Macroeconomic Announcements and Information … Macroeconomic Announcements and Information Asymmetry in the Foreign Exchange Market Abstract This article studies whether and to what

Macroeconomic Announcements and Information

Asymmetry in the Foreign Exchange Market

Yu-Lun Chen, Yin-Feng Gau, and Ching-Yu Wang

Yu-Lun Chen, Department of Finance, Chung Yuan Christian University; Yin-Feng Gau, Department of Finance, National Central University; Ching-Yu Wang, Department of International Business, National Chi-Nan University. Correspondence: Yin-Feng Gau, Department of Finance, National Central University, 300 Jhongda Rd., Jhongli, Taoyuan 320, Taiwan, phone: 886-3-4227151 ext. 66263, fax: 886-3-4252961, e-mail: [email protected]. The authors are grateful to the ICAP for providing the EBS tick-by-tick exchange rate data. Y.-L. Chen acknowledges a Post-Doctorate grant provided by the ATU plan of the Ministry of Education in Taiwan. Y.-F. Gau acknowledges a research grant from the National Science Council (NSC97-2410-H-008-061).

1

Macroeconomic Announcements and Information

Asymmetry in the Foreign Exchange Market

Abstract

This article studies whether and to what extent information affects the determination of

spot exchange rates in an electronic limit-order foreign exchange market. Using the

tick-by-tick data of EUR–USD quotes and transactions on the Electronic Broking

Services (EBS), the authors find evidence of intraweek and intraday variation in the

level of information asymmetry. After macroeconomic announcements in the United

States and Europe, the level of information asymmetry increases, which reveals that the

informational role of order flow increases immediately after the release of

macroeconomic announcements.

Keywords: Bid-ask spread; Adverse selection cost; Information asymmetry; Electronic

broking system; Foreign exchange market

JEL Classification: F31, G14

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1. Introduction

The microstructure approach to the foreign exchange (FX) market focuses on the

influence of trading on exchange rate movements. The importance of private

information in FX markets initially was confirmed by Lyons (1995), using evidence of

adverse selection.1 However, the recent availability of detailed data about the trading

activity in FX markets also makes it possible to study the link between information

asymmetry and the impact of trading on exchange rates.

In equity markets, knowledge about a firm’s cash flows is private information;

public information releases also may increase information asymmetry if traders differ in

their ability to interpret this news (Kim and Verrecchia 1994, 1997). Evans and Lyons

(2002) thus argue that interdealer FX transaction prices depend on dealers’ trading

decisions, which are based on incomplete, heterogeneous information. In categorizing

new information as common knowledge (CK) or non-common knowledge (NCK),2

Evans and Lyons (2002) suggest that NCK information affects both the transaction

prices and interdealer order flow, whereas CK information only influences prices, with

no effects on trading activities. Green (2004) also shows that the release of public

information affects the level of information asymmetry in the U.S. Treasury market.

Using EUR–USD spot trading data from the Electronic Broking Service (EBS),

we examine the influential role of information in exchange rate determinations. Our

data set contains relevant information about each trade, such as transaction time,

transaction prices, and the trade initiator. Relying on structural models to infer the

components of spreads related to information asymmetry, we study how the

informational role of trading varies around the releases of macroeconomic

announcements. We also examine how the level of information asymmetry changes

across trading sessions in local FX markets and by the day of the week.

Interdealer FX transactions can take place through two trading channels: direct

(bilateral) trades and brokered trades (including both electronic brokers and more

traditional voice brokers). For direct interdealer trades, details such as bid-and-ask

quotes or the amount and direction of trades are available only to the two trading parties.

1 Many studies provide strong empirical evidence of information asymmetry in FX markets, such as Lyons (1995, 1997), Ito et al. (1998), Payne (2003), Berger et al. (2008), Osler et al. (2007), and Love and Payne (2008). 2 Common knowledge information is characterized by the simultaneous arrival of new information to all traders and their homogeneous interpretation of its implications for equilibrium exchange rates. Non-common knowledge can come from public or private sources. A macroeconomic announcement may be a source of public NCK when dealers fail to reach consensus about its implications.

3

Trading via brokers is relatively more transparent. In particular, electronic brokers

announce the best bid-and-ask prices and the direction of all trades, though this

information is available only to dealers. Electronic brokers have become very popular

since their introduction in 1992 and now provide a dominant tool for interdealer trading;

they also provide some degree of centralization, in contrast with direct interdealer FX

trading.

Dealers in the FX market receive information from various sources, including their

own customers, screens of electronic broking systems, and newswire services. Because

traders or dealers can interpret news or economic events differently and their

interpretations cannot be incorporated into prices immediately and directly, order flow

plays an important role in the FX market, such that it reveals traders’ information about

fundamentals (Lyons, 1995; Peiers, 1997; Ito et al., 1998; Cao et al., 2006; Evans and

Lyons, 2008).3 For example, Evans and Lyons (2008) show that almost two-thirds of

the total effect of macroeconomic news about Deutschemark–USD exchange rate

changes is transmitted through order flows. Berger et al. (2008) demonstrate an intraday

pattern in the price impact of order flows in the FX market.

The price impacts of order flow thus are significant in major financial markets.

Returns increase with order flow, defined as net buyer-initiated trades in excess of

seller-initiated trades, as demonstrated in studies across equity markets (Huang and

Stoll, 1994; Chordia et al., 2002; Chordia and Subrahmanyam, 2004), bond markets

(Brandt and Kavajecz, 2004; He et al., 2009), and FX markets (Evans and Lyons, 2002;

Bjønnes et al., 2005; Bacchetta and van Wincoop, 2006). The impact of order flow

relates to the prevailing level of information asymmetry (Glosten and Milgrom, 1985;

Kyle, 1985). If some traders are more informed about the future value of an asset, their

trades reveal information, so the order flow conveys price-relevant information.

Because some traders have such an information advantage, information-based

models (Kyle, 1985; Glosten and Milgrom, 1985; Admati and Pfleiderer, 1988)

generally include learning and adverse selection problems. When a dealer receives a

trade, she or he adopts revised expectations and sets spreads to protect against informed

3 For example, Evans and Lyons (2008) show that macroeconomic news can affect exchange rates directly and indirectly through order flows. Lyons (1995) and Cao et al. (2006) suggest that information asymmetry in foreign exchange markets reflects dealers’ private access to customer order flows, and order flow can help predict short-run exchange rate movements. Peiers (1997) finds that Deutsche Bank is a price leader prior to German Central Bank interventions because of the information advantage it obtains from the order flow of the Central Bank. Ito et al. (1998) show that Japanese banks are perceived as informed traders in yen currency markets.

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traders. As Glosten and Milgrom (1985) show, the bid–ask spread can be decomposed

into three components: the order processing cost, inventory holding cost, and

information asymmetry cost (adverse selection cost). We examine whether private

information exists by searching for a non-zero adverse selection cost component in the

spread. Lyons (1995) also finds evidence of costs of both information asymmetry and

inventory control in the FX market, using Madhavan and Smidt’s (1993) model.

Bjønnes and Rime (2005) instead use the Huang and Stoll (1997) model to decompose

the effective spread into adverse selection, inventory holding, and order processing

costs; they find no support for the types of information and inventory effects predicted

by the Madhavan-Smidt model.

Well-known cyclical patterns in spreads, volume, and volatility have been widely

documented (e.g., McInish and Woods 1992). For a better understanding of the process

of intraday price formation, we examine how much trade-relevant information affects

trading costs in a day. Madhavan, Richardson, and Roomans (1997; hereafter MRR)

find that on the New York Stock Exchange (NYSE), the adverse selection cost follows

a U-shaped pattern, while liquidity-providing costs increase steadily over the day. In

contrast, Ahn et al. (2002, 2005) conclude that both information asymmetry and

inventory holding costs in the Tokyo Stock Exchange (TSE) exhibit independent

U-shaped patterns and that the costs of information asymmetry relate to firm

characteristics, not ownership characteristics.

With this study, we provide evidence of temporal variation in the spread

components in FX markets. To the best of our knowledge, this article offers the first

investigation of periodic variation in the level of information asymmetry in FX markets,

though Foster and Viswanathan (1993) and MRR (1997) have studied intraday and

intra-weekly variations in the cost of information asymmetry for stock markets.4 We

extend MRR’s (1997) structural model to examine if adverse selection costs vary with

time and follow a periodic pattern. We also allow order processing and inventory costs

and the autocorrelation of order flows to vary with time.

Evidence of heavy trading that typically accompanies macroeconomic news

releases contradicts the prediction of macro-based exchange rate models. Assuming all 4 With data about stock markets, Glosten and Harris (1988) and Stoll (1989) decompose trading costs into order processing, inventory holding, and adverse selection cost components, but they do not consider whether the components vary over time. Foster and Viswanathan (1993) and MRR (1997) examine whether temporal variations in adverse selection cost components exist in the stock market. MRR (1997) use intraday NYSE data to study how bid–ask spreads change over a day and find that the level of information asymmetry declines steadily.

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traders have rational expectations and all information is public, macro-based exchange

rate models imply that the release of macroeconomic news induces identical revisions

in all agents’ expectations, such that the price moves instantly to reflect new

expectations, without any associated changes in trading volume. Evans and Lyons

(2005, 2008) and Love and Payne (2008) find that the impact of order flows on

exchange rate changes increases with the release of macroeconomic announcements.

As our second main contribution, we highlight varying level of information

asymmetry around macroeconomic announcements. For the EUR–USD market, we

consider macroeconomic announcements. We modify MRR’s model to characterize

how the level of information asymmetry evolves during the process of information

assimilation, before and after a macroeconomic announcement. With this model we can

isolate the effective bid–ask component related to information asymmetry. The MRR

model is a generalized version of models proposed by Glosten and Milgrom (1985) and

Stoll (1989), and it allows order flow to be autocorrelated, which is a salient feature for

EBS spot transaction data.

Before an announcement, the adverse selection component of spread is lower, but

the cost of providing liquidity (including order processing and inventory holding costs)

is higher, which suggests less information asymmetry before an announcement. Our

finding that the sensitivity of prices to order flow is lower before the announcement

than on days without an announcement in the EUR–USD spot market is consistent with

Green (2004), who finds no information leakage in the market before an announcement.

The adverse selection cost rises after the announcement, and the market is more liquid,

which implies that the informational role of trading increases after the arrival of new

public information. This finding could reflect traders' heterogeneous interpretations of

the implications of macroeconomic announcements for equilibrium exchange rates.

However, greater information asymmetry following announcements does not last

long. Instead, the adverse selection cost returns to a near-normal level 15–30 minutes

after announcements, which suggests that information disperses quickly in the market,

and traders quickly reach consensus in their expectations about future exchange rates.

Intense trading immediately after announcements thus encourages information

transmission within the market, and more NCK information revealed from interdealer

order flow prompts a higher level of information asymmetry immediately after the

announcement. As more and more information emerges from the order flow, the level

of information asymmetry falls to the usual level.

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The remainder of this paper is organized as follows: In Section 2 we explain the

influence of trading activities on exchange rate changes in the foreign exchange market.

We then discuss the tick-by-tick exchange rate data from the EBS and summarize

macroeconomic announcements in Section 3. With Section 4 we introduce the

framework of spread decomposition proposed by MRR (1997). Section 5 contains the

modified MRR models we used to study variations in the extent of information

asymmetry and their empirical results. We conclude in Section 6.

2. Trading Activities in the FX Market

2.1. Intraday pattern of trading volume and spreads

The FX market is the world’s largest, with average daily turnover of US$3.2 trillion (as

of April 2007; Bank for International Settlements 2007). Rime (2003) points out that

the emergence of electronic brokers and Internet trading has significantly changed the

structure of FX markets though, making them more transparent, efficient, and

centralized, with a greater focus on order books instead of market making.5

Using EBS trading data, Ito and Hashimoto (2006) observe that trading volume

(i.e., number of deals) moves with the opening and closing of different trading centers.

As Tokyo trading opens, the trading volume rises modestly from overnight lows, after

which it follows a roughly U-shaped pattern during the TSE trading hours, then takes

another U-shaped pattern during the morning hours in London. Trading volume reaches

its peak for the day as London closes and New York traders have lunch, then declines

almost monotonically, reaching its intraday low as Tokyo trading opens. Because

USD–JPY trading volume follows this double-U pattern, the spread follows an inverse

intraday pattern: a peak during the overnight period, a decline as trading surges at the

opening of the TSE, followed by another increase; the spread then falls once again

during the London morning, and after the London market closes, the spread moves up

to its overnight peak (Ito and Hashimoto, 2006).6

This inverse relation between volume and spread over the course of just one day is

consistent with predictions from Hartmann (1999) and Admati and Pfleiderer (1988).

Hartmann (1999) explains intraday relations between spreads and volume in terms of

fixed operating costs, such as the costs of maintaining a trading floor and acquiring

5 For a detailed introduction to foreign exchange markets’ microstructure, see Lyons (2001), Rime (2003), or Osler (2008). 6 Breedon and Ranaldo (2011) also note that intraday patterns are present in EBS EUR–USD exchange rate returns and order flows: Local currencies tend to depreciate in their own trading hours and appreciate outside them.

7

real-time information. When the trading volume is high, the operating costs can be

covered easily by small spreads, and vice versa, as long as the extra volume is

dominated by uninformed liquidity traders. In their asymmetric information model,

Admati and Pfleiderer (1988) also predict a negative relation between intraday spread

and volume in security markets. Furthermore, MRR (1997) find that adverse selection

costs follow an intraday U-shaped pattern in the NYSE; the high spread at the market

opening might represent a response to the high adverse-selection risk accumulated

overnight. However, the higher spread at the close reflects the high inventory risk that

dealers face, due to the absence of trades before the market reopens the next morning.

2.2. Order flow and exchange rate movements

Most textbook models of exchange rate determination assume that news

announcements are impounded directly into prices, with no role for trading in the

information assimilation process. Evans and Lyons (2002) show that order flow,

defined as the difference between buyer-initiated and seller-initiated trades, can explain

contemporaneous exchange rate movement, which implies that order flow also contains

trade-relevant information. In this case, information asymmetry may be due to the

heterogeneity in dealers’ interpretations of customers’ order flow.

In currency trading, inventory control and information asymmetry effects alter the

movement of the exchange rates. As we noted, Lyons (1995) finds strong evidence of

both effects in an analysis of dealers’ trading behavior in the Deutschemark–USD

market. Ito et al. (1998) similarly find evidence of private information in the USD–JPY

market in Tokyo, and Bjønnes et al. (2008) reveal that large banks have more

information than small banks with regard to currency trading.

Therefore, using rational expectations and market efficiency, conventional macro

models of exchange rate determination predict that macroeconomic announcements or

public information can be directly integrated into prices, such that publicly available

information should not affect exchange rate changes, because exchange rates depend on

fundamentals. In other words, the exchange rate adjusts immediately to new

information, without any trading, and order flow has no role in adjustments to news.

Yet empirical evidence shows that order flow is the main channel through which news

affects exchange rate movements (Love and Payne, 2008; Evans and Lyons, 2008).

Recent studies of the FX microstructure emphasize the role of trading activities in price

formation through order flows; for example, Evans and Lyons (2002, 2008) and

Dominguez and Panthaki (2006) find that macroeconomic announcements affect the

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exchange rate through order flows. Evans and Lyons (2002) thus argue that order flows

convey information about individual assessments of announcements, because dealers

learn and interpret information implicit in order flows from both their own customers

and other dealers with whom they trade.

To identify the information effects of order flows, we might examine their

persistent effects on prices (Hasbrouck, 1991a, 1991b; Evans and Lyons, 2002; Payne,

2003; Bjønnes et al., 2005; Berger et al., 2008), consider adverse selection components

of bid–ask spreads (Lyons 1995; Yao, 1998; Naranjo and Nimalendran, 2000; Hartmann,

1999; Bjønnes and Rime, 2005), study how volatility responds to trading halts (French

and Roll, 1986; Ito et al., 1998), or directly survey FX dealers (Cheung and Wong, 2000;

Cheung and Chinn, 2001; Cheung et al., 2004). Even if information is not incorporated

immediately into prices, it disseminates through subsequent trades.

3. Data

3.1. EBS data

Unlike a conventional interdealer FX market that features decentralized trading across

locations, the EBS market functions under an electronic limit-order system. Electronic

brokers, first introduced in 1992, account for increasing market share, such that by the

late 1990s, the two top electronic broking systems, EBS and Reuters, participated in

more than 50% of spot FX transactions. The popularity of interdealer electronic broking

systems likely stems from their disclosure of market quotes and overall market order

flow, which provides important sources of real-time information.7 Electronic broking

systems also provide listed prices to all member dealers, whereas dealers in traditional

interdealer markets could acquire information only from their own customer order

flows, without any access to market-level information.

Our data regarding spot trading for the EUR–USD exchange come from the EBS

brokered segment of the interdealer FX market, during the period from January 1, 2004,

through December 31, 2005. Unlike equity markets with their regular open and close

times, FX trading is continuous. Over the two-year period we study, approximately 7.5

million deals took place. On the EBS, EUR–USD spot transactions tend to concentrate

during overlapping trading hours in London and New York, likely due to heterogeneous

expectations by participants in different regions.

7 As Rime (2003) notes, the EBS screen shows the bid and offer (ask) prices, whether the best prices in the market or the best available from credit-approved banks. It also shows the dealer’s trade and the price and direction of all trades throughout the system for selected exchange rates.

9

Because we know the trading direction for each deal in the EBS data, we can

measure information asymmetry directly, without classifying trades as buyer versus

seller initiated. The EBS provides currency trading for Sunday–Friday; however, EBS

trading is relatively minimal on Sunday, so we exclude Sunday's trading activities from

our analysis and assess the trades from Friday 24:00 to Saturday 24:00, GMT .

3.2. Macroeconomic announcements

We note changes in the information asymmetry around the release of macroeconomic

announcements in the United States and Europe, including the scheduled

macroeconomic announcements we list in Table 1. Most U.S. announcements occur

between at 8:30 Eastern Standard Time (EST) and 10:00 EST. Most European

announcements arrive at 11:00 GMT.

We focus on these times because these regularly scheduled macroeconomic

announcements represent a primary source of information in the FX market. To

determine how the degree of information asymmetry varies with macroeconomic

announcements, we estimate components of the bid–ask spread in the half-hour interval

before each announcement and the half-hour interval after it. A macroeconomic

announcement should prompt a change in the belief dispersions in the market, which

can reveal the extent of information asymmetry changes surrounding that

announcement.

4. Measuring Components of the Bid–Ask Spread

The bid–ask spread comprises order processing, inventory holding, and information

asymmetry costs. First, order processing costs derive from the provision of market

making services, including computer costs, rent, informational services, and the

opportunity cost of the market maker’s time. Second, inventory holding costs arise

when the market makers carry positions acquired to supply investors with liquidity (Ho

and Stoll, 1981). Third, market makers bear information asymmetry costs when they

trade with customers who are better informed about the true value of asset or interpret

information differently for some events (Easley and O’Hara, 1987).

There are two main approaches for decomposing these spread components: a

covariance-based model and a trade indicator–based model. As Roll (1984) shows,8

covariance-based models infer spreads from the autocovariance of returns; trade

8 Roll (1984) shows the spread can be presented as a function of the first-order autocovariance of returns. Stoll (1989) uses the autocovariance of returns to study spread components.

10

indicator models focus on the direction of a trade that carries information (e.g., Glosten

and Harris 1988; Hasbrouck 1988, 1991b; Huang and Stoll 1994, 1997; MRR 1997).9

We use the structural model proposed by MRR (1997) to measure the information

asymmetry and liquidity-providing costs in the spread that marks the EUR–USD

market. The MRR model represents a generalized version of previous models (e.g.,

Glosten and Milgrom 1985; Stoll 1989) that allows order flows to be autocorrelated.

According to equation (4) in MRR (1997), the relation between price changes and the

trade indicator is:

tttttt eXXPPR 11 )()( , (1)

where tP is the transaction price of the asset at time t; tX is an indicator variable for

trade initiation, such that 1tX if the trade is buyer initiated and 1tX if the

trade is seller initiated; is the first-order autocorrelation coefficient of order

flow tX ; and the error term te captures the effect of factors other than trades, such as

public information shocks and price discreteness. Furthermore, the parameter

reflects the adverse selection cost that results from information asymmetry, such that it

measures the private information revealed by order flow. Because order flow is

autocorrelated, only the unexpected portion of the order flow reveals information. The

parameter in turn captures compensation for providing liquidity and covering order

processing costs; a higher is compensation for the costs of inventory holding and

order processing when the liquidity supply is higher.

Following MRR (1997) and Green (2004), we use a generalized method of

moments (GMM) to estimate parameters in the model. The GMM estimates minimize

the distance function, according to the orthogonality restriction (or moment conditions)

implied by the model. That is, if is a constant, and

1)()( tttt XXRu , then the population moments implied by Equation

(1) can identify the parameter vector ),,,( :

9 Glosten and Harris (1988) propose a spread decomposition model with inventory and information asymmetry costs. Bjønnes and Rime (2005) use Huang and Stoll's (1997) indicator model and find evidence of private information in the spot FX market but no evidence of inventory control through dealers’ own prices. Using the model proposed by Madhavan and Smidt (1991) though, Lyons (1995) finds evidence of adverse selection and inventory effects in the FX markets.

11

0

)(

)(

)(

1

11

tt

tt

t

ttt

Xu

Xu

u

XXX

E

. (2)

The first-moment condition determines the autocorrelation in order flow, the

second equation defines the drift term as the average pricing error, and the last two

equations represent the ordinary least squares (OLS) normal equations. Within our

GMM procedure, we adopt the Newey-West heteroskedasticity and autocorrelation

consistent (HAC) covariance matrix to estimate the variance-covariance matrix of

estimated parameters.

5. Empirical Results

5.1. Spread components during the week

The estimation results of the basic MRR model in Table 3 reveal significant and

positive estimates of and , which indicate that both the adverse selection and the

order processing and inventory cost components are greater than 0. We also observe

that order flow tX has a positive first-order autocorrelation.

For transactions that take place during the week (Monday–Friday), the

day-of-the-week effect may be salient for exchange rate returns, spreads, and order

flow,10 such that spread components could vary across weekdays. We modify the MRR

model to examine intraweek periodicity in (information asymmetry parameter) and

(cost of providing liquidity), as follows:

5

111,

5

1, )()(

jtttjjjj

jttjjjt eXDXDR , (3)

where tjD , ( j = 1, …, 5) refers to a weekday dummy variable, such that 1,1 tD if t is

on Monday, and 0 otherwise; 1,2 tD if t is on Tuesday, and 0 otherwise; and so forth.

These dummy variables designate trades, whereas j (j = 1, …, 5) captures the extent

of information asymmetry on the jth weekday. Similarly, j (j = 1, …, 5) captures the

order processing cost on the jth weekday, and j (j = 1, …, 5) refers to the first-order

autocorrelation of tX on the jth weekday. To simplify the presentation, we use the

expressions

10 For example, the spread is wider on Monday morning in Tokyo (Ito and Hashimoto, 2006).

12

5

111,

jttjjtt XDX , (4)

and

5

111,

5

1, )()(

jttjjjj

jttjjjtt XDXDRu . (5)

Then we can use the following moment conditions implied by Equation (3) to identify

the parameter vector, ),,,,,,,,,( 515151 :

0

)(

)(

)(

)(

11,5

11,1

,5

,1

11,5

11,1

ttt

ttt

ttt

ttt

t

ttt

ttt

XDu

XDu

XDu

XDuu

XD

XD

E

. (6)

The first five moment conditions determine the autocorrelation in order flow during the

different weekdays, the sixth defines the drift term as the average pricing error, and

the remaining equations represent the OLS normal equations. As do MRR (1997) and

Green (2004), we again use the Newey-West HAC covariance matrix to estimate the

variance-covariance matrix of the parameters.

In Table 4 we display the GMM estimation results of Equation (4), which allows

for variation of information asymmetry by day of the week. In Panel A, all parameters

are significantly positive at the 1% level. The results of the Wald F-tests for the equality

of respective parameters suggest that significant day-of-the-week effects are present in

j (adverse-selection cost), j (liquidity-providing cost), and j (autocorrelation

in order flow).

We find that 32451ˆˆˆˆˆ and 12345

ˆˆˆˆˆ , so adverse

selection costs are highest on Monday and lowest on Wednesday; the liquidity

providing cost instead starts at the lowest level on Monday, increases steadily over the

week, and reaches its highest level on Friday. Because information asymmetry is higher

13

on Monday and Friday than in the middle of the week, we denote a U-shaped intraweek

pattern in the level of information asymmetry in the EBS EUR–USD market. The

evidence of high information asymmetry on Monday suggests that informed traders

avoid trading over the weekend, when most other traders are not trading. The high level

of information asymmetry on Friday also suggests that informed traders do not carry

information over the weekend when trading is inactive. However, the pressure for

traders to maintain a desired level of inventory is highest on Friday, the day before the

weekend, so the liquidity-providing cost increases, in that traders’ inventory-holding

costs are greatest on Friday.

Although FX trading is continuous, without regular open and close times, we find

the market shows active trading activities from Monday to Friday and slower (or

inactive) trading patterns on the weekend. If the FX market is open when trading is

active, but closed when trading is inactive during the weekend, we find results

consistent with MRR (1997)—that is, that adverse-selection costs follow a U-shaped

pattern, but liquidity-providing costs increase steadily during the day on the NYSE.

Panel B of Table 4 shows that more trades occur on Tuesday, Wednesday, and

Thursday than on Monday or Friday. This trend suggests an inverted U-shaped

intraweek pattern in trading volume (in number of trades) in the EBS EUR–USD

market. For comparison, we also report the average number of trades on Sunday; it is

very low relative to the number of trades on other weekdays. As Bollerslev and

Domowitz (1993) recommend, we exclude Sunday data from our analysis to avoid

confounding the evidence with decidedly illiquid trading activities over weekends.

5.2. Intraday pattern of spread components

We also study variation in the cost components of spread during local trading sessions

in New York and London. As we show in Table 2, there is a daily, three-hour period of

overlapping trading hours in New York and London. We thus categorize transactions

into four periods: London-only, London–New York overlapping, New York-only, and

neither London nor New York trading hours.

We modify the MRR model to incorporate intraday variation in trading costs:

)7()(

)()()(

)()()()(

11,&&&&

11,11,1000

,&&,,00

tttNYLNNYLNNYLNNYLN

ttNYNYNYNYttLNLNLNLNt

ttNYLNNYLNNYLNttNYNYNYttLNLNLNtt

eXI

XIXIX

XIXIXIXR

where tLNI , indicates trades during the trading hours in London before the beginning

14

of New York trading hours, such that 1, tLNI if t is between 08:00–13:00 GMT, and

0 otherwise. Similarly, 1, tNYI if the transaction occurs at time t, which is during

16:00:00–21:00 GMT, or New York-only trading hours, and 0 otherwise. Because

tNYLNI ,& indicates overlapping trading hours in New York and London, it equals 1 if t is

between 13:00:00–16:00 GMT, and 0 otherwise. The parameters 0 and 0 define

the trading session when neither New York nor London is operational, so they capture

the adverse selection cost and other costs of trading (e.g., order processing and

inventory holding) for transactions during 21:00–08:00 GMT, which includes Asian and

Australian trading hours. In this case, 0 refers to the first-order autocorrelation

coefficient of tX during this benchmark interval. The parameters LN , NY , and

NYLN & indicate the change in adverse selection costs for transactions that take place

during London-only, New York-only, and London–New York trading hours,

respectively, compared against the cost for transactions occurring during the baseline

period. Similarly, LN 0 , NY 0 , and NYLN &0 indicate liquidity-providing

costs for these respective transactions.

If we express the error terms in order flow and returns equations, we derive: 11,&&11,11,10 ttNYLNNYLNttNYNYttLNLNttt XIXIXIXX , (8)

and

)9()()(

)()()(

)()()(

11,&&&&11,

11,1000,&&&

,,00

ttNYLNNYLNNYLNNYLNttNYNYNYNY

ttLNLNLNLNtttNYLNNYLNNYLN

ttNYNYNYttLNLNLNttt

XIXI

XIXXI

XIXIXRu

In turn, we can determine the population moments to identify the parameter vector,

),,,,,,,,,,,,( &0&0&0 NYLNNYLNNYLNNYLNNYLNNYLN :

15

.0

)(

)()(

)(

)(

)(

)(

)(

11,&

11,

11,

1

,&

,

,

11,&

11,

11,

1

ttNYLNt

ttNYt

ttLNt

tt

ttNYLNt

ttNYt

ttLNt

tt

t

ttNYLNt

ttNYt

ttLNt

tt

XIu

XIuXIu

Xu

XIu

XIu

XIu

Xuu

XI

XI

XI

X

E

(10)

Similarly, we use the Newey-West HAC covariance matrix to adjust for

heteroskedasticity and autocorrelation in the error term.

With Table 5 we provide the estimation results of the model that considers the

variation of information asymmetry during New York and London trading hours. The

results of the Wald F-tests for the equality of the respective parameters suggest a

significant intraday pattern of adverse selection and liquidity-providing costs, as well as

autocorrelation in order flow over the trading hours.

Because j (j = LN, LN&NY, or NY) measures the difference in the

adverse-selection cost between the session j and the baseline session (neither London

nor New York operating), we can show in Panel A (Table 5) that the level of

information asymmetry is highest during the overlapping London–New York trading

hours. The London market does not open after 16:00 GMT, so the level of information

asymmetry declines and is slightly lower than the level of information asymmetry for

transactions after the closing of the New York market (21:00–08:00 GMT). Overall, we

observe an inverted U-shaped pattern in the variation of from 21:00 GMT on day

t – 1 to 21:00 GMT on day t. This evidence is consistent with MRR (1997), who

observe an inverted U-shape in the level of information asymmetry in the NYSE.

Yet estimates of j show that the liquidity-providing cost starts high during the

baseline period, declines when the London market opens at 08:00 GMT, reaches its

lowest level when both markets are operating, and finally increases when the London

market closes and only the New York market is still active. The liquidity-providing cost

16

roughly follows a U-shaped pattern over 24-hour global trading hours.

In Panel B we find a pattern for the average number of trades across different

sessions similar to that of j but opposite the j pattern, such that adverse selection

costs appear positively related to market liquidity, but liquidity-providing cost is

negatively associated. Because trading is liquid, the pressure on traders to hold an

undesired position decreases, as does the inventory holding cost. We also observe

increased first-order autocorrelation in tX for trading sessions when either London or

New York is operational. The highest for the overlapping interval shows that the

orders are more likely to be broken up into smaller trades when both London and New

York markets are open. The Wald F-test is significant at the 0.01 level.

Our finding of relatively higher adverse selection costs for 08:00–16:00 GMT

suggests that informed EUR–USD traders are more likely to exploit their information

advantage when the London market is open. When neither London nor New York is

open, the adverse-selection cost for EUR–USD trading is slightly higher than that

during the New York-only session; traders who are more informed about the future

value of EUR–USD are more likely to trade in the period before the open of London

market than in the period after its close to exploit their information advantage. He et al.

(2009) also observe that informed traders in the U.S. Treasury market are more likely to

trade in the pre-open interval (one hour before the market opens) than in the post-close

period (two-and-a-half hours after it closes) to exploit their information advantage.

5.3. Effects of macroeconomic announcements on spread components

The dispersion in traders’ beliefs about future macroeconomic announcements may

result in variation in the level of private information (or NCK information), before and

after the release of announcements. Following Green (2004) and Andersen et al. (2007),

we focus on transactions during the 30 minutes prior to and the 30 minutes after the

release of macroeconomic announcements to determine the degree to which

information asymmetry changes. To address changes in the components of trading costs

around the time of announcement, we modify the MRR model:

)11()()(

)()()()(

11),30(1)30(1)30(1)30(111),30(1)30(1)30(1)30(1

1000),30(1)30(1)30(1),30(1)30(1)30(100

ttttt

ttttttt

eXIXI

XXIXIXR

where 1),30(1 tI if t is in the half-hour before announcements, and 0 otherwise; and

1),30(1 tI if t is in the half-hour after announcements, and 0 otherwise. Furthermore,

0 refers to the degree of information asymmetry on days without macroeconomic

17

announcements. In turn, )30(10 and )30(10 measure the adverse selection

cost for trades during the half-hours before and after announcements, respectively. Then

0 indicates the costs of providing liquidity when there is no announcement;

)30(10 and )30(10 measure the liquidity-providing costs in the half-hours

before and after announcements, respectively. Finally, with )30(1 and )30(1 we can

examine how the first-order autocorrelation in tX varies across the half-hours before

and after the announcement.

We use the GMM procedure to estimate the parameters in Equation (11). If we

express

11),30(1)30(111),30(1)30(110 ttttttt XIXIXX (12)

and

11),30(1)30(1)30(1)30(1

1)30(1)30(1)30(11000

),30(1)30(1)30(1)30(1)30(1)30(100

)(

)()(

)()()(

tt

tt

tttttt

XI

XX

XIXIXRu

, (13)

then the following population moments identify the parameter vector,

),,,,,,,,,( )30(1)30(10)30(1)30(10)30(1)30(10 , from Equation (11):

0

)(

)()(

)(

)(

)(

11),30(1

11),30(1

1

),30(1

),30(1

11),30(1

11),30(1

1

ttt

ttt

tt

ttt

ttt

tt

t

ttt

ttt

tt

XIu

XIuXu

XIu

XIu

Xuu

XI

XI

X

E

. (14)

In Panel A of Table 6, we reveal that the cost of informed trading varies across the

periods before and after an announcement. The significant and positive estimate of

)30(1 suggests that the level of information asymmetry in the half-hour following

macroeconomic announcements is higher than that on days without macroeconomic

announcements. The significantly negative 1(30)- for the half-hour before an

announcement also shows that the adverse selection cost before an announcement is

18

lower than the usual level for periods on days without announcements. As Green (2004)

suggests, no information leaks before the release of an announcement; similarly, we

concur with He et al. (2009), who observe that the cost of informed trading is higher

following announcements but lower after announcements.

After the announcement, traders hold varying expectations about the equilibrium

exchange rate; therefore, compensation for trading with informed traders increases.

Although informed traders may exploit their information advantage and trade before the

announcement, our evidence suggests that their private or NCK information does not

disperse to other uninformed traders soon enough, such that information appears rather

slow. Following the announcement though, traders’ heterogeneous expectations may

result from their varied interpretations of the implications of macroeconomic

announcements.

Yet the liquidity-supplying cost is greater than usual before the announcement,

and lower than usual after it, as indicated by the estimates of )15(1 and )15(1 . This

evidence of variation in around announcements is consistent with Green’s (2004)

findings. We thus argue that increased price uncertainty before announcements makes

traders more reluctant to trade, whereas increased trading activity and reduced price

uncertainty make them more willing to provide liquidity. We also observe a reduced

first-order autocorrelation in tX before the announcement but a higher level after the

announcement; trades cluster more after the information release. Consistent with Green

(2004), we find that the orders are more likely to be separated into different trades,

following macroeconomic announcements.

5.4. Variation in spread components around announcements

We examine the effect of public information flows and variation in the informational

role of trading across different 15-minute intervals. Similar to Green (2004), we divide

each half-hour into two 15-minute segments and modify the MRR model:

)15()()(

)()(

)()()(

)()()(

11),15(2)15(2)15(2)15(211),15(1)15(1)15(1)15(1

11),15(1)15(1)15(1)15(111),15(2)15(2)15(2)15(2

1000),15(2)15(2)15(2),15(1)15(1)15(1

),15(1)15(1)15(1),15(2)15(2)15(200

ttttt

tttt

ttttt

tttttt

eXIXI

XIXI

XXIXI

XIXIXR

where 1),15(2 tI if the transaction takes place in the 30 to 15 minutes before the

announcement, and 0 otherwise. The dummy variables tI ),15(1 , tI ),15(1 , and tI ),15(2 ,

respectively, designate trades within the 15 minutes before the announcement, 15

19

minutes after the announcement, or from 15 to 30 minutes after the announcement.

Then 0 and 0 capture the adverse selection and liquidity-providing costs for the

transactions on a day without an announcement, whereas )15(20 , )15(10 ,

)15(10 , and )15(20 represent the costs of informed trading for trades in the 30

to 15 minutes before the announcement, 15 minutes before the announcement, 15

minutes after the announcement, and 15 to 30 minutes after the announcement,

respectively. Similarly, )15(20 , )15(10 , )15(10 , and )15(20 capture

the cost of providing liquidity across these different 15-minute periods. Furthermore,

)15(20 , )15(10 , )15(10 , and )15(20 capture the first-order

autocorrelation coefficients of order flow in the different 15-minute intervals.

To estimate the parameters in Equation (15), we first must specify

)16(11),15(2)15(211),15(1)15(1

11),15(1)15(111),15(2)15(210

tttt

ttttttt

XIXI

XIXIXX

and

)17()()(

)()(

)()()(

)()()(

11),15(2)15(2)15(2)15(211),15(1)15(1)15(1)15(1

11),15(1)15(1)15(1)15(111),15(2)15(2)15(2)15(2

1000),15(2)15(2)15(2),15(1)15(1)15(1

),15(1)15(1)15(1),15(2)15(2)15(200

tttt

tttt

ttttt

ttttttt

XIXI

XIXI

XXIXI

XIXIXRu

,

such that we can use population moments to identify the parameters in Equation (15):

0

)(

)()(

)(

)(

)(

11),30(1

11),30(1

1

),15(2

)15(2

11),15(2

11),15(2

1

ttt

ttt

tt

ttt

tt

tt

t

ttt

ttt

tt

XIu

XIuXu

XIu

XIu

Xuu

XI

XI

X

E

. (18)

According to the estimation results in Table 7, the cost of informed trading is

20

higher than usual after the announcement, because we find positive estimates of )15(1

and )15(2 . The adverse selection cost is highest in the 15 minutes immediately after

the announcement, then returns to near-normal levels in the next 15 minutes, along with

slightly lower trading intensity. The null hypothesis that )15(2)15(1 is rejected at

the 0.01 level. The quick decline in the adverse selection cost is consistent with Green

(2004) and supports our prediction that market participants differ in their interpretations

of the implications of macroeconomic news for the equilibrium exchange rate.

Negative estimates of )15(2 and )15(1 also suggest that the level of

information asymmetry in the two 15-minute intervals before the announcement is

lower than that during a period without any announcement. We can reject

)15(1)15(2 at a 0.01 level, which suggests there is an increase in the information

role of trading between the two 15-minute intervals leading up to announcement

releases. However, both estimates of adverse selection cost are lower than that during

the period without announcements. The results provide no evidence that FX dealers

trade speculatively in the half-hour before announcements on the EBS.

The estimation results for across the four 15-minute intervals show that the

cost of liquidity provision is highest in the 15 to 30 minutes prior to the announcement;

it declines, though still to a level higher than the no-announcement level, in the 15

minutes before the announcement. In the after-announcement period, the cost of

liquidity provision continues to decline, reaching the lowest level in the first 15 minutes,

after which it increases to the no-announcement level in the next 15 minutes.

Consistent with Green (2004) and He et al. (2009), we find that the informational

role of trading increases with the release of announcements, and the impact of order

flow on prices may be higher than usual after announcements. Before the release of

announcements, the level of information asymmetry decreases as the time of release

approaches. After the announcement release, the level of information asymmetry rises

in the 15 minutes immediately after the announcement. The order processing and

inventory cost components then follow a pattern opposite to pattern we find for adverse

selection costs in the four 15-minute intervals surrounding announcements.

As we show in Panel B, the information content of transactions increases with

trading volume; informed traders’ informative advantage declines quickly because other

uninformed traders find out the true exchange rate through their transactions with

21

informed traders. Information revealed by informed traders’ order flow induces

uninformed traders to revise their prices quickly. Therefore, in the second 15-minute

interval after the announcement, the level of information asymmetry declines.

6. Conclusions

We study the informational role of trading by examining variation in the component of

bid-ask spreads related to information asymmetry. Using actual trading data on the EBS

spot EUR–USD market, we find that the magnitude of information asymmetry changes

with the day of the week: The adverse-selection cost is highest on Monday, but order

processing and inventory costs are highest on Friday.

By studying intraday dynamics in spread components, we offer implications for

the timing of FX trades and the design of profitable intraday trading rules. Overall,

adverse selection and liquidity-providing costs exhibit time-of-day effects, and the

intraday pattern in these costs reflects the trading sessions of local markets. Specifically,

the patterns of intraday trading activity and trading cost components reveal a positive

relation between liquidity and the cost of informed trading, whereas the compensation

for liquidity provision is negatively associated with trading volume in the EBS

EUR–USD spot market. The level of information asymmetry is highest when both

London and New York markets are operational, but the liquidity-providing cost is the

lowest during this interval.

We also provide evidence that the level of information asymmetry in the spot

EUR–USD market varies around the release of U.S. and European announcements. By

considering the influence of macroeconomic news releases in structural models

proposed by MRR (1997), we observe that immediately after the release of

macroeconomic announcements, the cost of informed trading increases. The adverse

selection cost is lower in the half-hour before announcements, but order processing and

inventory holding costs rise in this time. The heavy trading volume that follows an

announcement induces more information asymmetry among traders, though the cost of

liquidity provision decreases if the market is more liquid. By dividing the half-hours

before and after the announcement into four 15-minute intervals, we find that the level

of information asymmetry reflects an inverted U-shape around the time of the

announcements, consistent with Green (2004) and He et al. (2009).

22

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26

Table 1

Scheduled Macroeconomic Announcements The data encompass the period from January 1, 2004, to December 31, 2005. The number of observations column includes the total observations in the announcement sample. The sources for the U.S. data are the Bureau of Census (BC), Bureau of Labor Statistics (BLS), Bureau of Economic Analysis (BEA), Federal Reserve Board (FRB), National Association of Purchasing Managers (NAPM), and the Conference Board (CB). The source of the Euro area data is the Economic and Financial Affairs (EFA) office of the European Commission. The monthly composite NAPM index gives the earliest indication of the health of the U.S. manufacturing sector. The FRB releases the U.S. target federal fund rate every six weeks and money supply every week. Announcement Local time Number of observations Source

US announcements

GDP 8:30 EST 24 BEA

Nonfarm Payroll Employment 8:30 EST 24 BLS

Retail Sales 8:30 EST 24 BC

Personal Income 8:30 EST 24 BEA

Personal Consumption Expenditures 8:30 EST 24 BEA

Durable Goods Orders 8:30 EST 24 BC

Trade Balance 8:30 EST 24 BEA

Producer Price Index 8:30 EST 24 BLS

Consumer Price Index 8:30 EST 24 BLS

Housing Starts 8:30 EST 23 BC

Initial Unemployment Claim 8:30 EST 24 BLS

New Home Sales 10:00 EST 24 BC

Construction Spending 10:00 EST 23 BC

Factory Orders 10:00 EST 24 BC

Business Inventories 10:00 EST 23 BC

Consumer Confidence Index 10:00 EST 24 CB

NAPM Index 10:00 EST 24 NAPM

Index of Leading Indicators 10:00 EST 24 CB

Target federal fund rate 14:15 EST 17 FRB

Money Supply (M1, M2, M3) 16:30 EST 104 FRB

Euro area announcements

PPI 11:00 GMT 24 EFA

Harmonized CPI 11:00 GMT 24 EFA

27

Industrial production 11:00 GMT 24 EFA

Trade balance 11:00 GMT 24 EFA

Retail sales 11:00 GMT 24 EFA

Unemployment rate 11:00 GMT 24 EFA

28

Table 2

Trading Hours in Foreign Exchange Markets: Tokyo, London, and New York

Trading center Trading hours (local time) Trading hours (GMT)

New York 09:00–16:00 EST 14:00–21:00 GMT

London 08:00–16:00 GMT 08:00–16:00 GMT

Tokyo 08:00–16:00 JST 23:00–07:00 (next day) GMT

29

Table 3 Estimated Spread Components for the EBS EUR–USD The standard MRR model used to estimate spread components of the adverse-selection, order-processing, and inventory costs is

tttt eXXR 1)()( , where tR is 10,000 times the change of

transaction prices from time t to time t – 1; tX is an indicator variable for trade

initiation, with 1tX if the trade is buyer initiated and 1tX if the trade is

seller initiated; denotes the first-order autocorrelation coefficient in order flow tX ;

and te is the error term. The parameter measures the adverse-selection cost, and

measures the cost of order processing and inventory holding. This table reports GMM estimates of these coefficients, with standard errors in parentheses. *Significant at the 1% level.

Cost of adverse selection

( )

Cost of liquidity

provision ( )

Autocorrelation of order

flow ( )

0.1218* 0.1322* 0.1042*

(0.0003) (0.0002) (0.0010)

30

Table 4

Variation in Spread Components over the Week

Panel A reports the GMM estimation results of the modified MRR model,

5

111,

5

1, )()(

jtttjjjj

jttjjjt eXDXDR , where tjD , (j = 1, …, 5) refers

to the day-of-the-week dummies for Mondays–Fridays. Panel B reports the average numbers of trades for Sunday–Friday. The sample period is from January 1, 2004, to December 31, 2005; we exclude data for Sundays and holidays. Standard errors are reported in parentheses. We also show coefficient restrictions and associated Wald F-test values. *Significant at the 1% level. Panel A: Spread components Adverse-selection cost:

1 2 3 4 5

0.1271* 0.1125* 0.1125* 0.1192* 0.1217*

(0.0007) (0.0006) (0.0006) (0.0007) (0.0008)

54321 ,

F = 86.77*

Order-processing and inventory-holding costs:

1 2 3 4 5

0.1127* 0.1274* 0.1311* 0.1311* 0.1362*

(0.0006) (0.0005) (0.0005) (0.0006) (0.0007)

54321 ,

F = 258.20*

Autocorrelation of order flow:

1 2 3 4 5

0.0948* 0.1033* 0.1013* 0.1002* 0.1049*

(0.0010) (0.0009) (0.0009) (0.0009) (0.0011)

54321 ,

F= 17.01*

Panel B: Average numbers of trades

Sunday Monday Tuesday Wednesday Thursday Friday

407 12,141 15,129 16,033 15,423 13,561

31

Table 5 Variation in Information Asymmetry: New York and London Trading hours Panel A reports the GMM estimation results of the modified MRR model, :

tttNYLNNYLNNYLNNYLNttNYNYNYNY

ttLNLNLNLNtttNYLNNYLNNYLN

ttNYNYNYttLNLNLNtt

eXIXI

XIXXI

XIXIXR

11,&&&&11,

11,1000,&&

,,00

)()(

)()()(

)()()(

where tLNI , indicates the trading hours in London before the beginning of New York

trading hours; tNYI , indicates trading hours in New York after the end of trading hours

in London; and tNYLNI ,& indicates the overlapping trading hours in New York and

London. Each variable equals 1 if t is in the related period and 0 otherwise. Panel B reports the average numbers of trades for London-only, overlapping, New York-only, and non-trading hours (per hour). The data encompass January 1, 2004, to December 31, 2005. Standard errors are reported in parentheses. We also show the coefficient restrictions and associated Wald F-test values. *Significant at the 1% level. Panel A: Spread components Adverse-selection cost:

0 LN NYLN & NY

0.0861* 0.0602* 0.0798* -0.0070*

(0.0005) (0.0007) (0.0007) (0.0009)

Coefficient restriction:

NYNYLNLN & , F = 5022.59*

Liquidity-providing cost:

0 LN NYLN & NY

0.1649* -0.0792* -0.0827* 0.0344*

(0.0006) (0.0008) (0.0008) (0.0011)


NYNYLNLN & , F = 6438.96*

Autocorrelation of order flow

0 LN NYLN & NY

0.0982* 0.0131* 0.0798* -0.0387*

(0.0008) (0.0011) (0.0012) (0.0014)


NYNYLNLN & , F = 773.65*

Panel B: Average numbers of trades across trading hours (per hour)

Non-trading London and New York

London trading hours

Overlapping trading hours in London and New York

New York trading hours

520 917 1385 443

32

Table 6 Effects of Macroeconomic Announcements on Spread Components for EBS EUR–USD spot transactions Panel A reports the GMM estimates for the modified MRR model that considers the effects of macroeconomic announcements,

ttttt

ttttttt

eXIXI

XXIXIXR

11),30(1)30(1)30(1)30(111),30(1)30(1)30(1)30(1

1000),30(1)30(1)30(1),30(1)30(1)30(100

)()(

)()()()(

where 1),30(1 tI if t is within the half-hour before an announcement, and 0 otherwise;

and 1),30(1 tI if t is within the half-hour after an announcements, and 0 otherwise.

We use the trading data for the half-hours before and after the release of macroeconomic announcements in the United States and Europe, that is, 10:30–11:30 (for releases at 11:00 GMT), 13:00–14:00 GMT (for releases at 13:30 GMT or 8:30 EST), 14:30–15:30 (for releases at 15:00 GMT or 10:00 EST), 16:45–17:45 GMT (for releases at 17:15 or 14:15 EST), and 20:30–21:30 GMT (for releases at 21:30 GMT or 16:30 EST). Panel B reports the average numbers of trades in the half-hours before and after an announcement. Standard errors are reported in parentheses. We also show the coefficient restrictions and associated Wald F-test values. **Significant at the 1% level. *Significant at the 5% level. Panel A: Spread components of the spread in 30 minutes before and after macroeconomic announcements Adverse-selection cost:

0 )30(1 )30(1

0.1490** -0.0066** 0.0380**

(0.0005) (0.0018) (0.0027)


)30(1)30(1 , F = 187.99**

Liquidity-providing cost:

0 )30(1 )30(1

0.0841** 0.0017* -0.0050*

(0.0005) (0.0010) (0.0030)


)30(1)30(1 , F = 3.45*


0 )30(1 )30(1

0.0966** -0.0224** 0.0327**

(0.0009) (0.0038) (0.0040)


)30(1)30(1 , F = 104.54**


30-minute interval before macroeconomic announcements

30-minute interval after macroeconomic announcements

606 718

33

Table 7

Levels of Information Asymmetry Surrounding News Announcements (II)

Panel A reports the GMM estimation results of modified MRR model for adverse-selection and liquidity-providing costs in the half-hour before and after macroeconomic announcements,

ttttt

tttt

ttttt

tttttt

eXIXI

XIXI

XXIXI

XIXIXR

11),15(2)15(2)15(2)15(211),15(1)15(1)15(1)15(1

11),15(1)15(1)15(1)15(111),15(2)15(2)15(2)15(2

1000),15(2)15(2)15(2),15(1)15(1)15(1

),15(1)15(1)15(1),15(2)15(2)15(200

)()(

)()(

)()()(

)()()(

,

where 1),15(2 tI if t is 30–15 minutes before the announcement, and 0 otherwise. For the transaction

taking place in the 15 minutes before an announcement, tI ),15(1 = 1, and 0 otherwise. Similarly,

1),15(1 tI if t is in the 15 minutes after an announcement, and 0 otherwise, and 1),15(2 tI if t is within

15–30 minutes after an announcement. Panel B reports the average numbers of trades in each 15-minute interval. Standard errors are reported in parentheses. We also show the coefficient restrictions and associated Wald F-test values. **Significant at the 1% level. *Significant at the 5% level.

Panel A: Information asymmetry cost and order processing and inventory costs in 30 minutes before and after macroeconomic announcements Information asymmetry cost:

0 )15(2 )15(1 )15(1 )15(2

0.1490** -0.0116** -0.0022* 0.0523** 0.0025*

(0.0007) (0.0024) (0.0012) (0.0044) (0.0015)

Coefficient restrictions:

)15(2)15(1)15(1)15(2 , F = 217.02**

)15(2)15(1 , F = 6.97**

)15(2)15(1 , F = 26.46**

Liquidity-providing cost :

0 )15(2 )15(1 )15(1 )15(2

0.0841** 0.0032* 0.0009* -0.0052* -0.0003

(0.0005) (0.0015) (0.0004) (0.0030) (0.0004)


)15(2)15(1)15(1)15(2 , F = 8.87**

)15(2)15(1 , F = 4.03*

)15(2)15(1 , F = 4.44*


0 )15(2 )15(1 )15(1 )15(2

0.0966** -0.0307** -0.0132** 0.0119** 0.0549**

(0.0009) (0.0053) (0.0054) (0.0055) (0.0055)


)15(2)15(1)15(1)15(2 , F =143.17**

)15(1)15(2 , F = 5.50*

)15(2)15(1 , F = 31.25**


Interval of 15-30 minutes prior to announcements

15-minute interval before announcements

15-minute interval after

announcements

Interval of 15-30 minutes after announcements

293 313 387 330

Documents

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