Order Flows, Fundamentals andExchange Rates
Kentaro Iwatsubo
Ian W. Marsh
September 2011
Discussion Paper No.1120

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANOrder Flows, Fundamentals and Exchange Rates

Kentaro Iwatsubo and Ian W. Marsh1

September 2011

Abstract: We examine the links between end-user order flows as seen by a major European
commercial bank and macroeconomic fundamentals. We show that both exchange ratechanges and flows are only weakly related to macroeconomic news announcements andhypothesise that “the cat is already out of the bag” by the time the news is announced.
Instead, order flows of financial and corporate customers reflect in real time the evolution ofmacroeconomies. The actions of the banks receiving the order flows in turn reveal theinformation to the market as a whole which prices the exchange rate accordingly. By thetime the news is announced, the exchange rate already contains the majority of theinformation.

JEL classification: F31
Keywords: Order flows; Fundamentals; Exchange rates; News; Microstructure.
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Contact author: Kentaro Iwatsubo, Kobe University (iwatsubo@econ.kobe-u.ac.jp), 2-1 Rokko Nada, Kobe,
657-8501 Japan. Tel. +81-78-6875.
Ian W. Marsh, Cass Business School (i.marsh@city.ac.uk), 106 Bunhill Row, London EC1Y 8TZ U.K. Tel. +44-207040-8600.

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A number of studies use real-time data to analyze the link between economic fundamentals
and exchange rates and they show that the exchange rates respond to the publicly announced
macro news (Andersen et al., 2003; Ehrmann and Fratzscher, 2005). Furthermore,
Dominguez and Panthaki (2006) find that both fundamentals-related and non-fundamentalsrelated news significantly influence intra-day exchange rate returns. However, thesemeasures of news explain a relatively small fraction of the total exchange rate variation.
One reason is that the unexpected component of unscheduled news is hard to detect but could,
potentially, account for the rest of the variation in exchange rates. This does not remove thebasic puzzle of why exchange rates react so little to news about fundamentals economists
think ought to be important in determining fair value.
An explanation may be that the information about pre-announced fundamentals is, to somedegree, already incorporated in the exchange rates. The modern microstructure approach toexchange rates would suggest that this takes place by way of order flows (Evans and Lyons,2005). Dealers observe private signals about so-far unannounced fundamentals through order
flow from their clients, together with public signals through interbank order flows on broker
systems like EBS or Reuters.
This paper explores whether pre-announced economic fundamentals are recognized bytraders and these pieces of private information are priced in the exchange rates. If so, who are
better informed about the not-yet-announced macro variable? When do they react to thedevelopment of economic fundamentals by changing their order flow? Among manypotentially relevant macroeconomic fundamentals, what variables play a significant role in
influencing the exchange rate? Our investigation contributes to the development of a new
micro-theory of exchange rate determination.
This paper uses disaggregated end-user order flow data to assess the forecastability by
investor groups of macro fundamentals and analyzes how the unannounced macro
fundamentals are priced in exchange rates.
We run five sets of regressions. First, we regress exchange rate changes on news
announcements regarding key macroeconomic variables. We show that exchange rates do
not react much to announced changes in fundamentals or even to the surprise components of
the announcements (based on survey expectations). Second, we regress flows on those sameannouncements. Again, we find little reaction in flows. This pair of results suggests that2

neither the exchange rate nor trading in that market are much influenced by announcements,
confirming one element of the disconnect puzzle outlined above and discussed in more detailbelow.
In our third set of regressions we test whether flows respond to developments in themacroeconomies. We find that different customers respond to different macroeconomic
variables. In particular, financial intuitions are better informed than corporations about thedevelopment of macro fundamentals such as GDP, Industrial Production and CPI. On theother hand, corporations are well informed about the unemployment rate. These fundamental
developments have not been announced publicly yet dealers can learn about them through theprivate signal contained in their customers’ order flows. We therefore reverse the regressions
and use order flows from different customer segments to ‘predict’ these unannounced
macroeconomic developments. Finally, we test whether the market exchange rate prices thepredicted fundamentals. Our results suggest that either the actions of our bank in setting
prices and/or trading or a positive correlation between private signals2 allows the market toset exchange rates that take fundamental developments into account even before thosedevelopments have been officially released via data announcements. The disconnect puzzleof the lack of response of exchange rates to macroeconomic announcements is because thecat is already out of the bag by the time announcements are made. The market has learned ofthe economic developments, at least in part through customer order flows, and has priced
them accordingly.
The paper proceeds as follows. In the next section we review and attempt to link two rather
separate literatures; the exchange rate disconnect literature starting with the work of Obstfeld
and Rogoff (2000), and the rather smaller set of papers that consider the information content
of order flow in foreign exchange markets. Section 2 describes our data set and outlines our
hypotheses and methods. Section 3 reports and interprets our results, and the paper ends witha brief summary and conclusion.

2

An informed investor might split a large order into several smaller ones placed with different banks, orseveral informed investors might simultaneously react to the same economic developments and place orders
with their preferred banks. Either way, several banks are receiving order flows containing similar information
at approximately the same time.

31. Literature Review and Hypothesis Development
1.1 Exchange rate disconnect
Meese and Rogoff (1983) took the leading macroeconomic models of the exchange rateavailable at the time and cast doubt on there being a reliable link between spot exchange rates
and the fundamentals used in those models. The state-of-the-art of exchange rate modelling
had hardly progressed by the new millennium when Obstfeld and Rogoff (2000) summarised
the literature as demonstrating a disconnect between exchange rates and macroeconomic
fundamentals.
One answer to the disconnect puzzle is that the link between fundamentals and exchange
rates is too rich and dynamic to be easily captured by regression methods. Engel and West(2005) emphasise that it is expected fundamentals that impact prices rather than realised
fundamentals, while Sarno and Valente (2009) argue that time-varying relations are natural ina world where risk premia and behaviour are changeable.
Andersen et al. (2003) show that the surprise component in news announcements moves
exchange rates in the expected direction but the overall explanatory power of fundamental
news events remains extremely low both because the magnitude of reactions to news is
relatively small and because news announcements are infrequent. The exchange rate
disconnect puzzle at macroeconomic frequencies of a month or more therefore matched byconsiderable unexplained exchange rate variation at higher frequencies. This has beentermed the news puzzle.
Engel and West (2005) rationalise the disconnect in a simple way. By embedding policy
reaction functions in a standard model of uncovered interest parity with risk premia they
derive a present value equation for the exchange rate. The related model in Engel, Mark andWest (2007) suggests that short-run changes in exchange rates are primarily driven bychanges in expectations about the future course of interest rates. In this class of models, asufficiently small discount rate implies that the exchange rate ought to be essentiallyunpredictable based on current fundamentals. Instead, the current exchange rate ought topredict future fundamentals, a finding weakly borne out by empirical work.

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1.2 Information in order flow
While the macroeconomic literature at first worried about the disconnect puzzle and thenembraced it as consistent with more sophisticated models, the microstructure approachstumbled upon a variable that is highly correlated with high frequency exchange rate
movements – order flow. For example, Evans and Lyons (2002) show that interbank orderflows can explain between 40 and 70% of the daily variation in spot returns for major
currencies. Interbank order flows result primarily from risk management activities of trading
desks in response to order flows from their customers, and subsequent work has shown
correlations between order flow from end-users and exchange rates consistent with theinterbank results (Evans and Lyons, 2007; Marsh and O’Rourke, 2005).
Evans and Lyons (2007) attempt to bridge the gap between macro and microstructure byembedding microstructural currency trading in an open-economy DSGE macromodel. Thismodel suggests that order flows contain information regarding macroeconomic developments
but that the information is insufficient for dealers to infer the true state of the economy.
Evans (2010) builds a model that combines elements of Evans and Lyons (2004) and Engeland West (2006) such that dealers extract information from order flow to infer the current
state of the economy while recognising that policy-makers set short-term interest rates in
response to changes in the macroeconomic environment.
Evans (2010) goes on to provide empirical support for his theoretical model by showing that
customer order flow from Citibank between 1993 and mid-1999 provide incremental
information regarding the real-time state of the macroeconomy over and above that containedin current and past data releases. Further, the information in these flows is price relevant inthat exchange rates significantly react to that portion of flows that add value in understanding
the current state of the economy.
Our paper seeks to build on the work of Evans by using order flow from a different bank overa more recent time period to confirm that order flow contains price relevant macroeconomic
information. We consider in detail the time periods over which order flow might reflectfundamental information, testing whether order flow reacts to macroeconomic developments
before they happen, as they happen or after they happen, a sub-set of the latter case being thatorder flows react to the news announcement. We show that for most key macroeconomic
variables, order flow is contemporaneously correlated with the evolution of themacroeconomy, such that flows provide real-time information on what is happening in the5economy. Further, we show that it is largely the order flow of financial institutions that gives
precise information about current fundamentals. We also identify fundamentals that are notreflected in order flows and find that, reassuringly, these are the variables whose
announcement effects are largest, suggesting that the foreign exchange market only learns
this information when it is publicly announced.

1.3 Hypotheses tested
The two literatures briefly surveyed above together suggest that exchange rates might react tomacroeconomic developments at various points in time. In this sub-section we outline ourregression approaches to testing these reactions.
Consider a generic macroeconomic variable, x, that is measured over a discrete interval (e.g.
the CPI-based inflation rate). One observation on this variable might correspond to the j-day
interval t0 through tj. Developments in this variable are not observable in real-time althoughagents in the economy can observe signals based on their own limited information sets. For
example, while the evolution of the aggregate consumer price level is not observable in realtime, a firm has information about the evolution of the basket of goods that it trades. A
subset of this firm’s basket of goods is also a subset of the basket of goods measured tocompute the consumer price index, and hence the firm observes in real-time a signal of theevolution of the consumer price index. Similarly, a financial firm is analysing public andprivate signals of the evolution of x based on its own imperfect information set. We assume
that corporate and financial firms trade foreign exchange based on their interpretations ofthese public and private signals. The implication is that customer order flow ought to becontemporaneously correlated with macroeconomic developments. Ideally, we would
observe order flow at the individual firm level. Unfortunately individual customer order flow
data is not available for long enough time periods to be analysed in this framework. Instead
we are forced to aggregate order flow into customer categories (corporate customers andfinancial customers in our data set) and we test the link between order flow and fundamentals
by regressing order flow from customer segment m observed between t0 and tj on
developments in i key macroeconomic variables x also measured between t0 and tj:(1)

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Significant coefficients in this regression would suggest that aggregate order flow from acustomer segment is reacting in real-time to the relevant macroeconomic variables.
This order flow is seen by their foreign exchange dealer. Importantly, a dealer for a largebank observes order flow from many different customers. In effect the dealer is receiving
many (private) signals about the private signals received by his many customers. The
dispersed information held by the agents in the economy is then concentrated through order
flow through one dealer. Of course, this aggregation is imperfect, but a central hypothesis of
this paper is that the real-time order flow seen by a dealer contains information about thecontemporaneous evolution of macro variables.
This is tested in the following section by reversing equation (1) outlined above and regressing
variable x between t0 and tj on order flow from customer segment m observed in the interval
t0 to tj:
(2)
Significant coefficients in the regression would indicate that a dealer can learn about variable
x from the contemporaneous flows he observes. Note that this order flow is privately
observed and constitutes an informational advantage for the dealer. Cheung, Chinn andMarsh (2004) report results from a survey of dealers that confirm access to a large customer
base as the single most important competitive advantage that a dealing desk might have.
Equation (2) tests whether the access to customer order flow gives a dealer an advantage
because he can use it to infer developments in the real economy before they are publicly
announced.
< Figure 1 about here >The actual evolution of x over the period t0 to tj is publicly announced on day tA. There is areporting delay and so the announcement day is later than the end of the measurement
interval. There is then the period tj+1 to tA-1 which we will call the pre-announcement interval,
during which there is a realised value for variable x that is not public knowledge. Agents inthe economy may still be learning about the value of x based on information flows during thispre-announcement interval, and so order flow in the pre-announcement period may stillcontain relevant information. This is tested by augmenting the above regression with order
flow from customer segment m observed in the interval tj+1 to tA-1.
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(3)Since all signals before the announcement are noisy, the announcement is likely to contain asurprise element that causes the exchange rate to adjust. The noisier the signals (both public
and private), the less ability the dealer has to infer the evolution of fundamentals and so thelarger the likely surprise element. One part of the literature on the effect of news on theexchange rate relies on econometric forecasts to generate expectations. These papers usually
implicitly assume that the expectations are based on public information since only public
information is included in the forecasting regressions. Papers that use expectations data fromsurveys loosen this constraint.
The announcement resolves all uncertainty about the value of x (except to the extent thatrevisions to an initial provisional estimate are likely) but this does not necessarily resolve theimplications of the value of x for the exchange rate. We would expect to see (i) an
adjustment of the exchange rate to the surprise element of the news announcement, (ii) order
flow following the announcement as agents react to the surprise element and (iii) subsequentexchange rate adjustments and order flow as the market prices the implication of the news
announcement for the exchange rate. Our data are sampled at the daily frequency and so wecannot separate these various reactions. However we would expect to see contemporaneous
correlations manifest by significance in regressions of the log change in the exchange rate ororder flow on news announcements.
As detailed in the survey of the disconnect literature, however, the key finding in regressionsof exchange rate changes on news is that the explanatory power is very low. To anticipate
our results, we also find that news announcements explain only a tiny percentage of thevariation in the spot euro-dollar exchange rate over our sample. Two resolutions to thispuzzle suggest themselves. Either the macroeconomic variables we analyse are simply not
relevant to exchange rate determination (an extreme answer to the disconnect puzzle) or theactual news announcement is largely irrelevant for exchange rate determination because thevariable is already incorporated in the exchange rate. Within the microstructure approach,order flow is the mechanism by which macroeconomic developments might be learned by themarket before they are publicly announced. We test the second potential resolution byregressing exchange rate changes (Δs) on the fitted values of fundamentals from regression(3) above.

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(4)These fitted values are predictions of the variables that the dealer could generate based solely
on his private order flow. In reality, the information set available in making predictions ismuch larger and so these predictions are very basic. However, these regressions tell us
whether that part of order flow that is correlated with fundamental x and observed before thevalue of x is announced publicly has already impacted the exchange rate. Statisticalsignificance, and more importantly a reasonable explanatory power would suggest that themarket has priced in fundamentals before they are announced through the impact of order
flow on the exchange rate.

2. Data

2.1 Order flow and exchange rates data
The order flow data used in this paper come from the Royal Bank of Scotland (RBS),
beginning on 1 October 2001 and ending 29 June 2004 giving a total of 658 observations
accounting for national holidays. During this period, RBS was among the top ten global
foreign exchange banks. Customer order flow data are obviously highly confidential and so
the data description provided here is necessarily less detailed than usual. However, we hopethat readers still get a feel for the nature of the flows across this bank’s foreign exchange
desks.
The RBS maintains a 24-hour foreign exchange trading service for its customers. The
customer order flow data are aggregated over a 24-hour window from the opening of the
Sydney market through the close of the US market (which approximates to midnight tomidnight Greenwich Mean Time). The data include all spot transactions entered into bycustomers against the bank. The data do not include forward deals or deals between the bankand other banks via the inter-dealer markets. In this paper we focus on customer order flow
data in the most liquid market, the euro-U.S. dollar.
The order flow is further disaggregated according to the counterparty classification assignedby the bank. There are four categories of customer: non-financial corporates (denoted Corp),
unleveraged financials such as mutual funds, leveraged financials including hedge funds and9other financials. The final category is rather heterogeneous but will include the trades ofsmaller banks that do not have access to the interbank dealer network and trades of central
banks. Since the motives and relevance of this final category is not well understood we do
not consider this set of flows individually below, although it is still included in the total
measure of flows. We also aggregate the leveraged and unleveraged financial institutions
into one category which we call ‘Financials’ (denoted Fin). The leveraged category is
relatively small, particularly at the start of our sample, and its use as a standalone segment isquestionable. Our results are robust to aggregating the two profit-maximising categories offinancial customers or just using the unleveraged category alone.
Where possible we use the USD/EUR exchange rate data from Global Treasury Information
Services, quoted at 18:00 NY (22:00 GMT). For missing data due to the US national
holidays, we used instead the London fixing rate at 16:00 GMT, obtained from WM/Reuters.
Both sets of rates are sourced from Datastream.
We also use other financial variables in our analysis. We collect LIBOR overnight interest
rates at the 17:00 fixing in London from the British bankers Association and we use S&P500
and Euro STOXX stock price indices. All series are sourced from Datastream.
Table 1 contains some descriptive statistics of the key series analysed. Key points from thisare that that flows from different counterparty classifications typically are not highly crosscorrelated, and neither are they highly auto-correlated. FX returns, conversely, are strongly
negatively autocorrelated as are changes in interest rate differentials. All series are
comfortably stationary.
< Table 1 here >< Descriptive Statistics >2.2 Macroeconomic data and expectations data
We collect the data on macroeconomic news and market participants’ expectations from
Bloomberg. The data set includes expected, announced and revised macroeconomic
variables. Bloomberg’s expectations are calculated as the mean from a survey of forecasts
made by market participants. One strong point of Bloomberg is its popularity and perceived
relevance among FX traders – there are no dealing rooms without Bloomberg feeds. There10

are often doubts about the value of survey-based measures of expectations, usually due to thelack of incentives for participants to give accurate responses, and so it is important to use
survey data from a source with a high perceived value among respondents. The start date ofBloomberg’s survey is relatively recent compared to other survey corporations, and thesurvey is therefore less used in academic work, however it covers our sample period and its
closeness to dealers is a significant strength especially in assessing the reaction of exchange
rates and flows to announcements. Most previous studies examining reactions of highfrequency bond prices and exchange rates to macroeconomic announcements utilize theInternational Money Market Studies (MMS) data, which are collected by InformaGM, on
expected and announced macroeconomic news. MMS is a telephone survey conducted
weekly and published on the Thursday of the week prior to the announcement date. Hence
MMS’s survey does not reflect the revision of market participants’ expectations just before
the announcements and is relatively stale. By contrast, the Bloomberg survey covers between
30 and 40 respondents who can revise their forecasts any time until each announcement.
Expectations based upon this survey are likely, then, to be as up-to-date as possible.
We analyse 17 scheduled macroeconomic announcements, seven for Europe and ten for theUnited States, listed in the Appendix. However, we only report results based on eight main
announcements in the text for compactness. The full sets of results are available on request.
The eight macro variables we analyse in detail are CPI, GDP, industrial production andunemployment announcements for the euro-area and U.S.
We define news about a macroeconomic variable in two ways: the actual (or percentage)change in the variable which includes both the anticipated and surprise component of theannouncement, and the surprise component. We measure the surprise component as thedifference between the actual and expected value of the variable, standardized by the standard
deviation of the news, as is normal in this literature.

3 Results and Interpretation
In this section we run sets of regressions with three aims in mind. First, we want todemonstrate that the exchange rate and flow disconnect puzzles hold for our data set. Second,
we want to better understand the determinants of order flow. In particular, financial
institutions might reasonably be expected to have followed some well-known FX trading
11

strategies which ought then be able to explain their flows. Similarly, as outlined above, allcustomer types ought to be reacting to or even anticipating key macroeconomic developments
and with the hindsight of econometricians we test whether real-time developments in macro
variables can explain flows. Third, and central to the hypotheses outlined above, we test
whether the dealers who privately observe their customers’ order flows and the dealer
community that aggregates these flows can learn enough from them to price macroeconomic
developments before they are publicly announced.

3.1 Preliminaries
We begin by establishing the relevance of our order flow data. End-user order flow is hard tocome by and analyses of customer order flows are relatively rare. The bulk of the literature
relies on data from one (exceptionally good) source, namely Citibank (see Evans and Lyons,2005). Other papers have used flows from smaller European banks (Osler et al., 2010
Bjonnes et al., 2005). Our data are from RBS, at the time a leading player in FX markets
with a global footprint. These data have been used in analyses of foreign exchangeintervention (Marsh, 2011) and to consider the information content of order flow (Marsh andO’Rourke, 2005).
< Table 2 here >< Order flow and contemporaneous exchange rate changes >In Table 2 we establish that one of the key relationships in FX microstructure holds for thisdata. We note, in line with the literature, that there are significant contemporaneous
correlations between order flow and exchange rate changes but that these correlations beardifferent signs. Flows from profit-maximising financial institutions are positively correlatedwith exchange rate changes, such that net buying pressure on the euro from financials
coincides with an appreciation of the euro, while the correlation between corporate customer
order flows and the exchange rate is negative. These correlations are very similarirrespective of whether the flows are included in the regression one at a time or all together.
They are also unaffected by the inclusion of other explanatory variables such as interest rates
or other asset returns. Proponents of the microstructure approach contend that these12correlations result from the information content of flows, although this contention is largely
driven by theoretical modelling and few papers have analysed the driving forces behindcustomer order flows. We turn to this issue in subsection 3.3.

3.2 Does news matter for exchange rates?
In this section we establish that one of the key puzzles in international finance also hold forour data set. The exchange rate disconnect puzzle concerns the lack of relations between
exchange rate changes and fundamentals (however defined) over short to medium horizons.
A corollary of that puzzle is the news puzzle – exchange rates often do not react much to data
releases concerning major macroeconomic fundamentals, even if there are relatively large
surprise components to the announcement.
< Table 3 here >< Exchange rate changes and news >Table 3 reports results from regressing exchange rate changes on news announcements. We
use both the actual announcement, usually in terms of percentage change, and the surprisecomponent of the announcement as discussed above. The key findings are that (i) some news
announcements are statistically significant but (ii) news announcements do not explain muchof the variation in exchange rates. Negative autocorrelation in exchange rate returns is quite
significant but developments in interest rate differentials have no explanatory power.
Columns III and VI indicate that order flows are significantly contemporaneously correlatedwith exchange rate changes after the addition of extra explanatory variables.

3.3 Determinants of order flow
Order flows are strongly correlated with contemporaneous exchange rate changes and, given
their position on the right hand side of most regressions in the microstructure literature, theusual view is that flows are causal for exchange rates. In passing, we note that there is noevidence of Granger causality in either direction between flows and exchange rates for ourdataset, although interestingly there is evidence that corporate customer order flows react to13

lagged financial customer order flows consistent with the liquidity provision hypothesis of
Bjonnes et al. (2005) (see Table 4).
< Table 4 here >< Granger Causality >The FX microstructure literature has long argued that order flows are only the proximate
cause of exchange rate changes. Order flow is then the mechanism by which the real driving
forces impact the exchange rate. This subsection examines the causes of customer order flow
as seen by RBS. Since the focus of the paper is the relevance of macro fundamentals thesewill play a dominant role, but we want to expand beyond this to control for other potential
determinants. As we have order flow from two customer segments that are very likely to be
driven by different factors we add potential explanatory variables suggested by recent
developments in foreign exchange markets.
The literature on foreign exchange determination has recently taken on a new life once it was
realised that certain trading strategies appear to provide considerable profits, possibly also
once these profits are risk adjusted. The carry trade has been dominant among thesestrategies with momentum trading following in rather distant second place. The portfolio
approach to carry trading suggests investing in a portfolio of n currencies offering the highest
yield, and funding this portfolio by borrowing the m currencies with the lowest yield from a
restricted universe of all tradable currencies. On a portfolio basis, neither the dollar nor the
euro would have been much used as part of the carry trade during our sample period as theirinterest rates were usually fairly average and so not in either extreme portfolio. However
many investment houses instead traded several bilateral currency pairs, going long the higher
yielding currency, to maximise the diversification impacts of carry trading and it isconceivable that some euro-dollar flows might have been affected by this version of thecarry trade. To this end we include both the interest rate differential and the change in the
differential as potential determinants of (financial customer) order flows. Similarly, we
include lagged exchange rate changes as a proxy for the drivers of momentum trading. Most
momentum trading in foreign exchange markets seems to be of the trend-following variety,so we would expect a positive correlation between lagged currency appreciations and inflows
into that currency.

14

Although our unconditional autocorrelation tests in Table 1 did not suggest a role for own
lagged order flow, we include lags now as Bjonnes et al. (2005) suggest that corporate
customers provide liquidity to financial customers, implying that there may be a lead-lag
relationship between flows from different customer groups. As noted, this is supported by
the Granger causality results reported in Table 4.
Since financial customer order flow includes unleveraged institutions such as mutual funds, it
is likely that FX flows from some customers is driven not by exchange rate factors but by
expected or realised developments in underlying assets. Interest rates are already included in
the regression so we also add changes in the S&P500 and EuroStoxx stock indices to capture
equity market developments.
Initially, we consider the role of fundamentals by including macro news announcements as
explanatory variables. We might not expect corporate flows to react at high frequency tonews announcements, and indeed a central hypothesis of this paper is that they will havealready reacted to the economic developments in real time and so in advance of the news
announcement. However, a corporate customer might respond to macro news to the extent
that it provides information on the development of the wider economy that is relevant to thefirm (for example, by allowing more accurate predictions of the future state of the economy
than could be made through an analysis of information directly observed by the firm itself).
It is less contentious that financial flows might react to new information releases, again
because the investment positioning of the firm is based on noisy signals and a data release
may necessitate a repositioning.

< Table 5 here >< Order flow determinants – news >Table 5 contains the results of sets of regressions aimed at explaining order flows. It is fair tosay that these results are disappointing. While some variables are occasionally significant,the overall fit of the regressions is very poor. In particular, the announcement variables arelargely insignificant and provide very poor fit. We find very similar results if the announced
change in the macroeconomic variables is used instead of simply the surprise component.
15

Similarly, our ability to explain financial customer order flows is unchanged if we expand thenumber of announcements included in the regression. This expansion of the number ofannouncement examined does increase the adjusted R2 figure for corporate flows to 3.2% but
most of the significant coefficients on news announcements bear the “wrong” sign,
suggesting that corporate customers buy the euro on bad news for the euro economy or goodnews for the US economy. This rather odd behaviour is also found in King et al. (2010)
when analysing the impact of flows in the Canadian Dollar. We noted above that exchange
rates do not react much to news announcements and the microstructure parallel of this is thatorder flows do not seem to react much either (or at least does so in an inconsistent way).
There is some evidence that stock market returns and changes in interest rate differentials
matter for financial customer order flows. The significant coefficient on lagged financial
flows in the corporate flows regression also supports the notion that corporate supply
liquidity for financials – in the words of Sager and Taylor (2006), financials are pushcustomers and corporate are pull customers. Nevertheless, our ability to explain order flows
of corporate or financial customers using this set of explanatory variables is very limited.
As outlined above, one of the key hypotheses in this paper is that customer order flow is
responding in real time to macroeconomic developments. Corporate clients are not, in themain, trading foreign exchange for profit but instead as a by-product of their corporate
activities. The evolution of the real economy is then potentially a major force behind flows
from corporate customers. As the markets in which they operate develop we might expect
they will trade in foreign exchange markets. Similarly, financial customers are continually
updating their assessments of the economy and taking positions accordingly. Hence wewould also expect their flows to be reacting to real-time economic developments. This
simple concept lies behind the dispersed information approach of micro FX. As individualcompanies trade based on the evolution of their own microeconomies, order flow through theforeign exchange market aggregates the microeconomic information into something
approximating a macroeconomic signal for the dealers observing the flow.
Given the poor performance of our other explanations of order flow, we now concentrate onwhether flows react contemporaneously to macroeconomic developments. We run
regressions at a monthly frequency for most macroeconomic variables, but use a quarterly
frequency for GDP series. We regress order flow on contemporaneous changes in macro
fundamentals as in equation (1) above.
16

< Table 6 here >< Order flow and current fundamentals >These results are marginally more encouraging. Although the goodness of fit measures arestill quite low there is frequent statistical significance and most of the coefficient values takethe expected signs. For financial customers, increases in European inflation, industrial
production and GDP growth, increases in the U.S unemployment rate, and decreases in U.S.
industrial production all result in contemporaneous flows into the euro. Flows of corporate
customers appear to be driven by unemployment rates – increases in U.S. unemployment anddecreases in European unemployment resulting in contemporaneous inflows for the euro.

3.4 Learning from order flow
Table 6 suggests that order flows of our bank’s customers are responding in real time tomacroeconomic developments. These order flows are private signals of the evolution of themacroeconomy seen by bank dealers. In this section we ask first whether the dealers can
extract this information from this set of private signals, and second whether the extracted
information is priced into the exchange rate.
We begin by reversing the above analysis and regress economic developments on order flows
as in equation (2) above. Significant coefficients in this regression would suggest that a
dealer observing these (private) customer order flows could make inference about
contemporaneous developments in the economy. We then also include order flows from theperiod tj+1 until the day before the announcement date in the regression to test whether
information is contained in non-synchronous order flows (equation (3)). Results arepresented in Table 7.
< Table 7 here >< Order flow and fundamentals >17

These findings are encouraging. Order flow from financial customers is significantly
correlated with contemporaneous macroeconomic developments six of the eight variables
analysed. Coefficient signs are generally as expected, with the exception of euro area CPI.
Interestingly, and mirroring the results of Table 6, while financial customer order flows
appear to contain little information that could be extracted regarding unemployment rates ineither the U.S. or Euro area, corporate flows are significantly correlated with changes
unemployment.
Goodness of fit measures are very high for the quarterly GDP series but we have relatively
few observations in our sample. Regressions for the monthly series display less explanatory
power but considering the very small information set these forecasts are being made upon theperformance is not unreasonable.
In unreported results we also included order flow from the month before t0 to test whether
there is information in lagged order flow about future fundamentals. We found no
significance. The results therefore indicate that in the main it is only contemporaneous flows
observed by our bank that contain information valuable for understanding economic
developments. Neither leads nor lags of order flows are consistently significant. Evans andLyons (2007) argue that order flows can predict fundamentals several months ahead. Our
results suggest that order flows can also be used to infer the contemporaneous evolution of
the economy.
Finally, we turn to perhaps the key results in the paper. Our point of departure was that news
announcements have little impact on exchange rates (or, it emerges, on order flows). We
hypothesised that this might be because exchange rates had already responded to economic
developments in real-time since flows contain strong enough signals to dealers that they caninfer the evolution of the key macro variables. Table 7 suggests that there is information
content in the flows and now we test whether exchange rates react to these signals.
< Tale 8 here >< Pricing of fundamentals >Following Evans (2010) we adopt a two stage procedure to test whether inferredfundamentals are priced using regression (4) above. The two stage strategy is necessary
18

because the explanatory variables in equation (4) are themselves estimated and so there ispotentially an errors-in-variables problem. Our estimation strategy is as follows. In the firststage regression, the change in macro fundamental variable is regressed on contemporaneousorder flows by financial institutions and corporations to obtain the estimated value of themacro fundamental (equivalent to the regressions reported in the first row for each variable in
Table 7). The second regression uses the estimate as an instrumental variable to estimate theimpact of the fundamental variable on daily exchange rate change.
Four of our eight key macroeconomic variables appear to be contemporaneously priced into
exchange rates. For three of these variables – Euro CPI and GDP and US industrial
production – all of the pricing is derived from financial customer order flows. The fourthvariable – US GDP – prices information in both corporate and financial order flows.
Interestingly, while corporates appear to produce flows that are correlated with
unemployment rate developments, particularly in the euro area, and in contrast with financial
customers whose flows are uncorrelated this does not appear to be priced into exchange rates.
In Table 3 above we note that the response of the exchange rate to unemployment news in theeuro area is highly significant. It appears that dealers have some relevant information about
unemployment developments but are not using it efficiently. Conversations with dealerssuggest that they anticipate any information in flows to come primarily from their financial
customers, and the importance of corporate order flows is downplayed. Our results indicatethat this view is largely correct since it is financial customer flows that contain the mostinformation. However, by ignoring flows from corporate customers, dealers are perhaps
missing out on some relevant information.
The variables not contemporaneously priced into exchange rates are exactly the ones whichhave large announcement effects. Column III of Table 3 indicates that the surprisecomponents of Euro industrial production and unemployment rate affect exchange rates mostsignificantly on the day of announcement, while column VI suggests that changes in US CPI
have significant announcement effects. None of these three are contemporaneously priced
according to Table 8.

19

4 Summary and Conclusions
This paper is motivated by two robust empirical observations. First, there is a disconnect
between fundamentals and exchange rates insofar as very little of the variation in exchange
rates can be explained by announcements about key macroeconomic developments. Second,
there are strong links between order flows from end-users in the foreign exchange market andspot exchange rates. The microstructure approach to exchange rates suggests that thecustomer order flows contain information about current and expected future developments inthe macroeconomies, and that this information is extracted and priced into exchange rates
through the trading mechanism.
Our paper tests this hypothesis, demonstrating that there is a contemporaneous correlation
between the real-time evolution of major macroeconomic variables and customer order flows
as seen by a major European commercial bank. Further, this information can be extracted by
the dealer to derive real-time information on macro developments. Finally, this information
is rapidly priced into spot exchange rates. The lack of response of exchange rates to thesubsequent announcement of these macroeconomic developments is because the cat isalready out of the bag. The market has learned of many of these economic developments, atleast partially because of customer order flows, and has priced them accordingly.
We conclude that the dispersed information microstructure theories of Evans and Lyons(2007) and Evans (2010) find empirical support. Customer order flow is a mechanism bywhich the foreign exchange market learns about the state of the economy. Since this learning
takes place in real-time, exchange rate changes anticipate the information content of delayed
macroeconomic news announcements.

Acknowledgement
This paper was completed while Iwatsubo was visiting Cass Business School. We are
grateful to seminar participants at Osaka University for helpful comments, and the foreignexchange team at Royal Bank of Scotland for providing the order flow data and for
informative discussions. Iwatsubo is thankful for financial support from JSPS Excellent
Young Researcher Overseas Visit Program and Grant-in-Aid for Young Scientists(B21730253). All errors are our own.

20Appendix
17 Scheduled Macro Announcements (EMU 7, US 10)
Title

Frequency

Source

Units

EMU

CPI

Monthly

ECB% change mom

EMUConsumer ConfidenceMonthlyEU
Commission

Index

EMU

GDP (seasonal adjusted)

Quarterly

ECB% change qoq

EMU

Industrial Production

Monthly

ECB% change mom

EMU

M3 (seasonal adjusted)

Monthly

ECB% change mom

EMU

PPI

Monthly

ECB% change mom

EMU

Unemployment Rate

Monthly

ECB% rateUS

Change in Manufact. Payrolls

Monthly

BLS

Change in thousands

US

Change in Nonfarm Payrolls

Monthly

BLS

Change in thousands

US

CPI

Monthly

BLS% change mom

US

GDP

Quarterly

BEA% change qoq

US

Housing Starts

Monthly

CensusMillions

US

Industrial Production

Monthly

FRB% change mom

USRetail Sales Less Autos

Monthly

Census% change mom

US

Trade Balance

Monthly

BEA$ billion

US

U. of Michigan Confidence

Twice aMonth

Univ. of
Michigan

Index

US

Unemployment Rate

Monthly

BLS% rate21

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announcements: real-time price discovery in foreign exchange. American Economic
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22

Marsh, I.W., O’Rouke, C., 2005. Customer order flows and exchange rate movements: isthere really informational content? Cass Business School Working Paper.
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Sarno, L., Valente, G., 2009. Exchange rates and fundamentals: footloose or evolving
relationship? Journal of the European Economic Association, 7(4), 786-830.

23

Table 1. Basic Statistics
This table presents basic statistics for the daily USD/EUR return, financial customer order flow (OF_F),
corporate customer order flow (OF_C), total customer order flow (OF_Total) and the overnight interest
differential expressed as the difference between Euro and US annualized interest rates. Panel A shows thedescriptive statistics and Panel B presents the correlations for the variables. Mean, maximum, minimum, andstandard deviations of exchange returns are presented in percentage points. Order flow shows net buying of
EUR in USD by each trader group. JB-Prob is the P-value for the null hypothesis of the Jarque-Bera test fornormality. AR(1)-Prob is the P-value for the null hypothesis that the first-order autocorrelation is zero. ADF is
the Augmented Dickey-Fuller test statistic with a null of non-stationarity. The sample period is 10/1/20016/29/2004, or 658 daily observations.

Panel A. Descriptive statistics
FX return
OF_F
OF_C
OF_TOTAL i_eu-i_us ⊿(i_eu-i_us)
Mean
0.04 -6.88E+05 -2.76E+06 -2.24E+06
1.30
0.00
Maximum
1.92
3.01E+08 1.90E+08 4.23E+08
2.43
0.84
Minimum-2.02 -2.71E+08 -2.14E+08 -3.33E+08
0.29
-0.97
Std. Dev.
0.64
4.37E+07 4.42E+07 7.64E+07
0.31
0.13
Skewness
-0.15
-0.06
0.04
-0.02
0.33
0.32
Kurtosis
3.44
12.82
6.26
6.47
3.16
17.16
JB-Prob
0.01
0.00
0.00
0.00
0.00
0.00
AR(1)
-0.11
-0.01
-0.01
0.02
0.90
-0.14
AR(1)-Prob
0.00
0.72
0.81
0.59
0.00
0.00
ADF
-29.69-25.45-25.42-24.34-6.65-20.05
Panel B. Correlations
FX return
1.00
OF_F
0.19
OF_C
-0.10
OF_TOTAL
0.01
i_eu-i_us
0.02⊿(i_eu-i_us)
-0.01

1.00
0.04
0.50
-0.01
-0.06

1.00
0.48
-0.02
-0.03

24

1.00
-0.03
0.00

1.00
0.20

1.00

Table 2. Order Flow and Exchange Rates
This table reports the results of regressing daily changes in the USD/EUR exchange rate on order flows andother potential explanatory variables. The coefficient on order flows shows the percentage change in exchange
rates for a net purchases of EUR by one USD by financial, corporate and total customers. Although notpresented, standard errors are estimated using an autocorrelation and heteroskedasticity consistent matrix ofresiduals. ***, **, and * denote significance at the 1%, 5%, and 10%, respectively. The sample period is
10/1/2001-6/29/2004.
Ⅰ
OF_F
OF_C
OF_TOTAL
i_eu-i_us⊿(i_eu-i_us)
order flow_f (-1)
order flow_c (-1)
EURUSD return (-1)
R2
Adj. R2Ⅱ

Ⅲ

2.74E-09 ***Ⅳ
2.81E-09 ***-1.59E-09 **-1.49E-09 **ⅤⅥ

2.81E-09 *** 2.72E-09 ***-1.59E-09 ** -1.66E-09 **1.12E-10

0.035
0.034

0.011
0.009

0.000
-0.001

25

0.047
0.045

0.052
-0.0170.065-0.0799.01E-10 *1.64E-11-0.115 ***

0.048
0.042

0.063
0.052

Table 3. Exchange Rates and News
This table reports the results of regressing the daily change in exchange rates on macroeconomic news
announcements and other explanatory variables. In columns I-III the surprise element of the news
announcement is included in the regression, while in columns IV-VI the announced change in the variable isincluded. Although not presented, standard errors are estimated using an autocorrelation and heteroskedasticity
consistent matrix of residuals. ***, **, and * denote significance at the 1%, 5%, and 10%, respectively. The
sample period is 10/1/2001-6/29/2004.

EMU
EMU
EMU
EMU
US
US
US
US

Ⅰ

Ⅱ
Ⅲ
Announced news surprises

Ⅳ

CPI
GDP
Industrial Production
Unemployment Rate
CPI
GDP
Industrial Production
Unemployment Rate
OF_F
OF_C
i_eu-i_us⊿(i_eu-i_us)
order flow_f (-1)
order flow_c (-1)
EURUSD return (-1)-0.205-0.0800.243-0.113-0.215-0.073
0.060
0.166

**

0.284
0.012
0.124 **
-1.274-1.383 ***
0.003
0.139
0.472

R2
Adj. R2

0.022
0.011

**
*

*

-0.198-0.0730.237-0.127-0.217-0.071
0.068
0.159

**

0.059
-0.134-2.99E-10
4.99E-10-0.116 ***-0.164 *-0.1150.214 **
-0.135 ***-0.224 *-0.1040.002
0.150
2.64E-09 ***-1.63E-09 **0.069-0.095-7.73E-11
7.33E-10-0.113 ***

0.036
0.018

0.084
0.062

**
*

*

26

Ⅴ
Ⅵ
Changes in news announcements

0.022
0.011

0.278
0.030
0.126 **
-1.398-1.371 ***
0.004
0.167
0.474

0.261
-0.0650.105 *
-1.391-1.306 ***
0.005
0.139
0.340
2.70E-09 ***-1.50E-09 **
0.049
0.047
-0.141-0.107-2.28E-10-1.23E-10
5.94E-10
7.37E-10-0.120 ***-0.113 ***
0.037
0.019

0.075
0.055

Table 4. Granger Causality
The table displays the results of Granger Causality tests among exchange rate return (USD/EUR), financial
customer order flow (OF_F) and corporate customer order flows (OF_C). The number of lags is determined tobe three by Schwartz Information criteria. The sample period is 10/1/2001-6/29/2004.

Null Hypothesis:

F-stat.

P-value

OF_F does not Granger Cause USD/EUR
USD/EUR does not Granger Cause OF_F

1.383
0.054

0.247
0.984

OF_C does not Granger Cause USD/EUR
USD/EUR does not Granger Cause OF_C

0.349
0.180

0.790
0.910

OF_C does not Granger Cause OF_F
OF_F does not Granger Cause OF_C

0.971
3.751

0.406
0.011

27

Table 5. Order Flow Determinants: Announced News Surprises
This table presents the explanatory power of macroeconomic news and other macro variables for the daily orderflows for the sample period 10/1/2001-6/29/2004. Order flows by financial institutions (OF_F), that bycorporations (OF_C), and total order flow (OF_total) are considered. Although not presented, standard errors are
estimated using an autocorrelation and heteroskedasticity consistent matrix of residuals. ***, **, and * denotesignificance at the 1%, 5%, and 10%, respectively.
OF_F
EMU
EMU
EMU
EMU
US
US
US
US

CPI
GDP
Industrial Production
Unemployment Rate
CPI
GDP
Industrial Production
Unemployment Rate
EU Stoxx returnS&P 500 return
i_eu-i_us⊿(i_eu-i_us)
order flow_f (-1)
order flow_c (-1)
order flow_total (-1)
EURUSD return (-1)
R2
Adj. R2

OF_C

OF_total

Announced news surprises-1.40E+07-6.28E+06-1.93E+07-1.06E+06-6.27E+06-3.63E+06
5.71E+06-3.37E+06-4.32E+06
2.36E+06-2.22E+06
3.51E+06
2.83E+06
1.77E+06
6.08E+06-8.79E+06
3.35E+05-9.97E+06
1.62E+06-1.94E+07 ** -2.00E+07 *6.29E+06 *-6.19E+06
1.76E+06
3.93E+06 ***3.14E+05
1.59E+06-9.34E+05
1.13E+06
1.58E+06
1.12E+06-1.05E+06-7.64E+06-1.62E+07 *-1.11E+07
5.74E+06-0.0390.007-0.0100.091 **
0.048
-3.06E+06-3.67E+04-8.39E+06 *
0.028
0.005

0.022
-0.0010.018-0.004

28

OF_F

OF_C

OF_total

Changes in news announcements-5.84E+06-2.22E+07-6.02E+07
2.79E+07 ** -1.23E+07
5.56E+06
5.53E+06-3.88E+06-2.91E+06-6.69E+07-9.70E+07-1.38E+08-2.47E+06
3.08E+07
3.95E+07-1.14E+06-1.14E+06-1.93E+06-1.31E+07-4.48E+07 *** -4.02E+07 **2.30E+07-5.18E+07-2.29E+07
3.89E+06 *** 3.19E+05
1.67E+06-8.53E+05
1.08E+06
1.35E+06
4.92E+05-8.82E+05-8.08E+06-1.73E+07 *-7.35E+06
7.38E+06-0.0370.006-0.0040.094 **
0.045
-3.31E+06
2.47E+05-8.31E+06 *
0.028
0.005

0.033
0.010

0.019
-0.003

Table 6. Order Flow Reactions to Current Macro Fundamentals
This table present the explanatory power of changes in macro variables for one-month cumulative order flow for
the sample period 10/1/2001-6/29/2004. Explanatory power is examined using the order flow by financial
institutions (OF_F), corporations (OF_C), and total order flow (OF_Total). Flows are cumulative through theperiod corresponding to the measured change for each macro variable. Although not presented, standard errors
are estimated using an autocorrelation and heteroskedasticity consistent matrix of residuals. ***, **, and *denote significance at the 1%, 5%, and 10%, respectively.
EMU
EMU
EMU
EMU
US
US
US
US

CPI
Industrial Production
GDP
Unemployment Rate
CPI
Industrial Production
GDP
Unemployment Rate
R2
Adj. R2

OF_F
1.53E+08 ***3.08E+07 **7.90E+07
1.98E+07-6.49E+07 *OF_F
2.46E+08 **6.97E+07 *-5.67E+07
2.27E+06

OF_C-1.50E+08-2.92E+07-1.40E+09 **6.60E+07-2.05E+07

OF_C-1.32E+08
1.20E+08-7.31E+08
3.13E+07

OF_total
5.21E+08 **6.10E+07
1.29E+08-1.82E+08-8.63E+07

OF_total
5.75E+08 **2.62E+08
1.61E+08-2.16E+08

1.30E+08 *-2.31E+07 *1.56E+08

3.20E+08 ***1.16E+07
3.57E+08 *4.10E+08-4.44E+06
5.44E+08 **

0.206
0.015

0.351
0.173

0.259
0.066

0.178
0.047

0.262
0.069

0.286
0.092

29

Table 7. Extracting Macroeconomic Information from Order Flows
This table presents the results of regressing changes in each economic variable on contemporaneous cumulatedorder flows from financial institutions (OF_F) and corporate (OF_C). In the second row we also include preannouncement order flows in the regressions. Although not presented, standard errors are estimated using an
autocorrelation and heteroskedasticity consistent matrix of residuals. ***, **, and * denote significance at the
1%, 5%, and 10%, respectively.

Current
OF_F
EMU
EMU
EMU
EMU
EMU
EMU
EMU
EMU

CPI
5.42E-10 **CPI
5.28E-10 ***GDP
1.27E-09 ***GDP
1.04E-09
Industrial Production 1.51E-09 *Industrial Production 4.07E-12
Unemployment Rate -4.20E-11
Unemployment Rate -4.76E-11

US
US
US
US
US
US
US
US

CPI
CPI
GDP
GDP
Industrial Production
Industrial Production
Unemployment Rate
Unemployment Rate

Current
OF_C

Preannounce
ment
OF_F

-2.38E-11-2.55E-11
1.90E-10
3.53E-10
2.15E-10
4.45E-10-9.77E-10-8.95E-10
1.00E-09 *-9.97E-11 ***-9.61E-11 ** -3.36E-11

Preannounce
ment
OF_C
-1.47E-10-7.23E-10-4.03E-10-2.17E-11

2.17E-10 *-1.11E-11
2.23E-10 *-4.06E-11
5.89E-10
1.69E-10-9.51E-09 *** 6.83E-09 ***-7.03E-09 *** 8.21E-09 ** -3.68E-09
2.39E-09 *-1.12E-09 **1.23E-10-9.61E-10 *** -2.29E-10
8.53E-10 *** -3.18E-10
8.99E-11
1.88E-10 *4.63E-11
1.92E-10-3.70E-10
3.95E-1030

R2Adj. R2

0.103
0.118
0.563
0.651
0.057
0.059
0.107
0.129

0.039
-0.0230.438
0.301-0.013-0.1040.044-0.011

0.018
0.110
0.711
0.772
0.121
0.169
0.052
0.084

-0.052-0.032
0.629
0.544
0.060
0.037
-0.013-0.056

Table 8. Pricing of Macro Fundamentals
This table presents the results of the regressions exploring whether pre-announced macro fundamentals are
priced following the estimation strategy by Evans (2010). In the first stage regression, the change in macro
fundamental variable is regressed on contemporaneous order flows by financial institutions and corporations to
obtain the estimated value of the macro fundamental (as reported in Table 7). The second regression reported
here uses the estimate as an instrumental variable to estimate the impact of the fundamental variable on daily
exchange rate change. The standard errors are corrected for heteroskedasticity and autocorrelation using an MA
error process. ***, **, and * denote significance at the 1%, 5%, and 10%, respectively.

R2

FX return
EMU CPIEMU GDPEMU Industrial Production
EMU Unemployment Rate
US CPIUS GDP
US Industrial Production
US Unemployment Rate

6.416 **
3.295 *
1.554
6.984
14.778-0.548 **-3.161 *-3.169

31

0.059
0.051
0.019
0.008
0.010
0.043
0.086
0.004

Figure 1. Timeline
The evolution of the fundamental variable between t0 and tj is announced at tA. Changes in the fundamental
variable could result in contemporaneous order flow (“Current OF”), or future order flow (“Pre-announcement
order flow”), and the anticipation of these changes could affect lagged order flow. We allow any of these flows
to affect foreign exchange returns. The evolution of the fundamental might also directly affect returns. The
news puzzle aspect of the disconnect puzzle is driven by the relatively small effect that the announcement has
on FX returns.

32