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High Frequency Trading in the US Treasury Market

– Evidence around Macroeconomic News Announcements

This version: June 2012

High Frequency Trading in the US Treasury Market

– Evidence around Macroeconomic News Announcements

Abstract

This paper investigates the role and effect of high frequency (HF) trading in the US

Treasury market around major macroeconomic news announcements. Based on

tick-by-tick activities from a major interdealer trading platform, we identify HF trades and

orders in the US Treasury market over the period of January 2004 to June 2007. Our results

show that both HF trades and orders increase substantially following news announcements.

In particular, more HF limit orders are placed following announcement with unexpected

high surprises. Moreover, while HF limit orders help to reduce the bid-ask spread, there is

no evidence that they help to improve the order book depth at best quotes. Finally, we

provide clear evidence that HF limit orders are less informative than manual orders placed

following news announcements.

JEL Classification: F3, G12, G14, G15.

Keywords: High Frequency Trading; Market Liquidity; Price Discovery; US Treasury Market.

I. Introduction

Automated trading or high frequency (HF) trading, carried out by computer programs, has

become prevalent in financial markets during the past decade.1 As reported in financial press, in

recent years trading records are routinely broken and millions of data messages are regularly sent

per second to various trading venues2. This anecdotal evidence is coupled with the hard fact that,

in only ten years, trading latency in several markets has decreased by two orders of magnitude

(Moallemi and Saglam, 2011) and trading, as well as quoting activity, takes place within a fraction

of a second (see, inter alia, Clark, 2011; Hasbrouck, 2012; Scholtus and Van Djik, 2012 and the

references therein). Although HF trading is one of the most significant market structure

developments in recent years (SEC, 2010), the role and effect of these activities on market

liquidity and price efficiency is still a relatively unexplored topic in the finance literature.

A stream of very recent studies has begun to investigate the impact of HF trading in equity

markets (see, inter alia, Hendershott et al., 2010; Hasbrouck and Saar, 2011; Brogaard, 2011a;

2011b; 2012; Hendershott and Riordan, 2011; Egginton et al., 2012; Bohmer et al., 2012 and the

references therein). However, very little research has been carried out to understand the impact of

HF activities in other important security markets.3 Another important but relatively unexplored

question is how HF trades and quotes react to information arrival. One unique feature of HF trade

and quotes is its speed in reaction to information arrival. With its quick reaction speed and

capacity to handle a large amount of information, it remains an open question whether HF

activities improve or deplete liquidity of the market and whether they help or hinder price

1 From a semantic viewpoint, HF trading can be seen a subset of market activities carried out by computers labelled

Algorithmic Trading, or AT (Chlistalla, 2011). Since this article focuses on the trading activities that are carried out

by machines at a very high speed, we will refer to these activities as HF trades and orders.

2 See “Speed and market complexity hamper regulation” Financial Times, 7

th October, 2011.

3 The only exception is represented by Chaboud et al. (2009) who investigate the role of algorithmic trading in the

foreign exchange market.

discovery around information arrival.

In this paper, we explore HF activities around macroeconomic news announcement in the US

Treasury market. The US Treasury market is one of the world’s largest financial markets with a

daily trading volume that rivals the US equity market. According to Aite Group (2008), the

introduction of Electronic Communication Networks (ECNs) in the early 2000s has encouraged

the establishment and development of HF trading and quoting activities in the US Treasury

secondary markets. Very recently ICAP, one of the major brokers in this market, quantifies that in

2009 more than 50 percent of their bids and offers are “black-box-oriented” and 45 percent of the

overall trading in US Treasuries over their ECN, BrokerTec, is due to algorithm-based trading

(Kite, 2010). Given the increasingly large role of HF trading and quoting, we fill an important gap

in this literature and study the role of HF activities on market quality and the price discovery

process in the US Treasury secondary market.

More importantly, we focus on the impact of HF trades and orders around the times of major

information arrival, macroeconomic news release. As Treasury securities have fixed cash flows,

macroeconomic news announcements contain public information that is most relevant to valuation

of Treasury securities. The advantage of using macroeconomic news release is that forecasts of

upcoming announcement are readily available and thus we can explicitly measure public

information shock. This allows us to directly investigate whether and how algorithm reacts to the

extent of public information shock in terms of announcement surprise. Furthermore, around the

time of announcement, market conditions, such as bid-ask spread and market depth also,

experience drastic changes. The change in information environment around announcements

provides a clean and unique setting to explore how HF activities relate to various market variables.

The dataset used in our study is obtained from BrokerTec, a major ECN for the trading of

on-the-run US Treasury notes and bonds, and contains tick-by-tick information regarding all

transactions and quotes over the period of January 2004 – June 2007. It contains trading activities

and order book information for 2-, 5- and 10-year notes. We propose a procedure to identify HF

transactions and orders that are placed or modified at a speed beyond human capacity.

With identified HF trades and orders, we address the following issues. First, given the

relevance of macroeconomic variables in driving the price of Treasury securities (see, inter alia,

Fleming and Remolona 1997; 1999; Balduzzi et al., 2001; Andersen et al., 2003; 2008 and

Hoerdahl et al., 2012; Menkveld et al., 2012 and the references therein), we investigate how HF

activities take place around macroeconomic news announcements. Following the arrival of new

information, market participants have to assess quickly the impact of different shocks and,

consequently, place new or modify existing orders. The recent development of automated news

service such as Reuters NewsScope allows computer programs to react and take position in the

market within a millisecond of news announcement. One of the questions studied in this paper is

how HF activities react to and evolve with the arrival of public information.

Second, we examine whether HF activities improve or deplete market liquidity. As HF trades

and quotes respond to information shocks quickly and potentially with (cumulated) large volumes,

it is important to understand whether in the US Treasury market HF activities are likely to supply

or absorb liquidity in different market conditions.

Third, as a logical extension of the analysis of HF activities around macroeconomic news

announcements, we investigate the informativeness of HF trades and orders relative to manual

orders. In addition, we are interested in whether HF trading and quoting facilitate or hinder the

Treasury securities’ price discovery process.

Our results show that both HF trades and orders increase substantially immediately following

macroeconomic news announcements. In particular, HF orders are placed and cancelled much

more frequently during the 15-minute post-announcement period. Moreover, there are more HF

orders placed following announcements with bigger surprises. There are also significantly more

HF trades of the 2-year note associated with bigger announcement surprises. The findings suggest

that HF trades and orders react to both the arrival and magnitude of shock in public information.

Second, we provide mixed evidence on whether HF trading activities improve or deplete

market liquidity. We show that, as expected, market orders or trades tend to deplete market

liquidity, whereas market orders tend to supply market liquidity. Specifically, trades tend to widen

bid-ask spread, especially during the 15-minute pre-announcement period, and reduce market

depth at best quotes. The later effect is highly significant during both the 15-minute pre-

announcement and the 15-minute post-announcement periods. On the other hand, limit order

submissions tend to narrow bid-ask spread and increase market depth at best quotes. These effects

are highly significant during both the 15-minute pre- announcement and the 15-minute

post-announcement periods. Further examining the effect of HF trades on market liquidities, we

find no evidence that a higher percentage of HF trades leads to further higher bid-ask spread. This

is true during both the pre- and post-announcement periods. Similarly, we find no evidence that a

higher percentage of HF trades further depletes market depth at best quotes. Again, this holds

during both the pre- and post-announcement periods. The findings on the effect of HF orders on

market liquidity are interesting. Our results show that while a higher percentage HF limit orders

tends to further help reducing the bid-ask spread, a higher percentage HF limit orders does not

further improve market depth at best quotes. Instead, our results show that a higher percentage HF

limit orders has a negative effect on market depth at best quotes. This seems to suggest that HF

limit orders tend to be conservative and a higher percentage of HF limit orders in the order book

may subsequently cause other dealers cancel orders at best quotes. The results are similar to

Hendershott et al. (2010) in the context of the US equity market, which also record that the impact

of a higher message traffic on the depth of the order book is negative.

Finally, we employ the test proposed in Kaniel and Liu (2006) to examine whether HF trades

and orders are more informative than manual orders. The test provides mixed findings across

different maturities during the pre-announcement period. While there is evidence that HF market

orders of the 10-year note are more informative, HF limit orders of 2-year notes are more

informative. The evidence is rather consistent across different maturities during the

post-announcement period. There is clear evidence that HF limit orders are less informative than

manual orders, but HF market orders are more informative than manual orders although the

evidence is slightly weaker. In addition, we further examine the effect of HF trades and orders on

subsequent absolute mid-quotes serial correlation, a price efficiency measure proposed by

Boehmer and Kelley (2010) and Boehmer et al. (2012). The results provide no consistent evidence

that HF trades and orders either help to facilitate or hinder the price discovery process of Treasury

securities.

Our research is closely related to recent studies that have explored the role and impact of HF

trades and orders to financial markets from a theoretical and empirical point of view. From a

theoretical perspective, Biais, Homber and Weill (2010) show that HF trading improves the traders’

ability to respond to new information and thus improves informational efficiency in the market.

Along the same lines, Biais, Foucault and Moinas (2010) show that HF activities reduce search

cost but they induce adverse selection in equilibrium since machines process information faster

than slow traders. Similar to Biais, Foucault and Moinas (2010), Javanovic and Menkveld (2011)

find HF activities induce adverse selection in terms of their speed of reaction to market events.

From an empirical point of view, various studies explore the rise and development of HF activity

in equity and foreign exchange markets. Two issues are mainly investigated: i) the impact of HF

activities on market liquidity and volatility and ii) the role of HF activities on price discovery. The

first group of studies records that, on average, HF activities improve market liquidity (Hendershott

et al., 2011; Brogaard, 2011; 2012a; Hasbrouck and Saar, 2011; Chaboud et al., 2009). However,

Boehmer et al. (2012) also suggest that HF activities reduce liquidity in small stocks and when

market making is difficult. With regards to the issue of HF activities and price discovery, Chaboud

et al. (2011) find evidence that, in foreign exchange markets, transactions carried out by machines

are less informative than the ones generated by human traders. On the contrary, Hendershott and

Riordan (2010) find that computer quoting activity improves significantly the price discovery

process by contributing more to innovations to efficient prices in NASDAQ. Similarly, Brogaard

(2010) finds that HF activity has greater price impact and leads price discovery.

Our study is also related to the vast literature on macroeconomic news announcements and

price movements in 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 et al. (2001), Fleming and

Remolona (1999), Green (2004) and Hoerdahl et al. (2012) point out that the price discovery

process of bond prices mainly occurs around major macroeconomic news announcements and the

same announcements are responsible for changes in risk premia across different maturities.

Menkveld et al. (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. However, the existing literature uses data sample before the introduction of electronic

limit order book, during which automatic trading does not exist. With the U.S. Treasury market

transition to an electronic order driven market during the late 90s (detailed in Fleming and

Mizrach (2009) and Mizach and Neely (2009), this paper is thus among the first in studying how

HF activities react to the arrival of public information and its impact in the Treasury market.

The reminder of the article is as follows: Section 2 introduces the dataset employed in the

empirical analysis and describes in detail the procedure used to compute the variables associated

with HF intensity. Section 3 discusses the empirical results and a final section concludes.

II. Data and HF Trades and Orders

II.1 Data

The data on US Treasury securities used in this article is obtained from BrokerTec, an

interdealer ECN in the secondary wholesale US Treasury securities market. Prior to 1999, the

majority of interdealer trading of US Treasuries occurred through interdealer brokers. After 1999,

two major ECNs emerged: eSpeed and BrokerTec. Since then, the trading of on-the-run Treasuries

has migrated to the electronic platforms (Mizrach and Neely, 2009; Fleming and Mizrach, 2009).4

In our empirical investigation we use data on 2-, 5- and 10- year Treasury notes from the

BrokerTec limit order book over the period January 5th

, 2004 to June 29th

, 2007. The data set

contains the tick-by-tick observations of transactions, order submissions, and order cancellations.

It includes the time stamp of transactions and quotes, the quantity entered and/or deleted, the side

of the market and, in the case of a transaction, an aggressor indicator.

The data on macroeconomic news announcements and the survey of market participants are

obtained from Bloomberg. Balduzzi et al. (2001) show that professional forecasts based on

surveys are neither biased nor stale. In our empirical investigation we use a set of 17 key

macroeconomic news announcements at 8:30 a.m. ET from Pasquariello and Vega (2007). In line

with much empirical literature on announcement news and asset prices (see Andersen et al. 2003;

4 According to Barclay et al. (2006), the electronic market shares for the 2-, 5- and 10-year bond are, respectively,

75.2%, 83.5% and 84.5% during the period of January 2001 to November 2002. By the end of 2004, the majority of

secondary interdealer trading occurred through ECNs with over 95% of the trading of active issues. BrokerTec is

more active in the trading of 2-, 3-, 5- and 10-year Treasuries, while eSpeed has more active trading for the 30-year

maturity.

2007 and the references therein), we construct standardized news surprises to measure public

information shocks as follows:

where is the actual value of announcement k on day t, is the median forecast of the

announcement k on day t and is the time-series standard deviation of . The full list

of macroeconomic news announcements, together with some descriptive statistics computed over

the sample period, is reported in Table 1.

The summary statistics of the trading activities around news announcements are reported in

Table 2. Panel A reports the results during the pre-announcement period (8:15 am to 8:30 am), and

Panel B reports the results during the post-announcement period (8:30 am to 8:45 am). The results

show that during the pre-announcement period, the 2-year note is the most liquid market followed

by the 5-year note and then the 10-year note. In fact, the 2-year note records the largest trading

volume, the deepest depth of the order book (at both the best quotes and overall) and the smallest

spread. The other two maturity tenors are not very different from each other in that they record

comparable levels of trading volumes, depths and spreads. The patterns highlighted in Panel A are

also confirmed in Panel B, where the variables of interest are recorded during the 15-minute

interval following major macroeconomic news announcements.

We construct abnormal values for those variables for use in the subsequent empirical analysis

in order to take into account for potential time-series effects that might have occurred over the

sample period. More specifically, we define abnormal spread, 𝑃 𝐷∗, as

𝑃 𝐷 1𝑀(𝑖)∗ 𝑃 𝐷 1𝑀( 𝑖)

1

5∑ [

1

30 ∑ 𝑃 𝐷 − 1𝑀(𝑖−𝑗)

𝑁𝑂𝑁30𝑗=1 ]5

=1 (1)

where 𝑃 𝐷 1𝑀( 𝑖) denotes the average bid-ask spread within i-th minute interval on

announcement day t and 𝑃 𝐷 − 1𝑀(𝑖−𝑗) 𝑁𝑂𝑁 denotes the average bid-ask spread in the past t-k

no-event day during (i-j)th intervals, where j=0,1,…30 and k=1,…,5. Similarly, abnormal depth

measures are computed as

𝐷𝑃𝑇𝐻 1𝑀(𝑖)𝐵𝑆𝑇∗ 𝐷𝑃𝑇𝐻 1𝑀(𝑖)

𝐵𝑆𝑇 1

5∑ [

1

30 ∑ 𝐷𝑃𝑇𝐻 − 1𝑀(𝑖−𝑗)

𝐵𝑆𝑇 𝑁𝑂𝑁30𝑗=1 ]5

=1 , (2)

𝐷𝑃𝑇𝐻 1𝑀(𝑖)𝐴𝐿𝐿∗ 𝐷𝑃𝑇𝐻 1𝑀(𝑖)

𝐴𝐿𝐿 1

5∑ [

1

30 ∑ 𝐷𝑃𝑇𝐻 − 1𝑀(𝑖−𝑗)

𝐴𝐿𝐿 𝑁𝑂𝑁30𝑗=1 ]5

=1 , (3)

where 𝐷𝑃𝑇𝐻 1𝑀(𝑖)𝐵𝑆𝑇∗ , 𝐷𝑃𝑇𝐻 1𝑀(𝑖)

𝐴𝐿𝐿∗ denote abnormal depth recorded at the best quote and the

overall depth, respectively and 𝐷𝑃𝑇𝐻 1𝑀(𝑖)𝐵𝑆𝑇 , 𝐷𝑃𝑇𝐻 1𝑀(𝑖)

𝐴𝐿𝐿 are the average of the visible depth at

the best quote and the overall depth recorded within i-th minute interval on announcement day t.

Finally, abnormal volatility is defined as,

𝑉𝑂𝐿 1𝑀(𝑖)∗ 𝑉𝑂𝐿 1𝑀(𝑖)

1

5∑ [

1

30 ∑ VOLA − 1𝑀(𝑖−𝑗)

𝑁𝑂𝑁30𝑗=1 ]5

=1 , (4)

where 𝑉𝑂𝐿 1𝑀(𝑖) denotes volatility over i-th minute interval on announcement day t. For each

order, we calculate the most recent change in mid-quote right before the order was submitted.

VOLA is calculated as the average of absolute change in mid-quote corresponding to all orders

within i-th minute interval on announcement day t.

The behavior of these trading variables on announcement and non-announcement days are

plotted in Figure 1. Bid-ask spreads are higher on announcement days vis-à-vis non announcement

days. On announcement days, the change in spreads occurs at the announcement time and the

value of the spreads reverts to pre-announcement values almost instantaneously.

The depth of the order book (both at the best quote and overall), decrease substantially on

announcement days around announcement times. Different from bid-ask spreads, the decline is

more pronounced for the 2-year note than the 5- and 10-years notes. In fact, around announcement

times the average depth of the limit order book for the 2-year note decreases between 50 percent

(overall) and 80 percent (best quote). However, in both cases the recovery occurs within 10

minutes from the announcement time. With regards to trading volume, as in much empirical

literature on trading and macroeconomic news announcements (Fleming and Remolona, 1997;

1999), on announcement days trading volume is higher and the difference peaks around

announcement times for all maturity tenors. However, the difference is larger for the 2-year note

than for the other two bond maturities.

II. Identification of HF Trades and Orders

Our dataset does not contain information about whether a trade or order is placed through

computer program or manually. However, the dataset records reference numbers that provide

information on the submission timing of an order and its subsequent alteration, cancellation or

execution. Using this useful piece of information, we identify high frequency (HF) activity by

looking at the reaction time of order and transactions to changes in market conditions. We select

those trades/orders that are placed to the market at a speed deemed beyond human reaction. More

specifically we label trades and orders as originating from computers if the following conditions

apply:

High Frequency Market order (HFMO) – if a market buy (sell) order is placed to hit the

best ask (bid) quote within a second of the changes of the best quotes

High Frequency Limit order

o HFLO1 – if a limit order at the best quote is modified within one second of changes

in best quotes on the same side of the market

o HFLO2 –if a limit order at the best quote is modified within one second of changes

in best quote on the opposite side of the market

o HFLO3 –if a limit buy (sell) order placed at the second best quote is modified

within one second of the changes of the buy (sell) best quotes.

o HFLO4 –if a limit order is cancelled or modified within one second of its

placement regardless of market condition changes.

We note that the procedure described above is designed to infer HF activities on the basis of

the speed at which trades are executed and orders are submitted to or withdrawn from the market.

However, some caveats are in order. First, manual orders can be mistakenly identified as HF

orders if manual orders are placed earlier but arrive exactly one second before market conditions

change. As a consequence, some manual trades and orders may be misidentified as HF trades and

orders. Second, while tick-by-tick data is available in our dataset, we are cautious about using

ultra high frequency data because of the concerns of market microstructure effects. In fact, due to

discrete tick size, market microstructure noise may be aggravated as the sampling frequency

increases. Therefore, we aggregate our data at the 1-minute frequency, consistent with existing

empirical studies (see, inter alia, Fleming and Remolona, 1999; Balduzzi et al., 2001 and the

references therein).

Having constructed orders and trades that are likely to be generated by computers, we define

a measure of abnormal HF activities around macroeconomic news announcements. Similar to

liquidity variables, the abnormal values are computed as the differences between actual HF trades

and orders during the time interval and ‘normal’ HF trades and orders, where the latter are defined

as the average one-minute HF trades and orders over the past 5 days with no major news

announcements or economic events.

More formally, we define abnormal HF trades in the i-th minute interval on day t as

𝐻𝐹 1𝑀(𝑖)𝑇𝑅𝐴𝐷𝐸∗ 𝐻𝐹 1𝑀(𝑖)

𝑇𝑅𝐴𝐷𝐸 1

5∑ [

1

30 ∑ 𝐻𝐹 − 1𝑀(𝑖−𝑗)

𝑇𝑅𝐴𝐷𝐸 𝑁𝑂𝑁30𝑗=1 ]5

=1 , (5)

where 𝐻𝐹 1𝑀(𝑖)𝑇𝑅𝐴𝐷𝐸 is the number HF trades within the i-th minute interval on announcement day t,

𝐻𝐹 − 1𝑀(𝑖−𝑗) 𝑇𝑅𝐴𝐷𝐸 𝑁𝑂𝑁

denotes the HF trades identified in the past t-k no-event day during (i-j)th intervals,

where j=0,1,…30 and k=1,…,5. The term 1

5∑ [

1

30 ∑ 𝐻𝐹 − 1𝑀(𝑖−𝑗)

𝑇𝑅𝐴𝐷𝐸 𝑁𝑂𝑁30𝑗=1 ]5

=1 denotes the average of

past HF trades recorded over the past five no-event days during the 30-minute interval prior to the

i-th minute interval.

Similarly, we define abnormal HF orders in the i-th minute interval on announcement day t as

𝐻𝐹 1M(𝑖)𝑂𝑅𝐷𝐸𝑅∗ 𝐻𝐹 1𝑀(𝑖)

𝑂𝑅𝐷𝐸𝑅 1

5∑ [

1

30 ∑ 𝐻𝐹 − 1𝑀(𝑖−𝑗)

𝑂𝑅𝐷𝐸𝑅 𝑁𝑂𝑁30𝑗=1 ]5

=1 , (6)

where 𝐻𝐹 1𝑀(𝑖)𝑂𝑅𝐷𝐸𝑅 denotes the number of HF orders within the i-th minute interval on day t .

Equations (5) and (6) define measures of HF intensity that take into account potential time trends

and intraday seasonality in HF trades and orders and the normal trading activities of days with no

major news events. In essence, abnormal HF activity describes how HF activity, computed over a

1-minute interval, deviates from its average of the previous 5 days without prescheduled

announcements and without significant events during the previous 30-minute intervals.

A summary of identified high frequency trades and orders is reported in Table 3. Panel A

reports the average number of HF trades (inferred from market orders) over a 1-minute interval

over the full sample period and across different macroeconomic news announcements. For all

maturity tenors, average HF trades range between 13 (38) and 35 (87) per minute before (after)

news announcements. The HF intensity seems more pronounced during the 15 minutes after the

announcement with a value that is 2 to 3 times larger than that prior to news announcements.

Panel B shows the average number HF orders which are consistent with the filter discussed

earlier in this Section. The values are disaggregated by the type of filter (HFLO1 to HFLO4) and

then aggregated (all HF orders). Across different types of potentially HF orders, limit orders that

are cancelled or modified within one second of their placement regardless of market condition

changes (HFLO4) exhibit the largest number. However, regardless of the filter applied and across

different maturity tenors, even the figures reported in Panel B confirm that HF quoting activity is

larger after macroeconomic news announcements with multipliers that range between 3 and 4

times the numbers of HF orders before the same announcements. If we take into account potential

time trends and seasonality in computing the figures in Panels A and B and compute abnormal HF

orders and trades as in Equations (1) and (2), the difference between pre and post announcement

period, reported in Panel C, is even more striking.

The results are corroborated by the patterns plotted in Figure 2. In fact, across all maturities,

HF orders and trades are substantially larger at and after the announcement time than the period

preceding the announcements. The shift is larger for the 5- and 10-year notes which record

increments of 500 percent for HF orders and trades at the announcement time in comparison to

pre-announcement periods. The 2-year note, although smaller in magnitude, records increments of

200-300 percent at the announcement time. In all cases the levels of HF orders and trades revert to

their pre-announcement levels within 15 minutes after the announcement time but taking into

account of “normal” market conditions, the abnormal HF orders and trades remain higher than that

of pre-announcement period during the 15 minute interval after announcement

III. Empirical Analysis

In this section, we address the following issues around public information arrival: i) the use

or occurrence of HF activity under different market conditions, ii) the effect of HF trades and

orders on subsequent market liquidity, and iii) the informativeness of HF trades and orders as well

as the effect of HF trades and orders on market efficiency.

III.1. HF Trading Activities and Market Conditions

The first issue we investigate relates to the relation between market conditions and HF

intensity during the pre- and post-announcement period. Put differently, under which

circumstances do HF trades and orders occur more often? We address this issue by regressing the

abnormal levels of HF trades and orders against various variables related to market conditions. We

separate the analysis for the pre- and post-announcement period and run the following panel

regression for both abnormal HF trades and orders. In the former case, the equations we estimate

are as follows:

HF 1𝑀(𝑖)TRADE∗ 𝛼 + 𝛽1𝑉𝑂𝐿 1𝑀(𝑖)

∗ + 𝛽2DPTH 1𝑀(𝑖)𝐵𝑆𝑇∗ + 𝛽3DPTH 1𝑀(𝑖)

𝐴𝐿𝐿∗ + 𝛽4SPRD 1M(𝑖)∗ + 𝜀 1M(𝑖)

HF 1𝑀(𝑖)ORDER∗ 𝛼 + 𝛽1𝑉𝑂𝐿 1𝑀(𝑖)


𝐴𝐿𝐿∗ + 𝛽4SPRD 1M(𝑖)∗ + 𝜀 1M(𝑖)

(7)

where the relevant variables are defined as discussed in Section 2. The specification for

post-announcement period is similar to Equations (7), but we also incorporate absolute news

surprises, denoted by | | to control for the effect of public information shock on HF orders

and trades:5

HF 1𝑀(𝑖)TRADE∗ 𝛼 + 𝛽1𝑉𝑂𝐿 1𝑀(𝑖)


𝐴𝐿𝐿∗ + 𝛽4SPRD 1M(𝑖)∗ + 𝛾| |

+ 𝜀 1𝑚(𝑖)

HF 1𝑀(𝑖)ORDER∗ 𝛼 + 𝛽1𝑉𝑂𝐿 1𝑀(𝑖)


𝐴𝐿𝐿∗ + 𝛽4SPRD 1M(𝑖)∗ + 𝛾| |

+ 𝜀 1𝑚(𝑖)

(8)

The results of the regressions are reported in Table 4. Panel A reports the results for the

pre-announcement period and Panel B reports the regression results for the post-announcement

period. The results show that first, HF trades are more likely to occur when market volatility

(VOLA) is high during the pre-announcement period. The relation is rather weak and mixed

during the post-announcement period. Interestingly, during both pre- and post-announcement

periods, HF limit orders more likely occur when market volatility is low. The negative relation

between market volatility and HF limit orders alleviates the concern that our identification

procedure more likely identifies limit orders submitted in a volatile market as HF orders. Second,

while both HF trades and limit orders are positively related to bid-ask spread during the

pre-announcement period, the relations tend to be negative during the post-announcement period.

This suggests that dealers submit more market orders and limit orders when the bid-ask spread is

wide during the pre-announcement period, whereas they tend to submit more market orders and

limit orders when the bid-ask spread is narrow during the post-announcement period. That is,

more HF trades and orders are placed during the pre-announcement period even under less

favorable market liquidity conditions. Third, almost the opposite patterns are observed for the

relation between HF trades and orders and overall depth of the limit order book. That is, while

5 If there are multiple announcements occurring at 8:30 on day t, we use the maximum value of absolute standardized

surprise.

both HF trades and limit orders are positively related to overall depth of the limit order book

during the pre-announcement period, the relations are negative during the post-announcement

period. This suggests that dealers submit more market orders and limit orders when the overall

limit order book is deep during the pre-announcement period, whereas they tend to submit more

market orders and limit orders when the overall limit order book is shallow during the

post-announcement period. Fourth, both HF trades and orders are negatively related to the depth at

the best quotes during the pre- and post-announcement periods. The only exception is HF orders

of the 10-year note during the post-announcement period. This suggests that when there is more

depth at the best quotes, HF trades and orders are more likely used. Finally, the results in Panel B

show an overall positive relation between HF trades and orders with standardized announcement

surprises. The positive relation is highly significant for HF orders. This suggests that more HF

limit orders are placed following announcement with unexpected high surprises.

III.2. The Effect of HF Trades and Limit Orders on Subsequent Market Liquidity

While the results in the above subsection may provide some interesting findings on the use of

HF trades and orders under different market conditions, a potential drawback of the analysis is that

it does not take into account of the interactive relations between HF activities and market

conditions. In this section, we examine the potential impact of HF activities on subsequent market

liquidity. Specifically, we focus on the effect of HF trading activities on bid-ask spread and the

depth standing at the best quotes. We first examine how the number of orders submitted and

transactions affect spread and depth at the best quotes,

𝑃 𝐷 1𝑀(𝑖+1)∗ 𝛼 + 𝜑0𝑂 𝐷 1𝑀(𝑖) + 𝛾0𝑇 𝐷 1𝑀(𝑖) + 𝛽 𝑃 𝐷 1𝑀(𝑖)

∗ + 𝜀 1𝑚(𝑖)

𝐷𝑃𝑇𝐻 1𝑀(𝑖+1)𝐵𝑆𝑇∗ 𝛼 + 𝜑0𝑂 𝐷 1𝑀(𝑖) + 𝛾0𝑇 𝐷 1𝑀(𝑖) + 𝛽𝐷𝑃𝑇𝐻 1𝑀(𝑖)

𝐵𝑆𝑇∗ + 𝜀 1𝑚(𝑖)

(9)

where 𝑂 𝐷 denotes the number of limit orders submitted with the i-th minute interval,

𝑇 𝐷 is the number of market orders submitted within the i-th minute interval and the

dependent variables 𝑃 𝐷∗ and 𝐷𝑃𝑇𝐻𝐵𝑆𝑇∗ are as defined in Section 2. We expect that 𝜑0 < 0

and 𝛾0 > 0 for 𝑃 𝐷∗ since market orders erode depth and widen spread and 𝜑0 > 0 and

𝛾0 < 0 for 𝐷𝑃𝑇𝐻𝐵𝑆𝑇∗ since limit order submission deepens depth of order book and narrows

spread.

Next, we examine the additional impact of HF orders and trades on subsequent market

liquidity. Formally, we estimate the following regressions:

𝑃 𝐷 1𝑀(𝑖+1)∗ 𝛼 + (𝜑0 + 𝜑𝐻𝐹𝑃

𝑂𝑅𝐷𝐸𝑅)𝑂 𝐷 1𝑀(𝑖) + (𝛾0 + 𝛾𝐻𝐹𝑃𝑇𝑅𝐴𝐷𝐸)𝑇 𝐷 1𝑀(𝑖)

+ 𝛽 𝑃 𝐷 1𝑀(𝑖)∗ + 𝜀 1𝑚(𝑖)

𝐷𝑃𝑇𝐻 1𝑀(𝑖+1)𝐵𝑆𝑇∗ 𝛼 + (𝜑0 + 𝜑𝐻𝐹𝑃

𝑂𝑅𝐷𝐸𝑅)𝑂 𝐷 1𝑀(𝑖) + (𝛾0 + 𝛾𝐻𝐹𝑃𝑇𝑅𝐴𝐷𝐸)𝑇 𝐷 1𝑀(𝑖)

+ 𝛽𝐷𝑃𝑇𝐻 1𝑀(𝑖)𝐵𝑆𝑇∗ + 𝜀 1𝑚(𝑖)

(10)

where 𝑃𝑂𝑅𝐷𝐸𝑅 (𝑃𝑇𝑅𝐴𝐷𝐸) indicates the proportion of HF orders (trades) out of the total number of

orders submitted (trades executed). If HF trades and orders improve liquidity, we expect that

𝜑ℎ𝑓 < 0 and 𝛾ℎ𝑓 < 0 for 𝑃 𝐷∗, and 𝜑ℎ𝑓 > 0 and 𝛾ℎ𝑓 > 0 for 𝐷𝑃𝑇𝐻𝐵𝑆𝑇∗. Again, as in

Section III.1, we separate the estimation for the pre- and post-announcement periods to examine

whether the arrival of public information changes the relationship.

The results of the above regressions are reported in Table 5. Panel A reports the results for the

abnormal quoted spread regressions during both pre- and post-announcement periods, and Panel B

reports the results for the abnormal depth at the best quotes regressions during both pre- and

post-announcement periods. The results are summarized as follows. First, both abnormal spreads

and abnormal depth at the best quotes are highly persistent as the coefficients of lagged variables

in their respective regressions are positive and highly significant. It is thus important to include the

lagged abnormal spreads and abnormal depth at the best quotes as control variables in their

respective regressions. Second, as expected market orders or trades tend to widen bid-ask spread,

whereas limit order submissions tend to reduce bid-ask spread. In the abnormal spread regressions,

the coefficient of order submission is significantly negative during both pre- and

post-announcement periods. On the other hand, the coefficient of trades is significantly positive

during the pre-announcement period but insignificant during the post-announcement period. Third,

again, as expected, market orders or trades tend to deplete the depth at best quotes, whereas limit

order submissions tend to improve the depth at best quotes. In the regressions of abnormal depth

at best quotes, the coefficient of order submission is positive during both pre- and

post-announcement periods, and the coefficient of trades is negative during both pre- and

post-announcement periods. In all cases, the coefficients are highly significant.

Next, we examine the additional effect of HF trades and orders on subsequent market

liquidity. As noted earlier, 𝜑𝐻𝐹𝑃𝑇𝑅𝐴𝐷𝐸 captures the additional effect of HF trades on subsequent

market liquidity, whereas 𝜑𝐻𝐹𝑃𝑂𝑅𝐷𝐸𝑅 captures the additional effect of HF order submission on

subsequent market liquidity. In the abnormal spread regressions, 𝜑𝐻𝐹𝑃𝑇𝑅𝐴𝐷𝐸 is negative in all

regressions. That is, HF trades reduce the widening effect of trading on subsequent bid-ask spread.

Nevertheless, the effect is stronger during the post-announcement period as the coefficient is

significant both 2-year and 10-year notes. On the other hand, HF limit orders tend to reinforce the

effect in narrowing subsequent bid-ask spread. The result is rather strong as the coefficient

𝜑𝐻𝐹𝑃𝑂𝑅𝐷𝐸𝑅 is negative and highly significant in five out of six regressions. Now we turn to the

regressions of abnormal depth at best quotes. 𝜑𝐻𝐹𝑃𝑇𝑅𝐴𝐷𝐸 is positive in five of the six regressions.

That is, HF trades tend to reduce the depleting effect of trading on depth at best quotes.

Nevertheless, the results are rather weak, especially during the post-announcement period.

However, the coefficient 𝜑𝐻𝐹𝑃𝑂𝑅𝐷𝐸𝑅 is negative and highly significant in all six regressions.

That is, HF limit order submission significantly reduces the improving effect of limit orders on the

depth at best quotes. The finding holds both before and after announcement. Coupled with results

in last subsection on negative contemporaneous relationship with market depth, this finding may

suggest that HF orders tend to be conservative and avoid competition with the positions at the best

quotes. In fact, Hendershott et al. (2010) in the context of the US equity market also record that

the impact of a higher message traffic on the depth of the order book is negative.

III.3. The Informativeness of HF Trades and Orders and the Effect on Price Efficiency

In this section we investigate whether HF activity promotes or hinders the price discovery

process of Treasury securities. The relationship between price discovery and trading activity has

been largely explored in the literature and several approaches have been proposed. In our

empirical investigation we examine this important issue from two angles: first, we compare HF

orders and trades against ”slow” (or manual) orders and trades to assess which group is more

informative. We do this by using the test proposed in Kaniel and Liu (2006). More specifically, we

divide the whole population of orders and trades into two samples. The first sample consists of the

HF orders (trades) while the second sample consists of orders (trades) which are submitted to the

market with a delay of 3 seconds or more following market changes. We exclude orders which are

submitted more than 1 second but less than 3 seconds following market changes. Intuitively, the

Kaniel and Liu (2006) test the informativeness of orders (trades) from the two samples by

comparing the actual percentages of orders (trades) in the “right” side of the market or predicting

the “correct” direction of the market. “Correct” direction means that a buy (sell) order is followed

by higher (lower) mid-quote in the future. If one sample has significantly larger number of quotes

in the “right” of the market than expected, then the sample is relatively more informed than the

other sample. More specifically, define 𝑃𝑚𝑎𝑛 (1 𝑃𝑚𝑎𝑛) as the probability that a submitted order

(trade) is a manual or HF order (trade), respectively; n the total number of times the quote

midpoint at 9:15 a.m. is in the correct direction (that is above the one at submission for a buy

order and below the one at submission for a sell order) following a submission of either a manual

or a HF order (trade) and 𝑛𝑚𝑎𝑛 the number of midpoint changes in the correct direction that

follow manual orders. Under the null hypothesis, Kaniel and Liu (2006) show that out of these n

quotes, 𝑛𝑚𝑎𝑛 or more is followed by manual order is given by

𝜙 1 𝑁 [𝑛𝑚𝑎𝑛−𝑛𝑃𝑚𝑎𝑛

√𝑛∙𝑃𝑚𝑎𝑛(1−𝑃𝑚𝑎𝑛)] (11)

If the probability is lower (higher) than 5% (95%), we reject the null hypothesis of equal

informativeness of HF orders (trades) and manual orders (trades) in favor of the alternative that

manual (HF) orders (trades) are more informative. In implementing the test, we also divide the

orders according to their size: small size (in the bottom tercile), medium size (in the middle tercile)

and large size (in the top tercile).

The results of the Kaniel and Liu’s (2006) testing procedure are reported in Table 6. Panel A

reports the probabilities computed for both orders and trades for all bond maturities during the

pre-announcement period. Panel B reports the probabilities computed for both orders and trades

for all bond maturities during the post-announcement period. We also divide both trades and

orders into three size groups. The results for market orders or trades are less conclusive, especially

during the pre-announcement period. However, during the post-announcement period, the test

favors the informativeness of HF trades. On the other hand, the results for limit orders strongly

suggest that manual orders overall tend to be more informative. In particular, during the

post-announcement period, the results are rather consistent across bond maturities and order sizes.

Overall the results reported in Table 6 are similar in spirit to the ones in Chaboud et al. (2009) who

recorded that the share of variance in returns that can be attributed to HF trading is surprisingly

small when compared to the share of variance attributed to human trading activity.

We assess and complement the above findings by further examining the effect of HF trading

on subsequent mid-quotes serial correlation, as suggested in Boehmer and Kelley (2010) and

Boehmer et al. (2012). The intuition is that, if prices follow a random walk, quote mid-points

autocorrelation should be equal to zero at all horizons. Deviations from zero imply predictability.

To put this framework to the data, we compute the quote mid-point return serial correlation within

each 5-minute intervals using 1-minute interval data after any macroeconomic news

announcement in our sample. As in Boehmer et al. (2012), we estimate the following equation:

| 𝐶 5𝑚(𝑖)| 𝛼 + 𝛾1𝐻𝐹 5𝑚(𝑖−1)𝑂𝑅𝐷𝐸𝑅∗ + 𝛾2𝐻𝐹 5𝑚(𝑖−1)

𝑇𝑅𝐴𝐷𝐸∗ + 𝛽1𝑉𝑂𝐿 5𝑚(𝑖−1)∗ + 𝛽2𝐷𝑃𝑇𝐻 5𝑚(𝑖−1)

𝐵𝑆𝑇∗

+ 𝛽3𝐷𝑃𝑇𝐻 5𝑚(𝑖−1)𝐴𝐿𝐿∗ + 𝛽4 𝑃 𝐷 5𝑚(𝑖−1)

∗ + 𝛾| | + 𝜀 5𝑚(𝑖)

(12)

where | 𝐶 5𝑚(𝑖)| denotes the absolute value of the mid-quote serial correlation coefficient and

the other variables are as discussed in Section 2.

The results of the estimation are reported in Table 7. The effect of HF activity on price

informativeness is mixed. In fact, abnormal HF orders and trades are only significant at the 5

percent level for the 2-year note. For the remaining bond maturities, there is no statistically

meaningful relationship between HF trades and orders and absolute mid-quote return serial

correlation. Even in the case of the 2-year note only HF orders are correctly signed. In fact, larger

the abnormal HF quoting activity is associated with smaller absolute serial correlation. The sign of

the coefficient associated with abnormal HF trades is, however, positive suggesting that larger the

HF trading activity is associated with smaller informational efficiency.

IV. Conclusion

This article explores the role of HF activities and their effects on market quality and the price

discovery process of US Treasury securities around macroeconomic news announcement. Using a

detailed dataset provided by BrokerTec, we propose and construct measures of HF activity by

looking at orders and transactions that have been recorded at a speed that is beyond human

capability. Using these new measures we assess i) how HF trades and orders take place around

macroeconomic news announcements, ii) whether HF trades and orders increase or deplete market

liquidity and iii) the role of HF activities in the price discovery process for US Treasury securities.

Our results are as follows: regarding (i), we find that both HF trades and orders submission go up

with the arrival of public information and remain high for the next fifteen minute interval. The

submission of HF orders increases significantly with the size of public information shock.

Regarding (ii), A higher percentage of HF trades does not widen bid-ask spread and does not

deplete market depth at best quotes. While a higher percentage HF limit orders tends to further

help reducing the bid-ask spread, a higher percentage HF limit orders has a negative effect on

market depth at best quotes. Regarding (iii), there is mixed evidence that HF activities contribute

to price discovery. We find that HF orders are not better informed than the “slow” orders. However,

there is mixed evidence that HF trades are more informative.

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Table 1

Macroeconomic News Announcements

This table reports the list of macroeconomic news announcements included in our analysis. N

denotes the total number of announcements during the period from January 5, 2004 to June 29,

2007; Day denote the weekday or day of the month of announcement; σ denotes the standard

deviation of announcement surprises; N|SUR| denotes the number of announcements with surprise

greater than one or two standard deviations.

Announcements N Day N|SUR|>σ N|SUR|>2σ

Business Inventories 13 Around the 15th of the month 4 1

Change in Nonfarm Payrolls 43 1st Friday of the month 14 3

Consumer Price Index 43 Around the 13th of the month 16 0

Current Account 14 10-11 weeks after quarter-end 5 0

Durable Orders 43 Around the 26th of the month 14 2

GDP Advance 15 3rd/4th week of the month for prior quarter 6 3

GDP Final 14 3rd/4th week of 2nd month following the quarter 2 1

GDP Preliminary 14 3rd/4th week of 1st month following quarter 2 1

Housing Starts 43 2 or 3 weeks after the reporting month 10 4

Initial Jobless Claims 187 Thursday weekly 47 9

NY Empire State Index 42 15th/16th of the month 17 2

PCE 43 Around the 1st business day of the month 5 5

Personal Income 43 Around the 1st business day of the month 5 2

Producer Price Index 44 Around the 11th of each month 2 2

Retail Sales 43 Around the 12th of the month 2 2

Trade Balance 43 Around the 20th of the month 12 2

Table 2

Summary Statistics of Trading Activities

This table reports summary statistics of trading volume ($ million), spread in ticks, depth at the best bid and ask ($ millions) and depth of the entire order book ($ millions) during the pre-announcement (8:15 a.m. to 8:30 a.m.) and post-announcement (8:30 a.m. to 8:45 a.m.) periods. All variables are calculated as the average over one-minute interval. The sample period is from January 5, 2004 to June 29, 2007.

Mean Median Std Max Min

Panel A: Pre-Announcement Period

2-year note

Trading volume ($ mln) 67.31 43.00 72.51 414.00 1.00

Spread (tick) 1.15 1.01 0.27 3.62 0.75

Depth at best quotes ($ mln) 398.86 317.08 305.75 1344.68 26.44

Overall depth ($ mln) 3454.29 2673.31 2981.17 11794.33 80.00

5-year note


Spread (tick) 1.35 1.20 0.51 5.98 0.75



10-year note


Spread (tick) 1.26 1.16 0.35 4.43 0.75



Panel B: Post-Announcement Period

2-year note


Spread (tick) 1.16 1.06 0.35 4.73 0.78



5-year note


Spread (tick) 1.36 1.18 0.62 7.14 0.78



10-year note


Spread (tick) 1.23 1.12 0.47 6.02 0.68



Table 3

HF Trades and Orders

This table reports the average number of HF trades (Panel A), HF orders (Panel B), as well as abnormal HF orders and traders (Panel C) over one-minute interval during the pre-announcement (8:15 a.m. to 8:30 a.m.) and post-announcement (8:30 a.m. to 8:45 a.m.) periods. HF trades are identified as a market buy (sell) order hitting the best ask (bid) quote within a second of the changes of the best quotes. HFLO1 denotes limit orders at the best quote modified within one second that the best quote on the same side of the market is changed. HFLO2 denotes limit orders at the best quote modified within one second that the best quote on the opposite side of the market is changed. HFLO3 denotes limit buy (sell) orders at the second best quote modified within one second that the best buy (sell) quote is changed. HFLO4 denotes limit orders cancelled or modified within one second of its placement regardless of market condition changes. Abnormal trades and orders are defined as in Equation (1)

2-year note

5-year note

10-year note

Pre-ann. Post-ann.

Pre-ann. Post-ann.

Pre-ann. Post-ann.

Panel A: HF Trades

HFMO 12.91 38.03

35.08 87.78

33.55 84.57

Panel B: HF Orders

HFLO1 7.59 11.29

14.76 28.19

13.73 23.02

HFLO2 10.24 26.93

23.49 59.40

22.56 55.73

HFLO3 7.71 22.41

21.43 59.41

18.19 49.33

HFLO4 449.98 1629.97

1012.87 3524.85

1148.77 4349.91

All orders 475.52 1690.60

1072.55 3671.86

1203.25 4478.00

Panel C: Abnormal HF Trades and Orders

Abnormal Trades 3.79 24.95

9.66 48.92

9.45 54.24

Abnormal Orders 60.74 1093.00

184.24 2185.52

196.06 3069.03

Table 4

HF Trades, Orders, and Market Conditions

This table reports the results of the regressions of abnormal HF trades (orders) against abnormal market volatility (VOLA) and abnormal market liquidity variables. The liquidity variables include depth at best quotes (DPTH

BST), overall depth (DPTH

ALL) and spread (SPRD). For each other,

market volatility is calculated as the most recent change in mid-quote before the order was submitted. Spread is the bid-ask spread in tick. Depth at the best quote is the sum of depth at the best ask and bid quote. Overall depth is the sum of depth on the ask side and bid side of the limit order book. All liquidity variables are calculated as the average over each one-minute interval. Abnormal liquidity measures are defined as in Equation (3) to Equation (5). The regressions are estimated as a panel data model with 2-way random effects. Panel A reports the estimation results during the pre-announcement period and Panel B reports the estimation results during the post-announcement period. ***, **, and * denotes significance at 1%, 5%, and 10% levels, respectively.

HF trades HF orders


2-year 5-year 10-year


Intercept 1.3297*** 1.2512*** 1.2868*** 4.4883** 12.4698** 14.9552**

VOLA* 0.2382*** 0.1614*** 0.0777*** -0.9935 -7.5381*** -7.0738***

DPTHBST

* -0.0001*** -0.0036*** -0.0053*** -0.0271*** -0.1885*** -0.3481***

DPTHALL

* 0.0008*** 0.0061*** 0.0071*** 0.0397*** 0.1688*** 0.2460***

SPRD* 0.5858*** 0.5383*** 1.0052*** 10.2995*** 6.4175*** 9.1128***

R2 0.454 0.235 0.242

0.022 0.019 0.028

Panel B: Post--Announcement Period



Intercept 2.4826*** 3.4505*** 3.7720*** 54.8281*** 129.0325*** 166.0489***

VOLA* 0.003 0.0013 -0.0213*** -1.6713** -1.1316*** -0.1191

DPTHBST

* -0.0002*** -0.0031*** -0.0016*** -0.0369*** -0.3145*** 0.2822***

DPTHALL

* -0.0005*** -0.0011 -0.0034*** -0.3403*** -1.5689*** -3.1111***

SPRD* -0.1444*** -0.1143*** 0.0407*** -3.2636*** -4.3266*** 0.0838

|SUR| 1.4820*** 0.1417 -0.0684

85.1247*** 59.7979*** 37.8848**

R2 0.046 0.042 0.048

0.114 0.077 0.116

Table 5

The Impact of HF Trades and Orders on Subsequent Market Liquidity

This table reports the results of the regressions of abnormal market liquidity against lagged abnormal HF trades (orders) with other control variables:

𝑃 𝐷 1𝑀(𝑖+1)∗ 𝛼 + (𝜑0 + 𝜑𝐻𝐹𝑃

𝑂𝑅𝐷𝐸𝑅)𝑂 𝐷 1𝑀(𝑖) + (𝛾0 + 𝛾𝐻𝐹𝑃𝑇𝑅𝐴𝐷𝐸)𝑇 𝐷 1𝑀(𝑖) + 𝛽 𝑃 𝐷 1𝑀(𝑖)

∗ + 𝜀 1𝑚(𝑖)

𝐷𝑃𝑇𝐻 1𝑀(𝑖+1)𝐵𝑆𝑇∗ 𝛼 + (𝜑0 + 𝜑𝐻𝐹𝑃

𝑂𝑅𝐷𝐸𝑅)𝑂 𝐷 1𝑀(𝑖) + (𝛾0 + 𝛾𝐻𝐹𝑃𝑇𝑅𝐴𝐷𝐸)𝑇 𝐷 1𝑀(𝑖) + 𝛽𝐷𝑃𝑇𝐻 1𝑀(𝑖)

𝐵𝑆𝑇∗ + 𝜀 1𝑚(𝑖) where 𝑂 𝐷 is the number of limit orders submitted, 𝑇 𝐷 is the number of market orders submitted, 𝑃𝑂𝑅𝐷𝐸𝑅 indicates the proportion of HF orders out of the total number of orders submitted and 𝑃𝑇𝑅𝐴𝐷𝐸 indicates the proportion of HF trades out of the total number of trades. Panel A reports the estimation results for abnormal spread and Panel B reports the estimation results for abnormal depth at the best quotes. ***, **, and * denotes significance at 1%, 5%, and 10% levels, respectively.

Pre-Announcement Period Post- Announcement Period



Panel A: Abnormal Spread

Intercept 0.0296*** 0.0333*** 0.0746*** 0.0839*** 0.0456*** 0.0532***

0.0270*** 0.0282*** 0.0473*** 0.0428*** 0.0268*** 0.0248***

SPRD* 0.4792*** 0.4812*** 0.6726*** 0.6700*** 0.5101*** 0.5057***

0.5399*** 0.5454*** 0.6047*** 0.6200*** 0.5848*** 0.5967***

𝑂 𝐷 -0.0003*** -0.0002** -0.0002*** 0.0002** -0.0002*** 0.0001**

-0.0001*** -0.0001 -0.0001*** 0.0002*** -0.0001*** 0.0001***

𝑇 𝐷 0.0065*** 0.0088*** 0.0016* 0.0015 0.0031*** 0.0028***

0.0007 0.0028*** -0.0002 0.0002 -0.0001 0.0013**

𝑃𝑂𝑅𝐷𝐸𝑅 × 𝑂 𝐷

-0.0003

-0.0011*** -0.0008***

-0.0002*

-0.0009*** -0.0004***

𝑃𝑇𝑅𝐴𝐷𝐸 × 𝑇 𝐷

-0.0115*** -0.0013

-0.0004

-0.0092*** -0.002

-0.0062***

|SUR|

0.3066*** 0.3039*** 0.4890*** 0.4583*** 0.2703*** 0.2562***

R2 0.184 0.1855 0.253 0.2557 0.1823 0.1862 0.4674 0.4691 0.5909 0.5973 0.5201 0.5242

Panel B: Abnormal Depth at Best Quote

Intercept -42.6938*** -43.4388*** -7.1298*** -7.4108*** -6.9624*** -7.2833***

-0.4949 -2.2805 0.0357 0.0729 -0.8772** -1.1123***

DPTHBST

* 0.8065*** 0.8730*** 0.7449*** 0.7567*** 0.7546*** 0.7602***

0.7891*** 0.7880*** 0.6533*** 0.6524*** 0.6404*** 0.6384***

𝑂 𝐷 0.2135*** 0.3364*** 0.0301*** 0.0523*** 0.0278*** 0.0527***

0.0632*** 0.1268*** 0.0047*** 0.0086*** 0.0046*** 0.0103***

𝑇 𝐷 -3.6264*** -3.3056*** -0.4335*** -0.6561*** -0.6340*** -0.6677***

-0.7352*** -1.2846*** -0.1875*** -0.1540*** -0.2122*** -0.2321***

𝑃𝑂𝑅𝐷𝐸𝑅 × 𝑂 𝐷

-0.4224*** -0.0572*** -0.0653***

-0.1547*** -0.0098**

-0.0133***

𝑃𝑇𝑅𝐴𝐷𝐸 × 𝑇 𝐷

2.8246

0.9634*** 0.3353

1.9910**

-0.135

0.1008

|SUR|

-23.2216** -22.8680** -2.9602 -3.1466 -3.8744** -3.8778**

R2 0.587 0.6275 0.5157 0.522 0.5183 0.5268

0.7206 0.7217 0.4944 0.4948 0.4877 0.4886

35

Table 6

Informativeness of HF Trades and Orders Relative to Other Trades and Orders

The table reports results of Kaniel and Liu (2006) test for the informativeness of HF trades

and orders relative to other trades and orders. A probability above 95% indicates relative

informativeness of HF order (trade), whereas a probability below 5% indicates relative

informativeness of other orders. We also report the results based on trades and orders in

different size categories. “Small” indicates orders (trades) in the bottom tercile, “Medium”

indicates orders (trades) in the middle tercile, and “Large” indicates orders (trades) in the top

tercile.

Market Order Limit Order


All Small Medium Large


2yr 23.88 5.72 32.13 74.08

99.90 99.86 99.32 8.05

5yr 4.52 1.08 74.22 11.95

0.00 0.00 0.45 85.24

10y 90.81 17.89 59.09 99.12

0.00 0.04 0.00 0.01

Panel B: Post-announcement Period



2yr 88.46 89.69 42.54 83.15

0.00 0.00 0.00 0.00

5yr 95.20 64.02 78.24 94.79

0.00 0.00 0.00 0.00

10y 76.18 46.44 77.28 68.21

0.00 0.00 0.00 0.00

36

Table 7

Price Informativeness of HF Trades and Orders

This table reports the results of the regressions of absolute return autocorrelation against HF trades and orders. The absolute autocorrelation ( | 𝐶 5𝑚(𝑖)| ), a measure of price informativeness, is calculated from minute by minute mid-quote returns over the next five minute interval.

| 𝐶 5𝑚(𝑖)| 𝛼 + 𝛾1𝐻𝐹 5𝑚(𝑖−1)𝑂𝑅𝐷𝐸𝑅∗ + 𝛾2𝐻𝐹 5𝑚(𝑖−1)

𝑇𝑅𝐴𝐷𝐸∗ + 𝛽1𝑉𝑂𝐿 5𝑚(𝑖−1)∗ + 𝛽2𝐷𝑃𝑇𝐻 5𝑚(𝑖−1)

𝐵𝑆𝑇∗

+ 𝛽3𝐷𝑃𝑇𝐻 5𝑚(𝑖−1)𝐴𝐿𝐿∗ + 𝛽4 𝑃 𝐷 5𝑚(𝑖−1)

∗ + 𝛾| 5𝑚(𝑖)| + 𝜀 5𝑚(𝑖)


Intercept 0.6191*** 0.7668*** 0.7739***

VOLA* 0.0062 0.0076** -0.0001

DPTHBST

* -0.0020** 0.0019 0.0104**

DPTHALL

* -0.0001 -0.0003 -0.0005**

SPRD* 0.1557** 0.0664** 0.0449

HFORDER

* -0.0005** 0.0001 0.0001

HFTRADE

* 0.0320*** 0.0001 0.0069

|SUR| 0.0860** 0.0456 0.0647

R2 0.062 0.0253 0.0128

high frequency trading in the us treasury marketconference/conference2012/... · 2012. 12. 20. ·...

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