Markets: Dissecting Customer Currency Trades
Full Study from BIS:
We study the information in order
ows of dierent customer segments in the world’s
largest over-the-counter market, the foreign exchange market. The analysis draws on
a unique dataset covering a broad cross-section of currency pairs and distinguishing
trades by key types of foreign exchange end-users. We nd that order
ows are highly
informative about future exchange rates and provide signicant economic value for the
few large dealers who have access to these
ows. Moreover, customer groups systematically engage in risk sharing with each other and dier markedly in their predictive
ability, trading styles, and risk exposure.
JEL Classication: F31, G12, G15.
Keywords: Order Flow, Foreign Exchange Risk Premia, Heterogeneous Information, Carry Trades,
We would like to thank an anonymous Referee, Alessandro Beber, Claudio Borio, Geir Bjnnes, Michael
Brandt, Steve Cecchetti, Jacob Gyntelberg, Hendrik Hakenes, Campbell Harvey, Joel Hasbrouck, Terrence
Hendershott, Sren Hvidkjr, Gur Huberman, Alex Kostakis, Jeremy Large, Albert Menkveld, Roel Oomen,
Richard Payne, Alberto Plazzi, Lasse Pedersen, Tarun Ramadorai, Jesper Rangvid, Paul Soderlind, Christian
Upper, Adrien Verdelhan, Michel van der Wel, as well as participants at several conferences, workshops and
seminars for helpful comments and suggestions. We are very grateful to Gareth Berry, Georey Kendrick
and UBS for providing us with the proprietary data used in this study, and for numerous conversations
on the institutional details of foreign exchange trading at UBS. Sarno acknowledges nancial support from
the Economic and Social Research Council (No. RES-062-23-2340) and Menkho and Schmeling gratefully
acknowledge nancial support by the German Research Foundation (DFG). The views expressed in this paper
are those of the authors and do not necessarily re
ect those of the Bank for International Settlements.
yKiel Institute for the World Economy and Department of Economics, Leibniz Universitat Hannover,
Konigsworther Platz 1, 30167 Hannover, Germany, Tel: +49 511 7624552, Email: email@example.com.
zCass Business School and Centre for Economic Policy Research (CEPR). Corresponding author: Faculty
of Finance, Cass Business School, City University London, 106 Bunhill Row, London EC1Y 8TZ, UK, Tel:
+44 20 7040 8772, Fax: +44 20 7040 8881, Email: firstname.lastname@example.org.
Faculty of Finance, Cass Business School, City University London, 106 Bunhill Row, London EC1Y 8TZ,
UK, Email: email@example.com.
xBank for International Settlements and CREATES, Centralbahnplatz 2, 4002 Basel, Switzerland. Tel:
+41 61 280 8942. Email: firstname.lastname@example.org.The foreign exchange (FX) market is the largest nancial market in the world with a
daily trading volume of about four trillion U.S. dollars (BIS, 2010). Also, the FX market is
largely organized as an over-the-counter (OTC) market, meaning that there is no centralized
exchange and that market participants can have only partial knowledge about trades of other
market participants and available liquidity in dierent market segments. Hence, despite its
size and sophistication, the FX market is fairly opaque and decentralized because of its
market structure. Adding to this lack of transparency, various trading platforms have been
introduced and market concentration has risen dramatically over the last decade with a
handful of large dealers nowadays controlling the lion’s share of FX market turnover (see,
e.g., King, Osler, and Rime, 2012). The FX market can thus be characterized as a fairly
This paper addresses several related questions that arise in this opaque market setting.
First, do large dealers have an informational advantage from seeing a large portion of customer trades, that is, do customer trades carry economic value for the dealer? Answering
this question is relevant for regulators and useful for understanding the implications of the
observed shift in market concentration. Second, how does risk sharing take place in the FX
market? Do customers systematically trade in opposite directions to each other or is their
trading positively correlated and unloaded onto dealers (as in, e.g., Lyons, 1997)? Answering
these questions is highly relevant to provide a better understanding of the working of the
FX market and, more generally, the functioning of OTC markets. Third, what characterizes
dierent customer groups’ FX trading, e.g., do they speculate on trends or are they contrarian investors? In which way are they exposed to or do they hedge against market risk?
Answering these questions allows for a better grasp of what ultimately drives the demand for
currencies from dierent types of end-users and enhances the knowledge about the ecology
of the world’s largest nancial market.
We empirically tackle these questions by means of a unique data set covering more than
ten years of daily end-user order
ow for up to fteen currencies from one of the top FX
1Due (2012) provides a general overview of how opaqueness and market structure impact price discovery
and trading in \dark markets", that is, OTC markets.
1dealers, UBS. The data are disaggregated into four dierent groups of nancial (asset managers and hedge funds) and non-nancial (corporate and private clients) end-users of foreign
exchange. We therefore cover the trading behavior of various segments of end-users that are
quite heterogenous in their motives of market participation, informedness and sophistication.
Putting these data to work, we nd that: (i) Order
ow by end-users is highly informative for
future exchange rate changes and carries substantial economic value for the dealer observing
ows; (ii) there is clear evidence that dierent end-user segments actively share risks
with each other; and (iii) end-user groups follow very heterogeneous trading styles and strategies and dier in their exposures to risk and hedge factors. This heterogeneity across players
is crucial for risk sharing and helps explain the vast dierences in the predictive content of
ows across end-user segments that we document in this paper.
To gauge the impact of order
ow on currency excess returns, we rely on a simple portfolio
approach. This multi-currency framework allows for a straightforward measurement of the
economic value of the predictive content of order
ow and is a pure out-of-sample approach
in that it only conditions on past information. Specically, we sort currencies into portfolios
to obtain a cross-section of currency excess returns, which mimics the returns to customer
trading behavior and incorporates the information contained in (lagged)
ows.2 The information contained in customer trades is highly valuable from an economic perspective: We
nd that currencies with the highest lagged total order
ows (that is, the strongest net buying pressure across all customer groups against the U.S. dollar) outperform currencies with
the lowest lagged
ows (that is, the strongest net selling pressure across all customer groups
against the U.S. dollar) by about 10% per annum (p.a.).
For portfolios based on disaggregated customer order
ow, this spread in excess returns
is even more striking. A zero-cost long-short portfolio that mimics asset managers’ trading
behavior yields an average excess return of 15% p.a., while conditioning on hedge funds’
leads to a spread of about 10% p.a. Flows by corporate customers basically generate no spread
in returns, whereas private customers’
ows even lead to a highly negative spread (about -14%
p.a.). In sum, we nd that order
ow contains signicant economic value for a dealer with
2Lustig and Verdelhan (2007) were the rst to build cross-sections of currency portfolios.
2access to such information. Hence, the trend towards more market concentration observed
in FX markets over recent years clearly benets large nancial institutions acting as dealers
and potentially trading on this information in the inter-dealer market. These informational
advantages of dealers are further enhanced by the non-anonymous nature of transactions in
OTC markets, as trades by dierent categories of customers convey fundamentally dierent
information for price movements.
What drives the predictive content in
ows? We investigate three main channels. First,
ow could be related to the processing of information by market participants via the
process of \price discovery". According to this view, order
ow acts as the key vehicle that
impounds views about (economic) fundamentals into exchange rates.3
private information, its eect on exchange rates is likely to be persistent. Second, there
could be a price pressure (liquidity) eect due to downward-sloping demand curves (e.g.,
Froot and Ramadorai, 2005). If a mechanism like this is at play, we are likely to observe a
positive correlation between
ows and prices for some limited time, followed by a subsequent
reversal as prices revert to fundamental values.4 Third, we consider the possibility that
ow is linked to returns due to the dierent risk sharing motives and risk exposures of
market participants. For example, order
ow could re
ect portfolio rebalancing of investors
tilting their portfolios towards currencies that command a higher risk premium. Related to
this, risk sharing could lead to the observed predictability pattern if non-nancial customers
are primarily concerned about laying o currency risk and implicitly paying an insurance
premium, whereas institutional investors are willing to take on that risk.
Discriminating between alternative explanations for the predictive content of order
we nd clear dierences across the four segments of end-users. Asset managers’
associated with permanent shifts in future exchange rates, suggesting that their order
3See, e.g., Payne (2003), Love and Payne (2008), Evans and Lyons (2002a, 2007, 2008), Evans (2010),
and Rime, Sarno, and Sojli (2010). Other papers relate order
ow in a structural way to volatility (Berger,
Chaboud, and Hjalmarsson, 2009) or directly to exchange rate fundamentals (Chinn and Moore, 2011).
4Several studies explore the underlying mechanism for the impact of order
ow and discuss the evidence
in terms of information versus liquidity eects (e.g. Berger, Chaboud, Chernenko, Howorka, and Wright,
2008; Fan and Lyons, 2003; Marsh and O’Rourke, 2005; Osler, Mende, and Menkho, 2011; Menkho and
Schmeling, 2010; Phylaktis and Chen, 2010; Moore and Payne, 2011; Ito, Lyons, and Melvin, 1998).
3related to superior processing of fundamental information.5
In contrast, hedge funds’
are merely associated with transitory exchange rate movements, that is, the impact of their
trades on future exchange rates is far less persistent. This result is more in line with shortterm liquidity eects but not with fundamental information processing. Corporate customers’
and private clients’
ows, however, seem to re
ect largely uninformed trading.
Our results also point to a substantial heterogeneity across customers in their trading
styles and risk exposures, giving rise to dierent motives for risk sharing. First, we nd
that the trades of various end-user groups react quite dierently to past returns. Asset
managers tend to be\trend-followers"(positive feedback traders) with regard to past currency
returns. By contrast, private clients tend to be \contrarians" (negative feedback traders).
The latter nding squares well with recent ndings for equity markets by Kaniel, Saar, and
Titman (2008) who show that individual equity investors behave as contrarians, eectively
providing liquidity for institutional investors. Dierent from their results, however, private
clients do not directly benet from serving as (implicit) counterparties of nancial customers
in FX markets. Second, the
ows of most customer groups are negatively correlated over
short to intermediate horizons, suggesting that dierent groups of end-users in FX markets
engage in active risk sharing among each other. It is thus not just via the inter-dealer
market that risk is shared in FX markets, as documented by Lyons (1997), but a signicant
proportion of risk is shared among end-users in the customer-dealer segment. Third, we
nd substantial heterogeneity in the exposure to risk and hedge factors across customer
segments. Asset managers’ trading does not leave them exposed adversely to systematic risk,
which suggests that the information in their
ows is not due to risk taking but likely re
superior information. Hedge funds, by contrast, are signicantly exposed to systematic risk
such as volatility, liquidity, and credit risk. This lends credence to the view that hedge funds
earn positive returns in FX markets by eectively providing liquidity and selling insurance
to other market participants. For non-nancial customers there is some evidence of hedging
but it is not strong enough to fully explain their negative forecast performance arising from
5This information processing can come in dierent ways, e.g., a more accurate and/or faster interpretation
of macroeconomic news releases, and better forecasting of market fundamentals such as liquidity and hedging
demands of other market participants.
4poor short-term market timing.
Our paper is related to prior work on the microstructure approach to exchange rates
(e.g., Evans and Lyons, 2002a,b), which suggests that order
ow is crucial for understanding
how information is incorporated into exchange rates. It is well known from the literature
ow is positively associated with contemporaneous returns in basically all asset
classes; see, e.g., Hasbrouck (1991a,b) for stock markets, and Brandt and Kavajecz (2004)
for U.S. bonds. This is a stylized fact which also holds in FX markets, as shown by Evans
and Lyons (2002a) and many subsequent studies. There is less clear evidence, however, on
ow predicts exchange rates. A few papers have shown that FX order
contains information about future currency returns but tend to disagree on the source of this
predictive power (e.g., Evans and Lyons, 2005; Froot and Ramadorai, 2005; Rime, Sarno,
and Sojli, 2010).6 Some other papers fail to nd robust predictive power of exchange rates
ow in the rst place (see, e.g., Sager and Taylor, 2008). Our work is also related
to a dierent strand of recent literature that analyzes the returns to currency portfolios by
investigating the predictive power of currency characteristics, such as carry or lagged returns,
and the role of risk premia in currency markets.7
Overall, we contribute to the literature in the following ways. We are the rst to show
ow forecasts currency returns in an out-of-sample forecasting setting by forming
ow portfolios. This multi-currency investment approach provides an intuitive measure of the economic value of order
ow for the few large dealers observing these
seems important as earlier papers either did not consider out-of-sample forecasting at all
or relied on purely statistical performance measures derived from time-series forecasts of a
limited number of currency pairs (e.g., Evans and Lyons, 2005, who study the DEM/USD
and JPY/USD crosses). Time-series forecasts are aected by trends in exchange rates, most
notably the U.S. dollar. Our portfolio procedure, by contrast, studies exchange rate pre-
6There is also evidence that marketwide private information extracted from equity order
ow is useful for
forecasting currency returns (Albuquerque, de Francisco, and Marques, 2008).
7Lustig and Verdelhan (2007), Farhi, Fraiberger, Gabaix, Ranciere, and Verdelhan (2009), Ang and Chen
(2010), Burnside, Eichenbaum, Kleshchelski, and Rebelo (2011), Lustig, Roussanov, and Verdelhan (2011),
Barroso and Santa-Clara (2011) and Menkho, Sarno, Schmeling, and Schrimpf (2012a,b) all build currency
portfolios to study return predictability and/or currency risk exposure.
5dictability in dollar-neutral long-short portfolios, and it does so out-of-sample over very long
time spans compared to the extant FX microstructure literature. Moreover, we are the rst
to test whether risk exposure drives the information in customer order
ows. We show how
dierent key FX market players trade, e.g., to which extent they rely on trend-following or
behave as contrarians, and in which ways they are exposed to systematic risk. We nd strong
evidence of heterogeneity in the exposures and trading behavior across dierent groups of
market participants. These ndings indicate that there is signicant risk sharing between -
nancial and non-nancial customers as well as between dierent groups of nancial customers
(leveraged versus real money managers).
Taken together, these results have implications for our general understanding of information
ows in dark markets and how large dealers in OTC markets benet from observing a
large proportion of the order
ow. These results also add to our general understanding of
how risk is shared in nancial markets due to dierent motives for trade and trading styles
across end-user segments.
The rest of the paper is structured as follows. Section I describes our data, Section
II presents empirical results on the predictive power of order
ow, Section III empirically
investigates alternative underlying reasons for why order
ow forecasts FX excess returns,
and Section IV presents results of robustness tests.