submitted, executed or altered. As HF traders use computers
to generate, route and execute orders over very short time frames, our empirical procedure is
designed to capture this salient feature from available data.8 Furthermore, the use of speed to
8
This is also supported by evidence that traders compete to locate their servers close to exchanges in order to
reduce the latency in managing their orders. One example is Thomson Reuters Hosting Solutions - Prime Broker-
10
separate HF from non-HF orders is also similar in spirit to the methodology proposed by Hasbrouck
and Saar (2013) to identify low latency orders.9
Nonetheless, we recognize our identification procedure is far from perfect in that non-HF orders
can be mistakenly identified as HF orders if non-HF orders are placed earlier but arrive within one
second of market condition changes. Similarly, some HF orders may be classified as non-HF orders
if they arrive at the system beyond one second of market condition changes. As a result, some
non-HFMO and non-HFLO may be labelled incorrectly as HFMO and HFLO, and vice versa.
Although limitations and potential misclassifications are intrinsic in any empirical identification
procedure, we also note that more than 90% of the HFLO are identified as HFLO1, which are
orders cancelled or modified within less than one second of their placement, regardless of market
condition changes. Given this low latency, these orders are unlikely to be placed manually, hence
allow us to capture the aspect of HF trading (speed) which we are especially interested in in the
empirical investigation.10
Figure 2 shows the ratio of overall HF orders, defined as the total monthly volume of HFMO
and HFLO, to the total volume of all market and limit orders submitted over the same period.
This ratio increases substantially over the sample period. In fact, it increases from 24% in the first
quarter of 2006 to 40% in the last quarter of 2011. We take into account this time trend in the data
by constructing measures of abnormal HF trading around macroeconomic news announcements in
our analysis. Similar to Bamber (1987) and Ajinkya and Jain (1989), the abnormal volume of HF
age (http://thomsonreuters.com/financial/thomson-reuters-elektron/) “We host algorithmic trading applications at our
data centers located in close proximity to the world’s leading financial centers ... We manage algorithmic trading
applications co-located in exchange data centers... Market data is delivered with ultra-low latency from the markets”
9
In our empirical analysis, we exclude those orders deleted by the central system, orders deleted by the proxy,
stop orders, and passive orders that are automatically converted by the system to aggressive orders due to a locked
market. On the BrokerTec platform, the percentages of these types of orders account for 1.5%, 1% and 0.8% of the
total number of orders for the 2-, 5- and 10-year notes, respectively.
10
One may also argue that this labelling could be purely mechanical, as around macroeconomic news announcements, more trading activity would make orders to cluster together around the same time, including manual orders.
Although we do not rule out this possibility, we think that it is very unlikely because manual orders cannot be submitted or altered by a manual trader within the cut-off period of 1 second. Furthermore, if a full-second threshold is
considered too large to characterize recent HF trading activity, it is important to emphasize that our results are fully
confirmed even with a smaller threshold of 200ms (as reported in Section 4), which is clearly beyond the ability of
manual traders.
11
market and limit orders is computed as the dollar volume of actual HFMO and HFLO in excess of
the average dollar volume of HFMO and HFLO over the same 1-minute interval over the past five
non-announcement days:
5
∗
HF M Ot,1M
(i) = HF M Ot,1M (i) −
1X
NA
HF M Ot−k,1M
(i) ,
5 k=1
(2)
5
∗
HF LOt,1M
(i)
1X
NA
= HF LOt,1M (i) −
HF LOt−k,1M
(i) ,
5 k=1
(3)
where HF M Ot,1M (i) and HF LOt,1M (i) denote the dollar volume of HFMO and HFLO, respecNA
tively, recorded within the ith 1-minute interval on announcement day t, HF M Ot−k,1M
(i) and
NA
HF LOt−k,1M
(i) denote the dollar volume of HFMO and HFLO recorded during the same 1-minute
interval over the past k non-announcement days, where k = 1, . . . , 5.
Table 3 reports summary statistics of HFMO and HFLO and overall market and limit orders for
all three notes during both the pre-announcement period and the post-announcement period. The
results in Panel A show that the identified HFLO are around one-third of all limit orders for each of
the three maturities. Both HFLO and all limit orders more than double after news announcements.
However, the increment of HFLO is larger than the one exhibited by all limit orders. The daily
average ratio of post-announcement HFLO volume relative to their pre-announcement level is
significantly larger than that of all limit order volume. Panel B shows that the HFMO identified
are around one-quarter of the overall trading volume for all three maturities. Similar to the case of
HFLO, HFMO increase after announcements and the daily average ratio of HFMO volume during
the post-announcement period relative to the pre-announcement level is significantly larger than
that of the overall volume of all market orders.
Figure 3 shows the minute-by-minute volume of HFMO and HFLO for the 2-year note around
announcements, contrasting with HFMO and HFLO activities during the same time interval on
non-announcement days. The patterns for the 5- and 10-year notes are similar and thus not reported
for brevity. The volume of both HFMO and HFLO spikes up following macroeconomic news releases and drops subsequently. Nonetheless, the volume of HFMO and HFLO on announcement
12
days remains higher than on non-announcement days at the end of the post-announcement interval. Together, these findings suggest that HF trading actively responds to the arrival of public
information and this confirms predictions of the theoretical literature that the HF participation rate
increases with news arrival.
To examine abnormal HF trading more closely, Panel C of Table 3 reports summary statistics
of the abnormal volume of HFMO and HFLO and overall market and limit orders for all three
Treasury notes during both the pre- and post-announcement periods. The abnormal volume of
HFMO and HFLO, as well as that of the overall market and limit orders, is negative in most cases
during the pre-announcement period. This indicates that HFMO and HFLO flee the the market
before announcements, compared with the same time on non-announcement days. The abnormal
volume of HF trading and overall trading turns positive during the post-announcement period. This
indicates that both HFMO and HFLO, and the overall market and limit orders, are more active after
information arrival compared with non-announcement days.
3
3.1
Empirical Analysis
Instruments for HF Trading
The main goal of our analysis is to investigate the effect of HF trading on liquidity and price
efficiency around macroeconomic news announcements. More specifically, we build upon Hendershott, Jones and Menkveld (2011) and formally test the relationship between our proposed
measures of HF trading and variables capturing market liquidity and price efficiency during the
15-minute period preceding the announcement and the 15-minute period following the announcement as follows:
0
Xit(j) = αi + γit(j) + βHFit(j) + ϕ Cit(j) + ηit(j) ,
(4)
where Xit(j) denotes a measure of liquidity or price efficiency computed for the U.S. Treasury note
i in minute j during day t, HFit(j) denotes our measure of HF trading, αi captures bond-specific
fixed effects, γit(j) is a minute-of-the-interval dummy variable capturing potential seasonal patterns
13
around announcement times, and Cit(j) is a set of variables controlling for market conditions. In
this paper, Cit(j) comprises the absolute change in mid-quote, a proxy for volatility, and the term
spread, defined as the difference between the yields of the 2-year note and the 10-year note (Fama
and French, 1993; Campbell and Ammer, 1993; and Li, Wang, Wu and He, 2009). Consistent
with equations (2) and (3), the variables Xit(j) , HFit(j) and Cit(j) are constructed as the difference
between their value in minute j during the announcement day t and their average value computed
during the same minute interval over the past five non-announcement days.
As emphasized in recent studies, HF trading and market liquidity are endogenously determined
(Hendershott, Jones and Menkveld, 2011; and Boehmer, Fong and Wu, 2012). Contemporaneous
changes in HF trading and market liquidity could be due to either HF trading reacting to changes
in market liquidity or to HF trading causing changes to market liquidity. To assess this causal
relationship, we follow Boehmer, Fong and Wu (2012) and Brogaard, Hendershott and Riordan
(2014) and use the introduction of a co-location facility on the BrokerTec platform by ICAP (labelled i-Cross) at the end of 2007 as an exogenous event. i-Cross hosts customers’ equipment and
network connectivity within two of Equinix’s Internet Business Exchange centers in the New York
region11 , which enables a low latency data exchange between HF trading firms and the BrokerTec
platform. In the official press release, it is explicitly indicated that the benefits of i-Cross include
“High-speed, low-latency connection” and “faster time to market · · · for a range of fixed income
products” (ICAP, November 7th, 2007). The introduction of i-Cross is likely to provide HF trading
firms with even faster access to the BrokerTec platform and the ability to react faster to changes
in market conditions or the arrival of new information. Thus, i-Cross is likely to have a significant
impact HF trading, but it is unlikely to be correlated with the idiosyncratic liquidity component,
ηit(j) , in Equation (4).
We examine the impact of the introduction of i-Cross on abnormal total HF activity, defined
11
According to the co-location service brochure of Equinix (Equinix, 2014, “Are your digital assets
mission-critical?”(available at http://www.equinix.com/resources/infopapers/equinix-colocation-brochure/), International Business ExchangeTM (IBX) data centers are built to have “direct access to the data distribution system to
allow quickly deployable interconnections” and their infrastructure “minimizes interference problems and permits
rapid provisioning of bandwidth from a large choice of participating providers.” (Equinix, 2014, page 2)
14
as the sum of HFLO and HFMO volumes, abnormal HFLO, abnormal HFMO, volatility, and term
spread. Specifically, we regress each of these variables on the i-Cross dummy Qit(j) as follows:
Mit(j) = αi + γit(j) + βQit(j) + εit(j) ,
(5)
where Mit(j) denotes abnormal total HF activity, abnormal HFLO, abnormal HFMO, volatility and
term spread; Qit(j) is a dummy variable that equals 0 during the period from January 1, 2006 to
November 7, 2007 and 1 after January 1, 2008; αi denotes maturity-specific fixed effect; and γit(j)
are minute-of-the-interval dummies capturing seasonal patterns around announcements. Table 4
reports the estimate of β for each of the regressions. The results show a clear impact of the introduction of i-Cross on HF trading as both abnormal volume of HFMO and HFLO increases after the
inception of the co-location facility. The result holds for all notes and the effect is larger in magnitude for the 5- and 10-year notes. On the other hand, there is no consistent relationship between
the introduction of i-Cross and volatility and term spread.
In our empirical investigation, we adopt an instrumental variable approach, beginning with the
estimation of the following first-stage regression:
0
HFit(j) = αi + γit(j) + βQit(j) + ϕ Cit(j) + εit(j) ,
(6)
where HFit(j) is the dependent variable capturing abnormal HF trading; Qit(j) is a dummy variable
that equals 0 during the period from January 1, 2006 to November 7, 2007 and 1 after January
1, 2008, αi captures bond-specific fixed effect, γit(j) is a minute-of-the-interval dummy variable
capturing potential seasonal patterns around announcement times, and Cit(j) denotes the set of
control variables including volatility, term spread and absolute standardized surprise during the
post-announcement period.
The predicted values of HF activities from Equation (6) are used in the second stage for the
estimation of the following equation:
d it(j) + ϕ0 Cit(j) + ηit(j) ,
Xit(j) = αi + γit(j) + β HF
(7)
d it(j) is the predicted value from Equation (6), and Cit(j) is a vector of control variables,
where HF
15
including volatility, term spread and three lags of Xit(j) ,12 γit(j) is a minute-of-the-interval dummy
variable capturing potential seasonal patterns of liquidity variables around announcement times.
Xit(j) denotes abnormal liquidity variables, i.e., bid-ask spread, depth at the best quotes and depth
behind the best quotes, used in early studies in similar contexts (see, Fleming and Piazzesi, 2006;
Mizrach and Neely, 2008; and Fleming and Mizrach, 2009). Consistent with equations (2) and (3),
we define abnormal bid-ask spread, abnormal depth at the best quotes and abnormal depth behind
the best quotes as:
5
∗
SP RDt,1M
(i) = SP RDt,1M (i) −
1X
NA
SP RDt−k,1M
(i) ,
5 k=1
5
BST ∗
DP T Ht,1M
(i)
=
BST
DP T Ht,1M
(i)
1X
BST,N A
−
DP T Ht−k,1M
(i) ,
5 k=1
=
BHD
DP T Ht,1M
(i)
1X
BHD,N A
DP T Ht−k,1M
−
(i) ,
5 k=1
5
BHD∗
DP T Ht,1M
(i)
In the post-announcement period only, we also include an absolute standardized announcement
surprise term and incorporate the interaction of HF variables with the absolute standardized surprise to analyze the role of public information shocks and whether the effect of HF trading depends
on the level of information shocks:
d it(j) + β2 HF
d it(j) × |SU R| + ϕ0 Cit(j) + ηit(j) ,
Xit(j) = αi + γit(j) + β1 HF
(8)
where |SU R| denotes the absolute value of standardized announcement surprise.
We assess the impact of HF trading on bond price efficiency in a similar fashion and use the
absolute autocorrelations of tick-by-tick log returns based on the mid-quote at each transaction over
a five-minute interval as a proxy for price inefficiency (Boehmer and Kelley, 2009; Boehmer, Fong
and Wu, 2012). 13
14
The intuition is that if prices follow a random walk, serial correlations of bond
12
The number of lags of dependent variables in the regression is based on the Akaike information criterion (AIC).
We confirm that the estimation results remain qualitatively similar using five lags in Equation (7).
13
The use of returns based on the mid-point of the quoted bid and ask helps to mitigate the effect of market
microstructure noise, particularly bid-ask bounce.
14
We recognize that the serial correlation of returns is not the only proxy to capture price efficiency. For example,
one could also look at measures that capture how much fundamental information gets revealed during trading in the
16
returns should be equal to zero at all horizons. Deviations from zero imply return predictability or
price inefficiency. More specifically, we estimate:
d it,5M (i+1) + ϕ0 Cit,5M (i+1) + ηit,5M (i+1) ,
|ACt,5M (i+1) | = αi + γit,5M (i+1) + β HF
(9)
where |ACt,5M (i+1) | is the absolute autocorrelation of tick-by-tick log returns based on the midd it,5M (i+1) is the predicted value of HF
quote at each transaction over a five-minute interval, HF
activities from Equation (6) estimated over the same five-minute interval. As in Equation (8),
absolute news surprises and their interaction with HF variables are included in the regressions
during the post-announcement period:
d it,5M (i+1) + β2 HF
d it,5M (i+1) × |SU R|
|ACt,5M (i+1) | = αi + γit,5M (i+1) + β1 HF
+ ϕ0 Cit,5M (i+1) + ηit,5M (i+1) .
3.2
(10)
The Impact of HF Trading on Market Liquidity and Price Efficiency
This section reports the results the empirical analysis based on the framework detailed in Section
3.1.
Table 5 reports the results related to market liquidity. We examine the impact of overall HF
trading, i.e. combining HFMO and HFLO together, in Model 1 and the individual impact of HFMO
and HFLO separately in Model 2. The results reported in Table 5 suggest that HF trading tends
to worsen liquidity both before and after announcements. During the pre-announcement period,
as shown in Model 1 of Panel A, HF trading on the whole significantly widens abnormal relative
spreads by 0.0002 basis points. One standard deviation change in overall HF trading leads to a 22%
increase in the relative spread for the 2-year note.15 Similar calculations show that a one-standarddeviation change in overall HF trading leads to 12% and 5% increases in the relative spreads for
spirit of Kyle (1985, p. 1330). In this study for the sake of simplicity and a direct link with existing studies, we use
the measure discussed above, but we leave the investigation of alternative measures of price efficiency as agenda for
future research.
15
Given an overall HF trading standard deviation of 927.61 for the 2-year note, a one-standard-deviation change in
overall HF value is associated with a 927.61*0.0002=0.18 basis points, which represents 0.18 × 100/0.85 = 22.1%
increase in the relative spread for the 2-year note.
17
the 5- and 10-year notes, respectively. Disentangling the impact of HFMO and HFLO, the impact
on abnormal relative spreads comes mainly from abnormal HFMO. Positive variations in abnormal
HFMO (HFLO) cause significant increments (reductions) of relative spreads
HF trading also leads to more depth at less aggressive levels during the pre-announcement
period. We find that overall HF trading significantly increases depth behind the best quote (Model
1 of Panel C) but has no significant impact on depth at the best quotes (Model 1 of Panel B). A
one-standard-deviation increase in overall HF trading is associated with $138.40, 95.97 and 72.37
million increases in depth behind the best quotes for the 2-, 5- and 10-year notes, respectively.
This is equivalent to 4.4%, 11.5% and 8.5% increases in depth behind the best quotes for the
2-, 5- and 10-year notes. In addition, the positive impact of HF trading on depth behind the best
quotes comes from HFLO. Increases in abnormal HFLO are associated with significantly positive
impact on depth behind best quotes (Model 2 of Panel C), while increases in abnormal HFMO
significantly reduce depth behind the best quotes.
During the post-announcement period, we find that HF trading has no significant impact on
bid-ask spreads but has a negative impact on depth. Increases in abnormal overall HF trading
significantly reduce both depth at the best quotes (Model 1a of Panel B) and depth behind the best
quotes (Model 1a of Panel C). A one-standard-deviation increase in overall HF trading is associated
with drops of $58.53, 38.65, 30.91 million in depth at the best quotes, which is equivalent to a
reduction of 11.50%, 47.86% and 41.52% in depth at the best quotes. Similarly, a one-standarddeviation increase in overall HF trading is associated with reductions of 17.08%, 43.83% and
32.86% in depth behind best quotes. If we look at HFMO and HFLO separately, we find that
HFMO have a significantly negative impact on depth at the best quotes (Model 2a of Panel B) but
a significantly positive impact on depth behind best quotes (Model 2a of Panel C). These results
suggest that HFMO are used by informed traders and, as a result, limit orders are placed at less
aggressive levels to avoid being picked off. On the other hand, HFLO significantly increase depth
at the best quotes but reduce depth behind the best quotes.
We also assess whether the impact of HF trading is affected by the level of public information
18
shocks. The coefficient estimates of the interaction term between |SU Rk,t(j) | and HF variables
show that the effect of HF trading on bid-ask spread and depth at the best quotes does not depend
on the level of information shock. However, a larger |SU Rk,t(j) | intensifies the impact of HF
trading on depth behind the best quotes. The interaction term of HF variables with |SU Rk,t(j) |
has the same sign as HF variables. For example, the coefficient of HF × |SU R| is negative
and statistically significant at the 1% level, suggesting that larger absolute announcement surprise
magnifies the negative impact of abnormal overall HF trading on depth behind the best quotes.
Overall the results suggest that HF trading causes a larger reduction in depth behind the best quotes
when the level of information shock is high.
Table 6 reports the results related to price efficiency. We find that HF trading improves price
efficiency during both the pre- and post-announcement periods. The sign of the coefficient on
the HF trading is significantly negative (Model 1, Model 1a and Model 1b), implying that HF
trading significantly reduces the absolute autocorrelation of returns around announcements. We
also find that the improvements in price efficiency come from HFMO during both the pre- and postannouncement periods. The coefficients associated with HFMO are significantly negative in Model
2, Model 2a and Model 2b, while those associated with HFLO are either insignificant (Model 2)
or significantly positive (Model 2a and Model 2b). Thus, while HFMO have a negative impact on
market liquidity, they help to quickly incorporate information into prices. However, the level of
information shocks tends to counteract the impact of overall HF trading and, more specifically, the
effect of HFMO on price efficiency. The coefficients of HF × |SU R| and HF M O × |SU R| are
both significantly positive at the 1% level. This suggests that higher level of information shock
hinders the process whereby overall HF trading incorporates information into prices.
4
Robustness
In this section, we perform various robustness checks on the results presented in the previous
section. Specifically, we examine whether our main results are robust to the consideration of a
subset of important macroeconomic news announcements. We also check whether using a shorter
19
cutoff time in classifying HF trading than the one adopted in Section 3 affects the patterns of
HFMO and HFLO around news announcements. Lastly, we analyze whether our results are robust
to the unique trading protocol, based on expandable limit orders or workups, adopted in the U.S.
Treasury market.
4.1
Important Announcements
We first analyze whether the impact of HF trading on market liquidity and price efficiency as documented in the previous section are robust to important macroeconomic news announcements. The
list of important announcements is selected based on the Bloomberg relevance index and all of
which are shown to have important impact on the U.S. Treasury market in the existing literature
(e.g., Green, 2004; and Pasquariello and Vega, 2007). The list includes the seven most important announcements, namely CPI, Change in Nonfarm Payroll, Initial Jobless Claims, Consumer
Confidence Index, GDP Advance, ISM Non-manufacturing and Retail Sales.
Table 7 reports the results for the impact of HF trading on market liquidity based on the list
of important macroeconomic news announcements. Similar to the analysis in Section 3, we use
observations during both the 15-minute pre-announcement and post-announcement periods. The
results show that our baseline findings based on all news announcements are robust to the list of
important news announcements. In fact, the sign and significance of the coefficients are largely
similar to those reported in Table 5 based on all macroeconomic news announcements. During the
pre-announcement period for important announcements, HF trading significantly widens spreads,
has no impact on depth at best quotes, deepens depth behind best quotes and improves price efficiency. However, the impact of HF trading seems to be more pronounced around announcements
of important news. In particular, the coefficient capturing the impact of abnormal HF trading on
relative spreads is almost triple the magnitude of the one based on all news announcements. During
the post-announcement period, the results for the impact of HF trading on liquidity variables are
also similar to those based on all news announcements, except that HF trading widens spreads, as
shown in Model 1b of Panel A. The positive impact on spreads comes again from HFMO, and the
20
associated coefficient is twice as large as the one based on all news announcements.
Table 8 presents the results for the impact of HF trading on price efficiency based on the list of
important macroeconomic news announcements. The results show that although HF trading does
not have a statistically significant impact on the absolute autocorrelation of bond returns, HFMO
continue to improve price efficiency during the post-announcement period. However, their effect
is offset by the opposite effect of HFLO.
4.2
Alternative HF Trading Classification
Kosinski (2013) reports that “human reaction times are in the order of 200 milliseconds.” As
a robustness check, we use 200 milliseconds instead of 1-second as a threshold in classifying
HFMO and HFLO. We are interested in whether the identified HF trading activities based on a
200 milliseconds threshold exhibit the consistent patterns as those based on 1-second threshold
around news announcements. Table 9 reports HFMO and HFLO classified using a 200 milliseconds threshold. The results show that using 200 milliseconds as the threshold does not affect the
patterns of HF trading around news announcements. Although the volume of HFMO and HFLO
drops naturally as a result of using a smaller time threshold, the results are qualitatively similar
to those reported in Table 3. The volume of HFMO and HFLO under the alternative classification scheme also increases during the post-announcement period. In addition, the increase of the
volume of HFMO (HFLO) during the post-announcement period relative to the volume during the
pre-announcement period is significantly higher than the relative increase of all market (limit) orders. This finding holds true for all three maturities. The abnormal volume of both HFMO and
HFLO under the alternative classification scheme also have consistent patterns with those reported
in Table 3.
4.3
The Workup Process and Price Efficiency
A unique feature of the secondary U.S. Treasury market is the workup process. As detailed in Boni
and Leach (2004), the U.S. Treasury market adopts a trading protocol which allows for Expandad-
21
able Limit Orders, or workups, where traders who submit limit orders have the right to expand the
quantity associated with their limit orders at the same prevailing price. As a result, during workup
phases bond prices are essentially frozen while trades still take place. Since our identification of
HFMO depends on changes in best quotes, a potential concern is that our measure of HF trading
may potentially be biased toward finding results supportive of increased price efficiency.
To address this important concern, we repeat the analysis on the impact of HF trading on
price efficiency in Section 3 by explicitly excluding transactions involved in the workup process.
Specifically, the absolute autocorrelation of tick-by-tick log returns is recalculated after excluding
trades that occur within each workup. This filter removes instances at which prices are unchanged
due to the workup process but trading still takes place. The results of this robustness exercise are
reported in Table 10 and show that our baseline findings are robust to the explicit consideration
of workups. In fact, during the pre-announcement period, the coefficient of overall abnormal
HF trading remains significantly negative, which indicates that HF trading does improve price
efficiency. Both HFMO and HFLO significantly improve price efficiency, although the coefficient
of HFMO is smaller in magnitude and less significant compared with the results reported in Table 6.
During the post-announcement period, the results are similar to those in Table 6, both qualitatively
and quantitatively. In addition, similar to the findings reported in Table 6, HFMO significantly
improve price efficiency in both Model 2a and Model 2b, while HFLO reduce price efficiency.
5
Conclusion
This paper investigates HF trading in the U.S. Treasury market around macroeconomic news announcements. Using a comprehensive data set provided by BrokerTec, one of the leading interdealer electronic trading platforms in the secondary U.S. Treasury market, we identify HFMO
and HFLO based on the speed of their placement, alteration or cancellation that is deemed beyond manual ability. We examine how HFMO and HFLO take place around macroeconomic news
announcements, whether they increase or deplete market liquidity, and their impact on the price
efficiency of the U.S. Treasury securities.
22
Our results show that the volume of both HFMO and HFLO increase substantially following
macroeconomic news announcements. The ratio of the volume of post-announcement HFMO
(HFLO) relative to the volume of pre-announcement HFMO (HFLO) is significantly larger than
the ratio for overall market (limit) orders. HFMO and HFLO tend to have a negative impact
on liquidity around public information arrival. In fact, during the pre-announcement period, HF
trading significantly widens spreads. The positive impact on spreads is mainly driven by HFMO.
During the post-announcement period, HF trading significantly reduces depth, both at the best
quotes and behind the best quotes. The negative impact on depth at the best quotes is mainly
driven by HFMO, whereas the negative impact on depth behind the best quotes is mainly driven
by HFLO. Our results also show that overall HF trading tends to improve price efficiency, and the
improvement is mainly driven by HFMO. An increase in abnormal HFMO significantly reduces
the absolute autocorrelation of bond returns during both the pre- and post-announcement periods.
Unexpected information shocks, however, significantly hinder the positive effect of HF trading on
bond price efficiency.
23
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29
30
Macroeconomic News
Building Permits
Business Inventories
Capacity Utilization
Change in Nonfarm Payrolls
Construction Spending
Consumer Confidence Index
CPI
Durable Goods Orders
Existing Home Sales
Factory Orders
Fed’s Beige Book
FOMC Minutes
FOMC Rate Decision
GDP Advance
GDP Final
GDP Preliminary
Housing Starts
Industrial Production
Initial Jobless Claims
ISM Manufacturing Index
ISM Services Index
Leading Indicators
New Home Sales
NY Empire State Index
Personal Income
Personal Spending
PPI
Retail Sales
Trade Balance
Treasury Budget
Unemployment Rate
N
72
72
72
72
72
72
72
68
72
72
48
44
24
24
24
24
72
72
313
72
72
72
72
72
72
72
72
72
72
70
72
Time
8:30
10:00
9:15
8:30
10:00
10:00
8:30
8:30
10:00
10:00
14:00
14:00
12:30/14:15
8:30
8:30
8:30
8:30
9:15
8:30
10:00
10:00
10:00
10:00
8:30/15:00
8:30
8:30
8:30
8:30
8:30
14:00
8:30
Day
Around 24th/25th of the month
Around the 15th of the month
Two weeks after month end
First Friday of the month
Two weeks after month end
Last Tuesday of the month
Around the 13th of the month
Around the 26th of the month
Around the 25th of the month
Around the first business day of the month
Two weeks prior to each Federal Open Market Committee Meeting
Thursday following the next FOMC meeting
According to schedule
3rd/4th week of the month for prior quarter
3rd/4th week of second month following the quarter
3rd/4th week of first month following the quarter
Two or three weeks after the reporting month
Around the 15th of the month
Thursday weekly
First business day of the month
Third business day of the month
Around the first few business days of the month
Around the last business day of the month
Around the 15th/16th of the month
Around the first business day of the month
Around the first business day of the month
Around the 11th of the month
Around the 12th of the month
Around the 20th of the month
About the third week of the month for the prior month
First Friday of the month
with absolute surprise k times greater than its standard deviation.
N|SU R|>σ
25
17
17
26
21
22
20
17
21
22
0
0
0
7
8
6
19
17
83
28
20
17
18
23
8
28
19
15
26
9
28
N|SU R|>2σ
2
3
4
2
7
3
3
4
3
4
0
0
0
2
2
1
3
3
21
2
3
6
6
3
4
6
4
5
3
2
5
of the month of the announcement. σ denotes the standard deviation of announcement surprises. N|SU R|>kσ denotes the number of announcements
sample period from January 3, 2006 to December 29, 2011. Time denotes the time (eastern) of the announcement. Day denotes the weekday or day
This table reports the list of macroeconomic news announcements included in our analysis. N denotes the total number of announcements during our
Table 1. List of Macroeconomic News Announcements
31
0.85
427.18
3144.70
1.01
68.80
829.21
1.93
62.80
853.18
Panel B: 5-year note
Relative Spread (×10,000)
Depth at best bid and ask ($mil)
Depth behind best bid and ask ($mil)
Panel C: 10-year note
Relative Spread (×10,000)
Depth at best bid and ask ($mil)
Depth behind best bid and ask ($mil)
Mean
Panel A: 2-year note
Relative Spread (×10,000)
Depth at best bid and ask ($mil)
Depth behind best bid and ask ($mil)
2006 to December 29, 2011.
1.57
46.00
599.00
0.78
52.00
631.00
0.78
335.00
2443.00
0.88
53.97
767.34
0.47
58.51
705.60
0.22
350.01
2518.48
1.53
13.00
202.00
0.77
15.00
196.00
0.78
65.00
605.00
Pre-announcement Period
Median
Std 10th Pctl
3.13
137.00
1999.00
1.57
145.00
1668.00
0.79
936.00
7043.00
90th Pctl
1.82
74.45
1109.90
0.94
80.74
1040.14
0.83
504.72
4041.07
Mean
1.57
57.00
778.00
0.78
63.00
830.00
0.78
432.00
3818.00
0.60
58.69
903.28
0.33
61.35
780.58
0.18
361.93
2560.39
1.52
20.00
313.00
0.77
23.00
308.00
0.78
103.00
1028.00
Post-announcement Period
Median
Std 10th Pctl
3.10
153.00
2644.00
1.56
163.00
2035.00
0.79
1032.00
7817.00
90th Pctl
each day. We report the mean, median, standard deviation (Std) and the 10th and 90th percentile of each variable. The sample period from January 3,
depth at best bid and ask ($ mil) and average depth behind best bid and ask ($mil). All variables are calculated over 1-minute intervals and averaged
and the 15-minute interval following announcements (the post-announcement period). Liquidity variables include relative bid-ask spread, average
The table reports summary statistics of liquidity variables during the 15-minute interval preceding announcements (the pre-announcement period)
Table 2. Summary Statistics of Liquidity Variables
32
241.93
1173.19
Panel B: Market orders
HFMO
All market orders
501.64
2533.40
25386.16
231.96
262.13
25880.24
78858.25
Panel C: Abnormal limit and market orders
Abnormal HFLO
-993.73
13196.99
Abnormal limit orders
-4761.57 38124.95
Abnormal HFMO
12.34
253.03
Abnormal market orders
6.26
1236.50
10425.35
127.69
121.32
10674.36
32283.24
Panel A: Limit orders
HFLO1
HFLO2
HFLO3
All HFLO
All limit orders
Pre-ann
2-year
Post-ann
−
Allpo
Allpr
0.63***
0.22***
HF po
HF pr
-1624.28
-5643.52
-5.34
-60.11
276.64
1067.61
11979.39
105.34
114.05
12198.78
32317.99
Pre-ann
13009.23
27376.75
259.81
860.69
557.07
2062.87
27403.47
171.61
189.32
27764.40
68229.92
5-year
Post-ann
−
Allpo
Allpr
0.34***
0.48***
HF po
HF pr
-1452.99
-4390.49
-5.06
-53.92
232.28
905.75
9063.69
85.86
93.35
9242.91
24753.13
Pre-ann
10947.94
23263.71
219.47
770.14
470.39
1806.57
22187.06
130.47
152.48
22470.02
54818.98
10-year
Post-ann
−
Allpo
Allpr
0.27***
0.53***
HF po
HF pr
differences between these two ratios for both limit orders and market orders. ***, **, and * denote significance at 1%, 5% and 10% levels, respectively.
trading volume during the post-announcement period versus that during the pre-announcement period each day ( All
Allpr ). The table reports average
po
of the HF trading volume during the post-announcement period versus that during the pre-announcement period each day ( HF
HF pr ) and the ratio of total
po
quotes on either side of the market. Abnormal HFMO and HFLO are defined as in Equations (2) and (3) of the main text. We also calculate the ratio
second of a change in the best buy (sell) quote. HFMO denotes market buy (sell) orders that are placed within one second of a change in the best
change in the best quote on either side of the market. HFLO3 denotes limit buy (sell) orders at the second-best quote that are modified within one
second of placement, regardless of market condition changes. HFLO2 denotes limit orders at the best quote that are modified within one second of a
15-minute pre-announcement and 15-minute post-announcement periods. HFLO1 denotes limit orders that are cancelled or modified within one
This table reports the average volume of HFLO (Panel A), HFMO (Panel B), as well as abnormal volume of HFLO and HFMO (Panel C) over the
Table 3. HF Market and Limit Orders around News Announcements
Table 4. Impact of i-Cross on HF Trading
This table reports the impact of i-Cross on abnormal total HF activity, abnormal HFLO, abnormal HFMO,
volatility, and term spread. Total HF activity is defined as the sum of HFMO and HFLO volumes. Volatility
is defined as the abnormal absolute price change. Term spread is the difference between the yields of the
2-year note and the 10-year note. We regress each of the variables used in the instrumental variable (IV)
analysis on the i-Cross dummy Qit(j) :
Mit(j) = αi + γit(j) + βQit(j) + εit(j) ,
where Mit(j) denotes abnormal total HF activity, abnormal HFLO, abnormal HFMO, volatility and term
spread; Qit(j) is a dummy variable that equals 0 during the period from January 1, 2006 to November 7,
2007 and 1 after January 1, 2008; αi denotes maturity-specific fixed effect; and γit(j) are minute-of-theinterval dummies capturing seasonal patterns around announcements. The table reports the estimate of β
for each of the regressions. ***, ** and * denote significance at 1%, 5% and 10% levels, respectively.
All Maturities
2-year Note
5-year Note
10-year Note
Abnormal HF
Abnormal HFLO
Abnormal HFMO
Volatility
Term Spread
62.652***
18.652***
115.302***
67.859***
62.402***
18.605***
114.897***
67.677***
0.250***
0.047*
0.406***
0.182*
0.000
-0.000***
0.001
0.000*
0.000
0.001
0.002
0.003
33
Table 5. HF Trading and Market Liquidity
This table reports the results of the second-stage regression in the instrumental variable (IV) analysis:
d it(j) + β2 HF
d it(j) × |SU R| + ϕ0 Cit(j) + ηit(j) ,
Xit(j) = αi + γit(j) + β1 HF
where Xit(j) denotes liquidity variables, i.e., bid-ask spread, depth at best quotes, and depth behind best quotes;
d it(j) is the predicted value of HF trading from Equation (6); and Cit(j) denotes a set of control variables
HF
including term spread, volatility and absolute news surprise (post-announcement period only). Fixed maturity
effects, lagged variables and minute-of-the-interval dummies are included in the regressions but the results are
not reported for brevity. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively, based
on heteroscedasticity and autocorrelation consistent standard errors. Adj.R2 denotes the adjusted R2 .
Pre-announcement
Model 1
Model 2
Panel A: Bid-ask spread
HF
HF × |SUR|
HFLO
HFLO × |SUR|
HFMO
HFMO × |SUR|
|SUR|
Term Spread
Volatility
AdjR2
0.0002***
0.0488
0.1856**
0.2000
Panel B: Depth at Best Quote
HF
0.0038
HF × |SUR|
HFLO
HFLO × |SUR|
HFMO
HFMO × |SUR|
|SUR|
Term Spread
-14.7841
Volatility
12.2082
2
AdjR
0.6463
Model 1a
-0.0000
Post-announcement
Model 1b
Model 2a
-0.0000
-0.0000
-0.0004***
-0.0001*
0.0472***
0.0056*
0.1439
0.1985***
0.2014
0.0048
0.0870*
-0.0322
0.0177
0.0205
0.0859*
-0.0247
0.0176
-0.0284***
-0.0289***
0.0006
0.0043
0.1009**
-0.0301
0.0177
0.1838***
0.0222**
-13.7429***
-3.7954***
-42.8808**
8.4561
0.6478
34
-5.4899**
19.7701*
0.7205
0.4152
Model 2b
-6.0451*
19.8298*
0.4627
0.4149
-5.1294**
10.4344
-0.7057
0.4154
-0.0001
-0.0000
0.0056*
-0.0002
0.0197
0.0996**
-0.0235
0.0177
0.0171
0.0148
-3.5211***
-0.9365
-6.8872*
9.4231
-6.1761
0.4151
Pre-announcement
Model 1
Model 2
Panel C: Depth Behind Best Quote
HF
0.1492***
HF × |SUR|
HFLO
0.5669***
HFLO × |SUR|
HFMO
-31.6159***
HFMO × |SUR|
|SUR|
Term Spread
4.0905
-60.9855
Volatility
35.5218
26.8384
2
AdjR
0.9364
0.9367
Model 1a
-0.3350***
Post-announcement
Model 1b
Model 2a
-0.2872***
-0.1172***
-0.8464***
37.5941***
-58.1093***
105.3344***
-9.7682
0.8473
Panel D: P-value of the First-stage F-test
Pre-announcement
HF
< 0.0001
HFLO
< 0.0001
HFMO
< 0.0001
52.0439***
98.1415***
43.4110
0.8481
-61.7546***
199.6547***
4.7190
0.8485
Post-announcement
< 0.0001
< 0.0001
< 0.0001
35
Model 2b
-0.6710***
-0.4809***
29.9662***
23.8111***
80.0631***
218.0802***
191.1228***
0.8497
Table 6. HF Trading and Price Efficiency
This table reports the results of the second-stage regression in the instrumental variable (IV) analysis:
d it,5M (i+1) + β2 HF
d it,5M (i+1) × |SU R| + ϕ0 Cit,5M (i+1) + ηit,5M (i+1) ,
|ACt,5M (i+1) | = αi + γit,5M (i+1) + β1 HF
where |ACt,5M (i+1) | is the absolute autocorrelation of tick-by-tick log returns based on the mid-quote at each transaction
d it,5M (i+1) is the predicted value of HF trading from Equation (9) as discussed in the
over the five-minute interval, HF
main text and Cit(j) is a set of control variables including term spread, volatility and absolute announcement surprise.
Fixed maturity effects, lagged variables and minute-of-the-interval dummies are included but the results are not reported
for brevity. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively, based on heteroscedasticity
and autocorrelation consistent standard errors. Adj.R2 denotes the adjusted R2 .
HF
HF × |SUR|
HFLO
HFLO × |SUR|
HFMO
HFMO × |SUR|
|SUR|
Term Spread
Volatility
Adj.R2
Pre-announcement
Model 1
Model 2
-0.0050***
0.1253
-1.1214
0.0129
-0.0012
Post-announcement
Model 1b
Model 2a
-0.0010***
0.0001***
0.0043***
-0.3529***
-0.3745***
-0.1389
-2.2221
0.0144
36
Model 1a
-0.0010***
0.0001***
0.7906
0.3563
0.0193
0.0001***
0.7906
0.3563
0.0191
0.0001***
0.3630
-0.8597
0.0255
Model 2b
0.0043***
0.0001***
-0.3745***
0.0001***
0.0001***
0.3630
-0.8597
0.0251
Table 7: HF Trading and Market Liquidity: Important Announcements
This table reports the results of the second-stage regressions in the instrumental variable (IV) analysis specified
in Table 5 based on the list of important announcements. They include CPI, Change in Nonfarm Payroll, Initial
Jobless Claims, Consumer Confidence Index, GDP Advance, ISM Services Index and Retail Sales. The same
control variables are included. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively,
based on heteroscedasticity and autocorrelation consistent standard errors. Adj.R2 denotes the adjusted R2 .
Pre-announcement
Model 1
Model 2
Panel A: Bid-ask Spread
HF
0.0006***
HF × |SUR|
HFLO
-0.0003**
HFLO × |SUR|
HFMO
0.0710***
HFMO × |SUR|
|SUR|
AdjR2
0.3216
0.3238
Panel B: Depth at Best Quote
HF
0.0059
HF × |SUR|
HFLO
0.2303***
HFLO × |SUR|
HFMO
-17.3185***
HFMO × |SUR|
|SUR|
Adj.R2
0.6406
0.6432
Panel C: Depth behind Best Quote
HF
0.1789***
HF × |SUR|
HFLO
0.6306***
HFLO × |SUR|
HFMO
-34.3288***
HFMO × |SUR|
|SUR|
AdjR2
0.9447
0.9449
Panel D: P-values of the First-stage F-test
Pre-announcement
HF
< 0.0371
HFLO
< 0.0286
HFMO
< 0.0001
37
Model 1a
0.0000
Post-announcement
Model 1b
Model 2a
0.0001**
-0.0000
-0.0001
0.0111*
0.0228
0.0280
0.0479*
0.0281
-0.0151**
-0.0163**
0.0028
0.0225
0.0282
0.0444**
-4.3169***
-6.3678
0.4282
-9.6167
0.4282
-0.3281***
-0.2949***
-0.0816***
-6.1847
0.4285
-0.8408***
36.5238***
-95.5830***
0.8530
Model 2b
-0.9324
0.8539
-97.5239***
0.8542
Post-announcement
< 0.0001
< 0.0001
< 0.0001
-0.0001
-0.0001
0.0104*
0.0025
0.0533*
0.0282
0.0301
0.0317*
-3.5479**
-1.8097
-12.8801**
0.4286
-0.7289***
-0.3074***
32.3008***
13.9365***
25.6217
0.8554
Table 8. HF Trading and Price Efficiency: Important Announcements
This table reports the results of the second-stage regression in the instrumental variable (IV) analysis as
specified in Table 6 based on the list of important announcements (see Table 7). The same control variables
are included. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively, based on
heteroscedasticity and autocorrelation consistent standard errors. Adj.R2 denotes the adjusted R2 .
HF
HF × |SUR|
HFLO
HFLO × |SUR|
HFMO
HFMO × |SUR|
|SUR|
Adj.R2
Pre-announcement
Model 1
Model 2
-0.0042***
0.0114
-0.0019
Post-announcement
Model 1b
Model 2a
0.0002
0.0001***
0.0030**
-0.1984
-0.1885**
0.0117
38
Model 1a
0.0002
0.0001***
0.0118
0.0001***
0.0113
0.0001***
0.0138
Model 2b
0.0030**
0.0001***
-0.1885**
0.0001***
0.0001***
0.0129
39
Panel A: Limit orders
HFLO1
HFLO2
HFLO3
All HFLO
Panel B: Market orders
HFMO
Panel C: Abnormal orders
abnormal HFLO
abnormal HFMO
14622.76
225.75
253.80
15102.32
414.68
7537.72
212.93
211.34
-517.32
3.30
2-year
Post-ann
6291.47
120.95
117.04
6529.46
Pre-ann
−
Allpo
Allpr
0.68***
0.47***
HF po
HF pr
denote significance at 1%, 5% and 10% levels, respectively.
-799.97
-8.03
231.79
6547.90
100.35
111.01
6759.26
Pre-ann
7070.98
198.08
438.57
14692.69
168.99
187.13
15048.81
5-year
Post-ann
−
Allpo
Allpr
0.27***
0.51***
HF po
HF pr
-791.47
-7.43
195.40
4899.76
82.48
90.60
5072.84
Pre-ann
5853.34
171.41
374.15
11885.38
128.63
150.50
12164.52
10-year
Post-ann
−
Allpo
Allpr
0.24***
0.58***
HF po
HF pr
each day ( All
Allpr ). The table reports average differences between these two ratios for both limit orders and market orders (trades). ***, **, and *
po
period each day ( HF
HF pr ) and the ratio of total trading volume during the post-announcement period versus that during the pre-announcement period
po
of the main text. We also calculate the ratio of the HF trading volume during the post-announcement period versus that during the pre-announcement
placed within 200ms of a change in the best quotes on either side of the market. Abnormal HFMO and HFLO are defined as in Equations (2) and (3)
at the second-best quote that are modified within 200ms of a change in the best buy (sell) quote. HFMO denotes market buy (sell) orders that are
at the best quote that are modified within 200ms of a change in the best quote on either side of the market. HFLO3 denotes limit buy (sell) orders
denotes limit orders that are cancelled or modified within 200ms of placement, regardless of market condition changes. HFLO2 denotes limit orders
milliseconds as the cutoff in classifying HF activities over the 15-minute pre-announcement and 15-minute post-announcement periods. HFLO1
This table reports the average volume of HFLO (Panel A), HFMO (Panel B), as well as abnormal volume of HFLO and HFMO (Panel C) using 200
Table 9. HFMO and HFLO Identification: Alternative Classification
Table 10. The Workup Process and Price Efficiency
This table reports the results of the second-stage regression in the instrumental variable (IV) analysis:
d it,5M (i+1) + β2 HF
d it,5M (i+1) × |SU R| + ϕ0 Cit,5M (i+1) + ηit,5M (i+1) ,
|ACt,5M (i+1) | = αi + γit,5M (i+1) + β1 HF
where |ACt,5M (i+1) | is the absolute autocorrelation of tick-by-tick log returns based on the mid-quote at each transaction,
d it,5M (i+1) is the predicted value
excluding transactions involved in a workup process, over the five-minute interval; HF
of HF activities from Equation (9); as discussed in the main text and Cit(j) is a set of control variables including term
spread, volatility and absolute announcement surprise (post-announcement period only). Fixed maturity effects, lagged
variables and minute-of-the-interval dummies are included but the results are not reported for brevity. ***, ** and *
denote significance at the 1%, 5% and 10% levels, respectively, based on heteroscedasticity and autocorrelation consistent
standard errors. Adj.R2 denotes the adjusted R2 .
HF
HF × |SUR|
HFLO
HFLO × |SUR|
HFMO
HFMO × |SUR|
|SUR|
Term Spread
Volatility
Adj.R2
Pre-announcement
Model 1
Model 2
-0.0049***
0.4167
-0.0022
0.0111
-0.0049***
Post-announcement
Model 1b
Model 2a
-0.0014***
0.0001***
0.0036***
-0.0120*
-0.3568***
0.4119
-0.0024
0.0109
40
Model 1a
-0.0014***
0.0001***
0.4278
-0.0073
0.0215
0.0001***
0.4278
-0.0073
0.0213
0.0001***
0.0203
-0.0189
0.0279
Model 2b
0.0036***
0.0001***
-0.3568***
0.0001***
0.0001***
0.0203
-0.0189
0.0275
Figure 1. Liquidity around News Announcements
This figure depicts average relative spread (×10,000), depth at best bid and ask ($mil) and depth
behind the best bid and ask ($mil) for the 2-year note in each 1-minute interval during the 15-minute
pre-announcement and 15-minute post-announcement periods. The sample period is from January 3,
2006 to December 29, 2011. For comparison, corresponding values of each variable at the same time
interval on non-announcement days are also depicted.
Relative spread
3.5
3
Ann Days
Non−Ann Days
2.5
2
1.5
1
0.5
0
−15
−10
−5
0
5
10
15
5
10
15
5
10
15
Depth at best quote
1000
800
Ann Days
Non−Ann Days
600
400
200
0
−15
−10
−5
0
Depth behind best quote
8000
Ann Days
Non−Ann Days
6000
4000
2000
0
−15
−10
−5
0
41
Figure 2. HF Trading Over Time
This figure plots the ratio of overall HF volume, defined as the sum of HFMO and HFLO volumes,
relative to the overall volume of all market and limit orders each month for the 2-, 5- and 10-year
notes during the sample period from January 3, 2006 to December 29, 2011.
0.6
0.5
0.4
0.3
0.2
2‐year
5‐year
10‐year
0.1
42
2011.10
2011.07
2011.04
2011.01
2010.10
2010.07
2010.04
2010.01
2009.10
2009.07
2009.04
2009.01
2008.10
2008.07
2008.04
2008.01
2007.10
2007.07
2007.04
2007.01
2006.10
2006.07
2006.04
2006.01
0
Figure 3. HF Trading around News Announcements
This figure depicts the HFMO and HFLO volumes of the 2-year note during the 15-minute preannouncement and 15-minute post-announcement periods. For comparison, corresponding HFMO
and HFLO volumes at the same time interval on non-announcement days are also depicted.
HFMO
60
Ann. Days
50
Non. Days
40
30
20
10
0
‐15 ‐13 ‐11
‐9
‐7
‐5
‐3
‐1
1
3
5
7
9
11
13
HFLO
4500
Ann. Days
4000
Non. Days
3500
3000
2500
2000
1500
1000
500
0
‐15 ‐13 ‐11
‐9
‐7
‐5
‐3
‐1
43
1
3
5
7
9
11
13