· web viewas a robustness test, we estimate the spread components using the methodology of...
TRANSCRIPT
The Cost of Trading during Federal Funds Rate Announcements: Evidence from Cross-listed Stocks
Bart Frijnsa, Ivan Indriawana,*, Yoichi Otsubob, Alireza Tourani-Rada
a Department of Finance, Auckland University of Technology, Auckland, New Zealandb Alliance Manchester Business School, University of Manchester, Manchester, United Kingdom
Abstract
We investigate the behavior of bid-ask spread components around U.S. Federal Funds Rate
announcement times for a sample of Canadian firms that are cross-listed in the U.S. We use
transaction-level data to decompose the spread into its three components, namely,
information asymmetry, order persistence, and order processing costs. We observe that at
times of news announcements, the information asymmetry component increases more in
Canada than in the U.S., indicating that trades in Canada are more information-driven than
trades in the U.S. We further find that the order persistence component increases more in the
U.S. than in Canada, indicating that there is a temporary price pressure surrounding the news
announcement period in the U.S.
JEL Classification: G1, C58, E44
Keywords: Macroeconomic News Announcements, Bid-Ask Spreads, Spread Decomposition
We thank Roberto Pascual and participants at the 2015 Auckland Finance Meeting, the 2016 New Zealand Finance Colloquium, the 2017 Asian Finance Association Conference, the 2017 Japanese Economic Association Autumn Meeting, the 2017 Japan Society of Monetary Economics Autumn Meeting, and the University of Otago finance seminar for constructive comments. The authors also acknowledge financial support from Auckland University of Technology (Research Grant RP2016-04).
*Corresponding author. Tel.: +64 9 921-9999 ext. 5061. E-mail address: [email protected] (I. Indriawan).
1
The Cost of Trading during Federal Funds Rate Announcements: Evidence from Cross-listed Stocks
Abstract
We investigate the behavior of bid-ask spread components around U.S. Federal Funds Rate
announcement times for a sample of Canadian firms that are cross-listed in the U.S. We use
transaction-level data to decompose the spread into its three components, namely,
information asymmetry, order persistence, and order processing costs. We observe that at
times of news announcements, the information asymmetry component increases more in
Canada than in the U.S., indicating that trades in Canada are more information-driven than
trades in the U.S. We further find that the order persistence component increases more in the
U.S. than in Canada, indicating that there is a temporary price pressure surrounding the news
announcement period in the U.S.
JEL Classification: G1, C58, E44
Keywords: Macroeconomic News Announcements, Bid-Ask Spreads, Spread Decomposition
2
1. Introduction
This paper investigates the effect of Federal Funds Rate (henceforth FOMC)
announcements on various components of the bid-ask spread in the context of cross-listed
stocks. Bid-ask spreads generally widen before a news release and remain so for a short while
after the announcement. Ederington and Lee (1995), for example, document short-run
reactions in spreads of interest rate and foreign exchange futures markets at times of
scheduled macroeconomic news releases. Fleming and Remolona (1999) and Balduzzi et al.
(2001) find that scheduled macroeconomic announcements significantly affect the U.S.
Treasury market, where bid-ask spreads of U.S. Treasury bills widen dramatically at
announcements and remain moderately wide as investors trade to reconcile residual
differences in their private views. More recently, Smales (2018) documents the effect of
FOMC announcements on the bid-ask spread of crude oil futures.
Various hypotheses have been suggested and tested empirically to explain the
widening of bid-ask spreads. Balduzzi et al. (2001) offer two explanations. First, the bid-ask
spread acts as an "option to trade" offered by market makers to traders. As volatility increases
because of the announcement, the value of the option increases, leading to a widening of the
spread (Copeland and Galai, 1983; Ho and Stoll, 1981). Second, bid-ask spreads widen due to
asymmetric information as traders may have differing abilities to process information from
macroeconomic news. In line with this argument, Frino and Hill (2001) show that the
widening in bid-ask spreads around announcements is consistent with market makers
adjusting spreads in response to increased information asymmetry. Fleming and Remolona
(1999) attribute the widening in spreads to market makers controlling inventory risk at a time
of extreme price volatility. One reason why the extant literature has provided various
explanations for the widening of the spread may have to do with the fact that these studies
focus on the total bid-ask spread, rather than its individual components which is the main
focus of the current study.
To explain why spreads widen during a major news release, we focus on assessing the
components of the bid-ask spread during FOMC announcements.1 These announcements
convey price-relevant information and their timing is largely predictable. Since market
makers revise bid and ask quotes following the arrival of new information, the changes in
1While FOMC announcements are pre-scheduled and their outcome is often well expected, a strong reaction in the bid-ask spread surrounding the FOMC announcement has been documented (see e.g. Scholtus et al. (2014) and Figure 1 of this paper).
3
spreads can be seen as a consequence of the trading activity of various market participants. In
this regard, spread decomposition models allow us to examine the effect of temporary
information asymmetries that exists among market participants.
In this paper, we focus on a sample of Canadian listed stocks that are cross-listed in
the U.S. for the period January 2004 to January 2011. For these stocks, we examine the extent
to which FOMC announcements affect the spread components in these two markets. We
employ the model of Lin et al. (1995) and decompose the spread into three components to
reflect information asymmetry, order persistence, and order processing costs. We then
compare these components on days with and without scheduled FOMC announcements.
The use of cross-listed stocks enables us to assess a market maker’s pricing strategy
across different markets. Several extant studies, such as Bacidore and Sofianos (2002), Eun
and Sabherwal (2003), and Brogaard et al. (2015), suggest that cross-listed stocks have
different market making properties. When a non-U.S. security trades on an established
exchange in the U.S., what to expect is not trivial with regard to which market makers
account for more information asymmetry. On the one hand, local investors in the security’s
home market may be more familiar with local companies and may be better at assessing the
implications of a macroeconomic news release for that specific company (see e.g. Bae et al.
(2008) and Giannini et al. (2014) who document that local analysts and investors are more
informed about local companies). This would imply that Canadian market makers may put
more weight on the information asymmetry component as they expect to encounter more
informed traders. On the other hand, the dominance of the U.S. stock exchanges, being
among the largest and most liquid exchanges in the world, suggests that they are likely to
attract more traders, including informed ones. Examining the components of the spread
around FOMC announcements allows us to shed light on which argument has more empirical
support.
Comparing spread components during days with and without FOMC announcements,
we document several interesting findings. First, consistent with the literature, bid-ask spreads
in both the U.S. and Canadian markets widen around the announcement time, confirming the
strong relation between stock prices and macroeconomic news. Second, we find that around
news announcements, the information asymmetry component of the spread increases more in
Canada than in the U.S., indicating that trades in Canada are more informative than trades in
the U.S. This finding is in line with our first argument that local investors in the security’s
4
home market are more familiar with local companies and are better at assessing the
implications of a macroeconomic news release for that specific company. Third, the order
persistence component of the spread increases more in the U.S. than in Canada, suggesting a
temporary price pressure surrounding news announcement period in the U.S. market.
We structure the remainder of this paper as follows. Section 2 discusses the related
literature and presents our main arguments. Section 3 contains the methodology used in the
paper. Section 4 describes the data. Section 5 reports our empirical findings and their
interpretations. Finally, Section 6 concludes.
2. Impact of News Announcements on Components of Bid-Ask Spread
In this section, we discuss how news announcements can affect the various
components of the bid ask spread by first focusing on the extant literature in this area. We
then present our main arguments as to why there can be differences in spread components for
cross-listed stocks.
Several studies have investigated the impact of corporate announcements on
components of the spread. For example, Krinsky and Lee (1996) investigate the behavior of
bid-ask spread components around corporate earnings announcements. They find that the
information asymmetry cost component significantly increases surrounding the
announcements, while the inventory holding and order processing components significantly
decline. They conclude that earnings announcements may have an insignificant impact on the
total bid-ask spread, even when they result in increased information asymmetry. Ascioglu et
al. (2002) assess the impact of merger announcements on the components of the bid-ask
spread for U.S. stocks. They do not observe any widening of the bid-ask spread before the
announcement day. However, they find evidence of a narrower spread and a lower
information asymmetry component of the spread that persists after the announcement. They
conclude that trading after the merger announcement is liquidity motivated. More recently,
Riordan et al. (2013) examine the impact of earnings announcements on liquidity. They find
that information asymmetry increases before the announcement because market participants
put different levels of effort into information gathering and thus possess different levels of
private information. After the announcement, information asymmetry remains high because
5
different traders have varying capabilities to interpret earnings announcements with relation
to the stocks, a finding which is consistent with Kim and Verrecchia (1994).
Our study extends the above literature by focusing on macroeconomic news,
particularly the FOMC announcements. We focus on these announcements for several
reasons. First, the timing of the FOMC announcement is largely predictable, which helps
investors with short investment horizons to focus on information sets specific to that period.2
The period surrounding the announcement is therefore crucial for investors, even if
information related to the upcoming interest rate may have been publicly available (through
speeches, interviews, etc.). Second, vital macroeconomic information is not disclosed until
the scheduled release time. In the case of FOMC announcements, the Federal Reserve Bank
provides accredited news outlets with pre-release access to the information under embargo
agreements. The accredited journalists receive the data prior to the public release (typically in
press lockup facilities) to allow time for clarifying questions and preparing reports, but are
unable to relay the information to the public until the official release time. This lockup
practice prevents information leakage that would give some traders an unfair advantage (see
e.g. Krishnamurti and Thong, 2008; Bernile et al., 2016; Hu et al., 2017), hence limiting the
source of asymmetric information which may affect the market.
Focusing on FOMC announcements also provides a unique setting to examine the
information processing capacity of market participants. As there will be little to no
information leakage prior to the FOMC announcement, we would not expect the information
asymmetry component to change prior to the FOMC announcement. After the FOMC
announcement, information asymmetry may increase due to the differing ability to process
information, and is expected to rise more in the market that has the faster processing capacity.
Our setting contrasts a study that focusses on information asymmetry that may arise around
corporate announcements, where traders can be privately informed about the company.3
We conjecture that the proportion of the bid-ask spread due to its different
components may change during prescheduled announcement times for the following reasons.
First, there is the asymmetric information argument, which predicts a widening of the spread
because of the fear on the part of market makers that traders may be better informed (Glosten
2Sims (2003) and Lucca and Moench (2015) explain that investors often face constraints as to how much information they can process. Consequently, they focus on short investment horizons to process information sets specific to that period. 3A possible extension to our work would be to examine the impact of earnings announcements on trading costs of cross-listed stocks.
6
and Milgrom, 1985 and Glosten, 1987). Since there should be no information leakage prior to
news announcements, and since trade-unrelated information is quickly disseminated to all
market participants in a widespread manner, asymmetry arises following the news release not
because different information is received by traders, but because traders have different
abilities to process information (Pascual et al., 2006). These different abilities raise the level
of information asymmetry in the market. In such a case, the typical information asymmetry
model (e.g., Copeland and Galai, 1983 and Glosten and Milgrom, 1985) will predict that the
spread, and in particular the information asymmetry component of the spread, should increase
during news announcements.
Second, the widening of the spread during announcement times can be attributed to an
increase in order persistence. High order persistence may occur when traders split large
orders into smaller ones (Barclay and Hendershott, 2004). High order persistence may also
occur if there is a temporary buying or selling pressure. Such a price pressure may occur due
to correlated liquidity demand (e.g. correlated demand of investors for rebalancing purposes).
Since market makers cannot distinguish the individual quantities traded by the different types
of traders, they set prices based on the aggregate quantities traded by all traders combined.
This results in a wider bid-ask spread.
Third, the FOMC announcement can also affect the order processing component of
the bid-ask spread. Copeland and Stoll (1990) argue that the average order processing cost
per share should decrease as trading volume increases because the order costs represent fixed
clerical expenses of carrying out a transaction. An increase in trading volume will create
economies of scale where the fixed costs incurred by the market makers are spread over more
shares. Therefore, we expect order processing costs to decrease around the time of an FOMC
announcement.
3. Methodology
We employ the model of Lin et al. (1995) to decompose the spread into its three
components, information asymmetry, order persistence, and order processing costs. This
decomposition model offers an advantage in our high-frequency setting because it does not
rely on inventory-induced effective spread like other decomposition models (e.g. Amihud and
7
Mendelson, 1980; Huang and Stoll, 1997). Specifically, the model does not necessitate trade
data to exhibit a high probability of order reversal (buy orders followed by sell orders, or vice
versa) to provide sensible estimates of the information asymmetry component of the spread.
Hence, the Lin et al. (1995) spread decomposition model is more fitting for intraday analysis
in a market where traders frequently split large orders into smaller ones and there is often
more than one trade per second. In addition, it is a model that focuses on decomposing the
effective spread and as such considers the actual prices at which securities trade.
Before decomposing the spread, we briefly explain the underlying model. Let At and
Bt be the ask and bid quotes at time t , respectively, and let ρ be the probability of order
persistence (a sell order followed by sell order, and vice versa). The expected future market
price conditional on a trade at time t+1 can be written as:
Et ( P t+1 )=ρ B t+1+(1−ρ ) A t+1 , (1)
where Pt is the transaction price at time t , and (1− ρ) is the probability of an order reversal.
Given this definition, and conditional on a sell order occurring at time t such that Pt=B t, a
market maker's expected gross profit at t+1 is:
Et ( P t+1 )−Pt=ρ Bt+1+(1− ρ ) A t+1−B t . (2)
Let Qt=( A t+Bt)/2 be the quote midpoint at time t and let z t=P t−Q t be the effective
half-spread. To reflect possible asymmetric information revealed by a trade at time t , quote
revisions are given as Bt+1=Bt+λ z t and At+1=A t+λ zt, where 0< λ<1 reflects the quote
revision in response to a trade as a fraction of the effective spread. A market maker's gross
profit for a sell order (transaction at the bid price) at time t is then related to the effective
spread by the following relation:
Et ( P t+1 )−Pt=ρ Bt+1+ (1− ρ ) A t+1−P t ,¿ λ zt+(1−2 ρ ) (Qt−Bt )+Qt−Pt¿−(1−λ−θ ) zt ,
(3)
8
where θ=2 ρ−1, and (1− λ−θ ) z t is the market maker’s expected profit. A market maker’s
gross profit for a buy order (transaction at the ask price) at time t can be obtained in a similar
fashion. The parameters in Equation (3) can be estimated using the following regressions:
ΔQt+1= λ z t+e t+1 , (4)
zt+1=θ zt+ηt+1 , (5)
where λ is the proportion of the spread due to asymmetric information, and θ is the
proportion of the spread due to order persistence, e t+1 and ηt+1 are the disturbance terms,
assumed to be uncorrelated. Using Equations (4) and (5) and given that z t+1≡ P t+1−Qt+1, the
order processing costs for the trade at time t is related to the effective spread, z t by the
following equation:
Pt+1−Pt=(Qt+1−Qt )+zt+1−z t Δ Pt+1=−γ zt+u t+1,
(6)
where γ=1−λ−θ and ut+1=et+1+ηt+1. Equation (6) shows that changes in transaction prices
are somehow predictable, i.e. they tend to bounce between bid and ask prices. This temporary
price effect reflects the dealer’s cost of order processing. Hence, the estimate, γ, is considered
as the order processing cost component of the spread.
4. Data and Sample Description
Our sample consists of 36 Canadian stocks which are listed on the Toronto Stock
Exchange (TSX) and cross-listed on the New York Stock Exchange (NYSE) for the period
January 1, 2004 to January 31, 2011. The end of the sample is chosen to avoid confounding
effects from the new Order Protection Rule in Canada, which became effective on February
9
1, 2011. Regular trading hours for the U.S. and Canadian markets are both from 9:30 AM to
4:00 PM (EST), allowing us to conduct intraday analysis for both markets.
We obtain transaction-level data (to the nearest millisecond) for prices, volume, bid-
ask quotes and bid-ask depths from Thomson Reuters Tick History (TRTH) maintained by
the Securities Industry Research Centre of Asia-Pacific. For the U.S. market, we use the
National Best Bid and Offer quotes for stocks with the NYSE as primary listings and for the
Canadian market, we use quotes posted at the TSX. We sometimes observe trades executed at
different prices but at the same time stamp. In such cases, we treat them as one trade. We
assign the appropriate price of the trade using value-weighted average and aggregate the
volume from multiple trades, attribute it to the first trade, and then remove the other trades
from the sample. To ensure the comparability of prices between the two markets, we obtain
intraday CAD/USD exchange rate quotes from TRTH and use the midpoint to convert
Canadian prices into U.S. dollar.
Our sample covers days with and days without FOMC announcements in both
markets. The Federal Funds Rate is announced every 6 weeks at 2:15PM (EST) during the
Federal Open Market Committee (FOMC) meeting. The dates of these meetings are obtained
from the Federal Reserve Bank website. There are 57 days with FOMC announcements and
430 days without announcements, leading to a total of 487 trading days considered over the
sample period.
Table 1 contains summary statistics for the stocks in our sample. It reports company
name, market capitalization, average daily price, trading volume, trade, and effective spread
for each stock in the U.S. and Canada. Our sample covers a broad set of firms with market
capitalization ranging from a minimum of $889 million to a maximum of $66 billion.
Average daily prices in the U.S. and Canada are $37.35 and $37.12, respectively. The
average trading volume in both markets are comparable, with 1,354,000 and 1,357,000 stocks
traded daily in the U.S. and Canada, respectively. The average daily trade, on the other hand,
is higher in the U.S. with 6,887 trades as compared to 4,221 trades in Canada, suggesting that
the average trade size in the U.S. is smaller than in Canada. The average daily effective
spreads in the U.S. and Canada are 7.30 bps and 8.79 bps, respectively, suggesting that cost
of trading, on average, is higher in Canada than in the U.S.
INSERT TABLE 1 HERE
10
Figure 1 illustrates the intraday effective bid-ask spreads for the stocks in the NYSE
and TSX. We plot the abnormal spread, which is the difference between the spread during
FOMC announcement days and the spread during non-announcement days. The difference is
then averaged across firms. We observe that abnormal spreads are relatively constant
throughout the trading day except for the period surrounding the FOMC announcement.
Spreads start to widen prior to the news release and peak at precisely 2:15pm when the new
interest rate is announced. Following the announcement, spreads decline gradually until
trading closes for the day. This figure shows that FOMC announcements indeed have a
pronounced impact on the bid-ask spread.
INSERT FIGURE 1 HERE
In Table 2, we report summary statistics for several market microstructure variables
during periods surrounding FOMC announcements and of the same period during non-
announcement days. The reported values are computed by averaging over the sample period
across firms.
INSERT TABLE 2 HERE
Panel A reports the statistics during the full 90-minute period from 1:45pm to 3:15pm.
We find that both in the U.S. and Canada: (1) the effective spreads (defined as twice the
difference between the transaction price and the quote midpoint) increase; (2) trading
volumes and trades increase; (3) volume per trade decreases, indicating that large orders are
split into smaller ones; (4) autocorrelation in order flow increases, reflecting that persistence
in trades has increased; and (5) both depths at the ask and bid prices reduce significantly. The
last two columns report difference-in-means statistics between the two markets. Apart from
the significant increase in U.S. effective spreads around news announcements relative to
Canada, we do not observe significant differences between the revisions in other
microstructure variables in both markets.
11
Panel B reports the statistics during the 30-minute period prior to the FOMC
announcements from 1:45pm to 2:15pm. We observe an increase in effective spreads,
consistent with Figure 1. Since neither trading volume nor number of trades change
considerably during this period, the increase in spreads seem to be driven by lower ask and
bid depths.
In Panel C, we observe stronger market reactions during the 60-minute period
following the FOMC announcements (2:15pm to 3:15pm). Focussing on the last two columns
of Panel C for the difference between the two markets, we find that the increase in trading
activity is greater in the U.S., leading to a significant difference in trading volume and
number of trades. We observe that traders split large orders almost equally in both markets.
Another interesting observation is that autocorrelation of order flow increases more
significantly in the U.S., indicating that persistence in trades is more substantial in the U.S.
compared to Canada. This finding highlights the importance of decomposing spreads into its
various components. In the next section, we examine the responses of various spread
components to FOMC announcements.
5. Empirical Findings
5.1. Spread Components during FOMC Announcements
To assess the components of the bid-ask spread, we estimate Equations (4) to (6) for
each of the 36 stocks in our sample. We decompose the spreads in the two markets
separately. This allows us to assess the possibility of any systematic variations in spread
components between the two markets. Table 3 reports the difference in the various
components of spreads during FOMC announcement and non-announcement days, where we
present the relative proportion of the various spread components, as well as the difference-in-
means and related t-statistics. The table suggests that the U.S. and Canadian markets have
different characteristics. Specifically, information asymmetry accounts for about 22% of the
total spread in the U.S., but about 36% in Canada, suggesting that, on average, there is greater
divergence in public perception of news. Order persistence accounts for 30% of the total
spread in both markets, indicating comparable degree of persistence in order flow. The order
12
processing component is higher in the U.S. than in Canada, suggesting that trading in the U.S.
involves a higher fixed cost than trading in Canada.
INSERT TABLE 3 HERE
Panel A reports the results for the 90-minute window surrounding the announcements
(30 minutes before and 60 minutes after). The first row of this panel shows that around
announcement times, information asymmetry increases by 0.67% in the U.S., and by 1.21%
in Canada. Hence, market makers in both markets take into account the probability of facing
informed traders around the FOMC announcement. Comparing the change in components
between the two markets, we observe that the TSX experiences a greater increase in the
information asymmetry component than the NYSE (by 0.54%). This finding is in line with
the argument that local investors in the security’s home market may be more familiar with
local companies and may be better at assessing the implications of a macroeconomic news
release for that specific company, and trade on this information quickly.
The second row of Panel A shows that the order persistence component also increases
around announcement times. Order persistence increases by 1.85% in the U.S. and by 0.80%
in Canada, with the difference being statistically significant at 1.05%. These figures indicate
that there is greater order persistence in the U.S. relative to Canada. These findings suggest
that liquidity-motivated trade increases more in the U.S. than in Canada. This could be due to
relatively light trading on the TSX, or more uninformed demand in the U.S. (we could think
of investors rebalancing portfolios in light of the new information).
The third row of Panel A shows that both markets experience a decrease in the order
processing component. This finding suggests that following an increase in trading activity,
market makers benefit from economies of scale and thus the cost of processing a transaction
can be lower relative to the other spread components (Copeland and Stoll, 1990).
Panel B reports the results for the 30-minute window prior to the announcements. We
observe neither a significant change in the spread components in each of the markets, nor a
significant difference between the changes of the two markets, suggesting that there is no
substantial change in the composition of trades. Our findings therefore confirm that that there
is no information leakage prior to the news release.
13
Panel C reports our results for the 60-minute window following the announcements.
The increase in the information asymmetry components is now very pronounced in both
markets (at 1.18% in the U.S. and 1.80% in Canada). We attribute this finding to the
increased divergence in interpretation of public news. Kim and Verrechia (1994) explain that
some market participants are inherently more informed (e.g. large shareholders, financial
analysts, etc.) than others. Information asymmetry may rise following FOMC announcements
because those informed market participants are now able to produce better assessments on a
firm's performance on the basis of the newly-released interest rate relative to other traders in
the market. This leads to an increased information asymmetry among market participants.
The difference between the changes in the two markets is significant at -0.63%, suggesting
that informed trades are more pronounced in Canada than in the U.S. The order persistence
component also increases significantly by 2.74% in the U.S. and 1.54% in Canada. The
difference between the two is 1.20% and statistically significant. This finding indicates a
greater increase in order flow persistence in the U.S. relative to Canada, which we attribute to
uninformed liquidity demand being more prevalent in the U.S. As expected, the order
processing component decreases significantly in both markets.
The findings in Table 3 suggest that different factors are considered by market makers
in determining their pricing strategy. Canadian market makers seem to put more weight on
the information asymmetry component as they expect to encounter more informed traders.
U.S. market makers put more weight on the order persistence component as they expect to
encounter more uninformed liquidity demand. These observations imply that Canada, being
the security’s home market, is still the market where most informed traders are active.
In addition to providing estimates of the proportional components of the spread, we
also calculate the dollar value of each component of the spread. Given that estimates of the
costs show exactly how much each spread component contributes to trading costs, these
estimates can be more informative for investors. Table 4 presents the spread components in
U.S. cents. These values are computed for each stock by multiplying each of the proportional
components with the effective spread. We then compute the average of these values across
firms.
INSERT TABLE 4 HERE
14
Panel A of Table 4 reports the spread components during the 90-minute period
surrounding the announcements. Consistent with Figure 1, effective spreads increase
significantly in the U.S. (by 0.307 cents) and in Canada (by 0.295 cents). In terms of the
economic magnitude of our results, the information asymmetry cost for an average stock in
the U.S. (with the price of $37.35 as documented in Table 1) increases by 0.095 cents, while
order persistence and order processing amounts increase by 0.154 and 0.058 cents,
respectively. These findings suggest that a big part of the spread increase in the U.S. is to
compensate market makers for the increase in order flow persistence. In Canada, information
asymmetry accounts for 0.149 cents of the spread increase, while the remaining 0.133 and
0.013 cents are contributed by the increase in order persistence and order processing costs,
respectively. From an investor’s perspective, these figures are important because they
indicate that in the U.S., the increase in spread is mainly caused by temporary liquidity-
driven shocks, while in Canada, the increase is driven by permanent shocks originating from
the news announcements.
Panel B reports the spread components in the 30-minute period prior to the
announcements. During this time, the increase in spread in the U.S. is 0.104 cents while in
Canada it is 0.081 cents. The majority of the increase in the U.S. is caused by the order
persistence component (0.052 cents) which accounts for 50% of the overall increase in
spread. Panel C shows the increase in trading costs in the 60-minute period following the
announcements. During this period, the effective spread increases the most. Of the 0.377
cents increase in the U.S., 0.185 cents is caused by order persistence, which accounts for 49%
of the increase in spread. In Canada, however, 0.202 of the 0.379 cents increase in spread is
caused by information asymmetry, thus accounting for 53% of the overall increase. Overall,
Table 4 shows that the information asymmetry component increases more in Canada than in
the U.S. while the order persistence component increases more in the U.S. than in Canada.4
5.2. Intraday Variations of Spread Components
We further assess the intraday variations of the various cost components using a
rolling window analysis. Specifically, we estimate Equations (4) to (6) using a 30-minute
rolling window. We roll the estimation window forward one trade at a time and obtain a
4As a robustness test, we estimate the spread components using the methodology of Madhavan et al. (1997). The results are comparable to those report here and are available upon request.
15
series of coefficients which are then averaged to the nearest minute. We then compute the
abnormal components of the spread which are obtained by subtracting the spread components
on non-announcement days from announcement days.
We start by evaluating the breakpoint when the abnormal spread components start to
depart from the stable state. We utilize the structural breakpoint identification framework of
Bai (1997), and Bai and Perron (1998, 2003).5 This framework enables us to detect the time
when market participants start to react to news announcement. The procedure is accompanied
by the associated confidence intervals and F-statistics, which are reported in Table 5.
INSERT TABLE 5 HERE
Table 5 reports the breakpoints for each of the spread components. The F-statistics
indicate strong statistical evidence of the existence of structural breaks in all the abnormal
spread components. Panel A shows that information asymmetry in the U.S. starts to deviate
from its stable state just before Canada (14:08 and 14:18). In Panel B, we observe that the
order persistence component starts to depart from the stable state earlier in Canada than in the
U.S. (13:47 against 14:11). Finally, Panel C suggests that the order processing components
also changed in similar timing in the U.S. and Canada. The significant F-statistics (sup F, ave
F and exp F) confirm that structural breaks are present across all the abnormal spread
components.
Further important results of Table 5 are reported in Columns 6 and 7 where we
indicate the direction of the change before (Pre) and after (Post) the detected break point.6
We observe a significant increasing trend in abnormal spread components for information
asymmetry and order persistence after the break, as indicated by the positive trend
coefficients for both U.S. and Canada. For the order processing component, a decreasing
trend is observed after the break. Overall, Table 5 shows that the spread components depart
from the stable state close to the announcement time.
To provide a visual confirmation of the tests, Figures 2 to 4 plot the abnormal
information asymmetry, the order persistence and the order processing cost components of 5For detailed description of the methodology, please refer to Appendix 1.6These coefficients are obtained from OLS regressions with a linear trend term before and after the detected break point. The linear model is specified by Equation (A.2) in Appendix 1.
16
the effective bid-ask spread throughout the day, respectively. In general, besides the period
surrounding the announcement, the abnormal components are relatively stable and mean
reverting.
INSERT FIGURE 2 HERE
Figure 2 shows that the abnormal information asymmetry component is relatively
constant during the day, except during the period following the news release when we
observe an increase in abnormal information asymmetry in both markets. The increase in
Canada is faster and steeper than in the U.S. It peaks at 14:45 and declines afterward. In the
U.S., the peak comes a bit later at 14:50. We attribute this finding to informed trades
occurring in Canada earlier than in the U.S. Both abnormal components remain high until
trading closes for the day, indicating differential public interpretation among market
participants regarding the effect of the news.
INSERT FIGURE 3 HERE
Figure 3 plots the intraday variation in the abnormal order persistence component.
The figure shows that abnormal order persistence is more volatile throughout the trading day
in either market, indicating trade clustering at different times of the day. For the U.S., the
abnormal order persistence component jumps significantly at 14:15, suggesting a surge in
price pressure as soon as the news is released. It remains elevated for another 30 minutes
before it gradually declines. For the Canadian market, we observe the increase is slower and
weaker, suggesting less persistence in order flow. Unlike information asymmetry, the
abnormal order persistence component reverts to the pre-break point level.
INSERT FIGURE 4 HERE
17
Figure 4 plots the intraday variation in the abnormal order processing component. In
both markets, the order processing component declines significantly following the
announcement. This finding confirms that order processing costs decrease following an
increase in trading activity during FOMC news announcements.
6. Conclusion
In this paper, we study the impact of FOMC announcements on the components of
bid-ask spread for a sample of Canadian cross-listed stocks in the U.S. Our analyses lead to
several interesting findings. First, we find that around FOMC announcements, information
asymmetry increases significantly more in Canada than in the U.S., indicating that trades in
Canada are more informative than trades in the U.S. This finding suggests that local investors
in the security’s home market may be more familiar with local companies and may be better
at assessing the implications of a macroeconomic news release for that specific company. The
order persistence component increases significantly more in the U.S. than in Canada,
indicating the effect of temporary price pressure surrounding news announcement period in
the former market. We attribute these differences to more uninformed liquidity demand in the
U.S., which could be due to portfolio rebalancing decisions after the announcement.
18
Appendix 1: Break Point Estimation and Testing Procedure
First, we set a window to be used for the breakpoint test. The peak (trough) of
abnormal information asymmetry and order persistence components (order processing
component) is identified by detecting the maximum (minimum) during regular trading hours.
The end of the window is then set to the timing when the peak (trough) is identified in the
previous step. Then, the point which is two hours before the peak (trough) is defined as the
starting point of the window. Finally, we apply the breakpoint test to the defined window
following the procedure suggested by Bai (1997) and Bai and Perron (1998, 2003).
In the test, the following standard linear regression model is considered
com p t=β0+β1 trend+ β2 com pt−1+μ t , ( t=1,…,T ) , (A.1)
where com p t is the abnormal component at time t , trend is the linear trend, and β are the
regression coefficients, which may vary over time. As indicated in Figures 3 to 5, there is a
breakpoint close to the announcement time, where the coefficients, especially one for the
linear trend, shift from one stable regression relationship to a different one. In other words,
there are two structures in which the model parameters are time invariant. Thus the model can
be written in such a form as
com p t=βi , 0+ βi , 1trend+β i ,2 com pt −1+μ t (t=t i−1+1 , …, ti ,i=1,2 ) , (A.2)
where i denotes the index for the two different model structures. Thus t ₁ is the breakpoint, τ ,
while t ₀=0, and t ₂=T . We estimate the breakpoint by minimizing the residual sum of
squares of Equation (A.2). For the computation of the confidence intervals of the estimated
breakpoints, the distribution in Bai (1997) is utilized.7
7Please see Zeileis et al. (2003) for the ideas behind the implementation. The breakpoint estimation are obtained by utilizing the ‘strucchange’ package for R provided by the authors.
19
Furthermore we test the hypothesis that the following regression coefficients remain
constant against the alternative that at least one coefficient varies over time, i.e. a structural
break.
H ₀: β1,0=β2,0 , β1,1=β2,1 ,∧β1,2=β2,2 (A.3)
The test rests on a series of F-statistics for a break at time τ . The OLS residuals μ(τ) from
Equation (A.2) are compared with the residuals μ from Equation (A.1) via
F τ=μ̂' μ̂− μ̂ (τ )' μ̂ (τ )
μ̂ (τ )' μ̂ (τ )/(T−2 ×3). (A.4)
These F-statistics are then computed for τ=τ , ... , τ . The sequence of F-statistics can be
aggregated in three alternative ways, suggested by Andrews (1993) and Andrews and
Ploberger (1994). The three statistics are the supremum, average and exp functional of the F-
statistics:
¿ F=su pT0 ≤ τ ≤τ F τ ,ave F= 1τ−τ+1∑τ =τ
τ
Ftexp F=log ( 1τ−τ+1 )∑
τ =τ
τ
exp (0.5× F τ¿).¿
The corresponding p-values are then computed based on Hansen (1997).
20
References
Amihud, Y., & Mendelson, H. (1980). Dealership market: Market-making with inventory. Journal of Financial Economics, 8, 31-53.
Andrews, D.W.K. (1993). Tests for Parameter Instability and Structural Change with Unknown Change Point. Econometrica, 61, 821-856.
Andrews, D. W. K., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica, 62, 1383-1414.
Ascioglu, N. A., McInish, T. H., & Wood, R. A. (2002). Merger announcements and trading. Journal of Financial Research, 25, 263-278.
Bae, K.-H., Stulz, R. M., & Tan, H. (2008). Do local analysts know more? A cross-country study of the performance of local analysts and foreign analysts. Journal of Financial Economics, 88(3), 581-606.
Bai, J. (1997). Estimation of a change point in multiple regression models. Review of Economics and Statistics, 79, 551-563.
Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural change models. Econometrica, 66, 47-78.
Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18, 1-22.
Balduzzi, P., Elton, E. J., & Green, T. C. (2001). Economic news and bond prices: Evidence from the U.S. Treasury market. Journal of Financial and Quantitative Analysis, 36, 523-543.
Barclay, M. J., & Hendershott, T. (2004). Liquidity externalities and adverse selection: Evidence from trading after hours. Journal of Finance, 59, 681-710.
Bernile, G., Hu, J., & Tang, Y. (2016). Can information be locked up? Informed trading ahead of macro-news announcements. Journal of Financial Economics, 121, 496 - 520.
Brogaard, J., Hendershott, T., & Riordan, R. (2018). Price Discovery without Trading: Evidence from Limit Orders. Working Paper.
Copeland, T. E., & Galai, D. (1983). Information effects on the bid-ask spread. Journal of Finance, 38, 1457-1469.
Copeland, T. E., & Stoll, H. R. (1990). Trading markets. In D. E. Logue (Ed.), Handbook of Modern Finance, 2nd ed. Boston: Warren, Gorham & Lamont.
Ederington, L. H., & Lee, J. H. (1995). The Short-run dynamics of the price adjustment to new information. Journal of Financial and Quantitative Analysis, 30, 117-134.
Eun, C. S., & Sabherwal, S. (2003). Cross-border listings and price discovery: Evidence from U.S.-listed Canadian stocks. Journal of Finance, 58, 549-575.
Fleming, M. J., & Remolona, E. M. (1999). Price formation and liquidity in the U.S. Treasury market: The response to public information. Journal of Finance, 54, 1901-1915.
21
Frino, A., & Hill, A. (2001). Intraday futures market behaviour around major scheduled macroeconomic announcements: Australian evidence. Journal of Banking and Finance, 25, 1319-1337.
Glosten, L. R. (1987). Components of the bid-ask spread and the statistical properties of transaction prices. Journal of Finance, 42, 1293-1307.
Glosten, L. R., & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics, 14, 71-100.
Hansen, B. E. (1997). Approximate asymptotic p-values for structural-change tests. Journal of Business and Economic Statistics, 15, 60-67.
Ho, T., & Stoll, H. R. (1981). Optimal dealer pricing under transactions and return uncertainty. Journal of Financial Economics, 9, 47-73.
Hu, G. X., Pan, J., & Wang, J. (2017). Early peek advantage? Efficient price discovery with tiered information disclosure. Journal of Financial Economics, 126, 399-421.
Huang, R. D., & Stoll, H. R. (1997). The components of the bid-ask spread: A general approach. Review of Financial Studies, 10, 995-1034.
Krishnamurti, C., & Thong, T. Y. (2008). Lockup expiration, insider selling and bid–ask spreads. International Review of Economics and Finance, 17, 230-244.
Kim, O., & Verrecchia, R. E. (1994). Market liquidity and volume around earnings announcements. Journal of Accounting and Economics, 17, 41-67.
Krinksky, I., & Lee, J. (1996). Earnings announcements and the components of the bid-ask spread. Journal of Finance, 51, 1523-1535.
Lin, J.-C., Sanger, G. C., & Booth, G. G. (1995). Trade size and components of the bid-ask spread. Review of Financial Studies, 8, 1153-1183.
Lucca, D. O., & Moench, E. (2015). The pre-FOMC announcement drift. Journal of Finance, 70, 329-371.
Madhavan, A., Richardson, M., & Roomans, M. (1997). Why do security prices change? A transaction-level analysis of NYSE stocks. Review of Financial Studies, 104, 1035-1064.
Pascual, R., Pascual-Fuster, B., & Climent, F. (2006). Cross-listing, price discovery and the informativeness of the trading process. Journal of Financial Markets, 9, 144-161.
Riordan, R., Storkenmaier, A., Wagener, M., & Zhang, S. S. (2013). Public information arrival: Price discovery and liquidity in electronic limit order markets. Journal of Banking and Finance, 37, 1148-1159.
Sims, C. A. (2003). Implications of rational inattention. Journal of Monetary Economics, 50, 665-690.
Smales, L. A. (2018). Slopes, spreads, and depth: Monetary policy announcements and liquidity provision in the energy futures market. International Review of Economics and Finance, forthcoming.
22
Zeileis, A., Kleiber, C., Kramer, W., & Hornik, K. (2003). Testing and dating of structural changes in practice. Computational Statistics and Data Analysis, 44, 109-123.
23
Table1. Summary Statistics
This table provides summary statistics for the 36 stocks in our sample. It reports the company name and market capitalization (as of 31 January 2011). It also reports the average daily price, trading volume ('000), number of trades, and percentage effective spread (in bps), defined as twice the percentage difference between the transaction price and the quote midpoint, in the U.S. and Canada.
January 2004 - January 2011 Market Cap US CANNo. Company ($mil) Price Volume Trade ESpread Price Volume Trade ESpread1 Barrick Gold 34,904 32.81 5,951 27,948 3.95 32.81 2,588 8,922 5.352 Agnico-Eagle Mines Limited 7,122 40.34 2,032 11,123 5.74 40.31 690 3,264 8.493 Agrium Inc. 8,784 41.32 1,732 10,672 5.49 41.31 765 3,872 8.494 BCE Inc. 27,213 27.72 550 2,911 5.02 27.73 2,955 5,304 5.375 Bank of Montreal 31,497 50.75 298 1,994 5.63 50.77 1,537 5,259 4.646 Bank of Nova Scotia 49,846 41.72 249 1,672 7.02 41.40 1,935 6,033 4.737 Brookfield Office 7,793 24.00 1,384 6,730 7.63 23.84 327 1,354 11.608 Cameco Corp. 11,372 40.24 1,794 9,083 5.84 39.82 1,165 4,480 7.669 Celestica Inc. 1,826 9.75 1,063 3,396 12.57 9.75 784 1,506 15.3710 Canadian Imperial Bank Communication 27,844 66.19 211 1,497 5.98 66.20 1,194 4,359 4.8011 Canadian National Railway Company 27,396 52.84 937 5,519 3.58 52.65 937 3,966 5.0412 Canadian Natural Resources Ltd. 34,037 54.28 1,734 10,136 4.57 54.20 1,702 6,626 5.7713 COTT Corp. 889 13.36 421 1,587 16.14 13.32 248 634 22.1714 Canadian Pacific 9,967 48.27 457 2,859 5.29 48.28 593 2,415 7.1015 Encana Corp. 31,810 49.86 2,467 12,293 3.56 49.65 2,226 7,491 4.8216 Enbridge Inc. 19,012 38.80 198 1,245 6.96 38.80 628 2,382 7.0717 Enerplus Corp. 4,834 35.36 535 2,404 7.46 35.39 287 1,267 10.4618 Goldcorp Inc. 24,539 28.29 5,585 25,604 5.04 28.28 2,626 8,819 6.6519 CGI Group 3,738 9.40 116 558 18.81 9.40 983 1,440 16.0420 Gildan Activewear Inc. 3,060 33.13 426 2,596 9.39 33.00 318 1,292 12.8121 Kinross Gold Corp. 10,759 13.40 3,786 16,518 9.87 13.39 3,405 6,792 10.8922 Manulife Financial Corp. 40,305 34.23 1,256 6,448 4.93 34.19 3,063 7,170 5.7523 Nexen Inc. 12,615 35.80 1,413 8,256 6.52 35.65 1,597 5,429 7.5924 Precision Drilling Trust 2,307 27.00 871 3,689 10.04 27.05 672 1,775 12.1825 Pengrowth Energy Corp. 3,156 16.03 888 2,818 10.60 14.75 434 1,192 15.13
26Potash Corporation of Saskatchewan Inc. 28,774 109.00 3,849 23,278 4.14
108.17 698 4,947 5.87
27 Ritchie Brothers Auctioneers 2,262 39.35 198 1,108 10.54 39.10 45 242 20.5628 Rogers Communication Inc. 16,220 37.63 421 2,158 5.25 34.26 1,289 3,633 7.9829 Royal Bank of Canada 66,555 51.39 509 3,388 4.70 50.81 2,383 7,548 4.3830 Shaw Communications Inc. 7,803 22.34 165 859 8.85 22.29 738 1,850 11.0631 Sun Life Financial 20,867 35.17 297 1,882 6.20 35.19 1,154 3,666 6.8132 Suncor Energy Incorporated 42,305 52.84 3,806 19,598 4.05 52.73 2,501 9,485 5.1833 TransAlta Corp. 4,865 21.02 32 188 14.58 21.02 611 1,559 10.7034 Toronto-Dominion Bank 52,833 54.56 602 3,936 4.53 54.56 1,823 6,614 4.3635 Talisman Energy Inc. 17,131 25.80 2,277 10,668 6.47 25.56 2,763 6,006 7.6736 TransCanada Corp. 23,358 30.74 228 1,324 5.96 30.75 1,172 3,376 5.78
24
Mean 37.35 1,354 6,887 7.30 37.12 1,357 4,221 8.79
25
Table 2. Descriptive statistics during announcement and non-announcement days
This table provides summary statistics surrounding the FOMC announcements. Panel A reports statistics during the full 90-minute period surrounding the announcement (1:45pm to 3:15pm). Panel B reports statistics during the 30-minute period prior to the announcement (1:45pm to 2:15pm). Panel C reports statistics during the 60-minute period following the announcement (2:15pm to 3.15pm). The reported figures are the average values over the sample period across firms. " NA" and "A" denote Non-announcement and Announcement days, respectively, while "%Diff" is the percentage change, and the t-statistics are in parentheses. ***, ** and * denote statistical significance at 1%, 5% and 10% levels.
US CAN US less CANNA A %Diff t-stat NA A %Diff t-stat %Diff t-stat
Panel A: Full period (1:45pm - 3:15pm)Effective spread (in cents) 1.88 2.19 15.4%*** (13.30) 2.25 2.54 12.5%*** (11.69) 2.8%*** (3.17)Volume ('000) 259 414 52.4%*** (11.88) 253 382 46.9%*** (11.86) 5.5% (1.51)Trades 897 1508 59.8%*** (14.06) 511 831 57.2%*** (14.42) 2.5% (0.93)Volume per trade 264 251 -4.7%*** (-5.20) 502 476 -6.1%*** (-3.08) 1.4% (0.80)Autocorrelation of order flow 0.251 0.270 10.2%*** (2.82) 0.293 0.310 6.4%*** (4.52) 3.8% (1.00)Ask Depth 1189 1041 -10.0%*** (-6.72) 1901 1710 -10.5%*** (-7.59) 0.5% (0.38)Bid Depth 1056 917 -11.7%*** (-9.26) 1787 1606 -10.5%*** (-8.50) -1.2% (-1.15)
Panel B: 30-min before (1:45pm - 2:15pm)Effective spread (in cents) 1.86 1.97 5.6%*** (5.84) 2.21 2.29 2.9%*** (3.17) 2.7%** (2.50)Volume ('000) 87 77 -0.9% (-0.28) 96 107 2.9% (0.76) -3.9% (-0.93)Trades 297 252 -2.3% (-0.63) 192 200 2.7% (1.15) -4.9%* (-1.91)Volume per trade 310 302 -2.7% (-1.64) 550 547 -5.4% (-0.85) 2.7% (0.46)Autocorrelation of order flow 0.218 0.219 1.0% (0.08) 0.226 0.229 0.4% (0.10) 0.6% (0.05)Ask Depth 1030 965 -9.4%*** (-3.01) 1820 1707 -6.7%*** (-5.01) -2.7% (-1.01)Bid Depth 978 915 -9.5%*** (-3.10) 1788 1682 -6.1%*** (-4.90) -3.3% (-1.22)
Panel C: 60-min after (2:15pm - 3:15pm)Effective spread (in cents) 1.85 2.22 19.6%*** (11.32) 2.22 2.60 17.8%*** (13.26) 1.8% (1.49)Volume ('000) 172 337 84.8%*** (15.76) 157 275 74.8%*** (15.94) 10.1%* (1.86)Trades 600 1256 96.0%*** (17.13) 318 631 88.8%*** (15.87) 7.3%* (1.89)Volume per trade 315 298 -5.9%*** (-3.69) 606 572 -6.7%** (-2.50) 0.8% (0.22)Autocorrelation of order flow 0.246 0.265 10.2%*** (4.07) 0.249 0.258 5.0%** (2.47) 5.3%** (2.12)Ask Depth 1348 1117 -27.4%*** (-11.22) 1982 1713 -15.1%*** (-10.87) -12.3%*** (-7.08)
26
Bid Depth 1134 919 -37.3%*** (-9.23) 1786 1530 -16.2%*** (-11.36) -21.1%*** (-6.38)
27
Table 3. Spread components surrounding FOMC Announcements (in percentages)
This table reports the spread components (in percentages) during FOMC announcement days (A), non-announcement days (NA), and their differences. Panel A reports the components during the full 90-minute period surrounding the announcement (1:45pm to 3:15pm). Panel B reports the components during the 30-minute period prior to the announcement (1:45pm to 2:15pm). Panel C reports the components during the 60-minute period following the announcement (2:15pm to 3:15pm). The information asymmetry component is the coefficient λ in Equation (4). The order persistence component is the coefficient θ in Equation (5). The order processing component is the coefficient – γ in Equation (6). Figures in parentheses are the t-statistics. *** and ** denote statistical significance at 1% and 5% levels, respectively.
US CAN US - CANNA A Diff t-stat NA A Diff t-stat NA A Diff t-stat
Panel A: Full 90min periodInformation Asymmetry Component 22.3% 22.9% 0.67%*** (3.29) 35.6% 36.8% 1.21%*** (4.28) -13.3% -13.9% -0.54%** (-1.97)Order Persistence Component 29.8% 31.6% 1.85%*** (6.66) 28.8% 29.6% 0.80%*** (3.35) 1.0% 2.1% 1.05%*** (2.95)Order Processing Component 48.0% 45.5% -2.54%*** (-10.45) 35.6% 33.6% -2.01%*** (-6.66) 12.4% 11.9% -0.54% (-1.60)
Panel B: 30min pre-announcementInformation Asymmetry Component 22.4% 22.0% -0.35% (-1.62) 35.8% 35.8% 0.01% (0.03) -13.4% -13.7% -0.36% (-1.01)Order Persistence Component 29.6% 29.7% 0.07% (0.22) 28.9% 28.3% -0.66% (-1.55) 0.7% 1.4% 0.73% (1.56)Order Processing Component 48.1% 48.3% 0.28% (0.79) 35.3% 36.0% 0.66% (1.59) 12.8% 12.4% -0.38% (-0.75)
Panel C: 60min post-announcementInformation Asymmetry Component 22.2% 23.4% 1.18%*** (4.30) 35.5% 37.3% 1.80%*** (5.41) -13.3% -14.0% -0.63%** (-2.10)Order Persistence Component 29.9% 32.6% 2.74%*** (7.54) 28.7% 30.2% 1.54%*** (4.72) 1.2% 2.4% 1.20%*** (2.69)Order Processing Component 48.0% 44.0% -3.95%*** (-10.81) 35.8% 32.4% -3.34%*** (-8.93) 12.2% 11.6% -0.61% (-1.37)
28
Table 4. Spread components surrounding FOMC announcements (in US cents)
This table reports the spread components (in US cents) during FOMC announcement days (A), non-announcement days (NA), and their differences. Panel A reports the components during the full 90-minute period surrounding the announcement (1:45pm to 3:15pm). Panel B reports the components during the 30-minute period prior to the announcement (1:45pm to 2:15pm). Panel C reports the components during the 60-minute period following the announcement (2:15pm to 3:15pm). The information asymmetry component is the coefficient λ in Equation (4) multiplied by the effective spread. The order persistence component is the coefficient θ in Equation (5) multiplied by the effective spread. The order processing component is the coefficient – γ in Equation (6) multiplied by the effective spread. Figures in parentheses are the t-statistics. *** and ** denote statistical significance at 1% and 5% levels, respectively.
US CAN US - CANNA A Diff t-stat NA A Diff t-stat NA A Diff t-stat
Panel A: Full 90min period
Effective Spread1.88
2.19 0.307*** (9.95)
2.25
2.54 0.295*** (8.25)
-0.37
-0.36 0.012 (0.59)
Information Asymmetry Component
0.41
0.51 0.095*** (9.05)
0.86
1.01 0.149***
(11.13)
-0.45
-0.50
-0.054***
(-3.42)
Order Persistence Component0.52
0.68 0.154*** (8.25)
0.64
0.77 0.133*** (5.82)
-0.11
-0.09 0.021 (0.87)
Order Processing Component0.94
1.00 0.058*** (4.31)
0.75
0.76 0.013 (0.68) 0.19 0.24 0.045** (2.44)
Panel B: 30min pre-announcement
Effective Spread1.86
1.97
0.104*** (7.12)
2.21
2.29
0.081*** (3.58)
-0.35
-0.32 0.023 (1.11)
Information Asymmetry Component
0.41
0.39 -0.026
(-1.12)
0.88
0.87 -0.013 (-0.75)
-0.47
-0.48 -0.013
(-0.41)
Order Persistence Component0.51
0.56
0.052*** (4.25)
0.60
0.63 0.030 (1.23)
-0.09
-0.07 0.023 (1.07)
Order Processing Component0.94
1.01
0.077*** (3.07)
0.72
0.79
0.064*** (2.57) 0.21 0.23 0.013 (0.38)
Panel C: 60min post-announcement
Effective Spread1.85
2.22 0.377*** (9.63)
2.22
2.60 0.379*** (7.75)
-0.37
-0.38 -0.002
(-0.08)
Information Asymmetry 0.4 0.5 0.130*** (9.14) 0.8 1.0 0.202*** (11.63 - - - (-
29
Component 0 3 6 6 ) 0.45 0.52 0.072*** 3.85)
Order Persistence Component0.51
0.70 0.185*** (8.42)
0.62
0.78 0.159*** (8.00)
-0.10
-0.08 0.026 (1.17)
Order Processing Component0.93
0.99 0.062*** (3.12)
0.75
0.77 0.018 (0.76) 0.18 0.23 0.044** (2.01)
30
Table 5. Breakpoint of the intraday variation of the components
This table reports the estimated breakpoints of the intraday variation of the abnormal spread components, the corresponding 95% confidence intervals and the three F-statistics, sup F, ave F and exp F. The Pre and Post columns report the OLS coefficient estimates for the linear trend term before and after the detected break point, respectively. The reported numbers for the OLS coefficients are multiplied by 10,000 for presentation. *** and ** denote statistical significance at 1% and 5% levels, respectively. The estimation and testing procedure is described in Appendix 1.
Break Point 95% C.I. sup F ave F exp F Pre Post
Sample Period
Panel A: Information Asymmetry ComponentUS 14:08 14:04-14:09 40.55*** 24.55*** 17.44*** 0.27 6.05*** 12:53-14:53Canada 14:18 14:17-14:19 33.62*** 20.68*** 13.83*** -0.32 6.39*** 12:53-14:53
Panel B: Order Persistence ComponentUS 14:11 14:10-14:12 53.52*** 27.37*** 23.42*** -0.37* 12.12 12:29-14:29Canada 13:47 13:45-13:48 63.36*** 37.98*** 27.51*** -0.54* 5.36*** 12:39-14:39
Panel C: Order Processing ComponentUS 14:10 14:09-14:11 45.70*** 32.22*** 19.67*** 0.25 -13.57 12:29-14:29Canada 14:18 14:17-14:20 38.26*** 26.22*** 16.04*** 0.15 -5.32** 12:48-14:48
31
Figure 1. Intraday abnormal effective spread
This figure plots the abnormal effective spread (effective spread during FOMC announcement less non-announcement days) in the U.S. and Canada. The plots are the average spreads across the 36 firms in the sample.
32
Figure 2. Intraday abnormal information asymmetry component of spread
This figure plots the abnormal information asymmetry components of effective bid-ask spread (information asymmetry component during days with FOMC announcement less non-announcement days) for the U.S. and Canadian markets. The figures are the 30-minute moving averages computed from the 36 firms in the sample.
33
Figure 3. Intraday abnormal order persistence component of spread
This figure plots the abnormal order persistence components of effective bid-ask spread (order persistence component during days with FOMC announcement less non-announcement days) for the U.S. and Canadian markets. The figures are the 30-minute moving averages computed from the 36 firms in the sample.
34
Figure 4. Intraday abnormal order processing component of spread
This figure plots the abnormal order processing components of effective bid-ask spread (order processing component during days with FOMC announcement less non-announcement days) for the U.S. and Canadian markets. The figures are the 30-minute moving averages computed from the 36 firms in the sample.
35