Markets: Dissecting Customer Currency Trades Full

Markets: Dissecting Customer Currency Trades 

Full Study from BIS:


We study the information in order 

ows of di erent 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 signi cant 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 di er markedly in their predictive

ability, trading styles, and risk exposure.

JEL Classi cation: F31, G12, G15.

Keywords: Order Flow, Foreign Exchange Risk Premia, Heterogeneous Information, Carry Trades,

Hedge Funds.

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, Geo rey 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: menkho

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:

Faculty of Finance, Cass Business School, City University London, 106 Bunhill Row, London EC1Y 8TZ,

UK, Email:

xBank for International Settlements and CREATES, Centralbahnplatz 2, 4002 Basel, Switzerland. Tel:

+41 61 280 8942. Email: 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 di erent 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

\dark" market.1

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

di erent 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 di erent 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 di erent 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 di erent end-user segments actively share risks

with each other; and (iii) end-user groups follow very heterogeneous trading styles and strategies and di er in their exposures to risk and hedge factors. This heterogeneity across players

is crucial for risk sharing and helps explain the vast di erences 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. Speci cally, 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 signi cant 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 bene ts 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 di erent categories of customers convey fundamentally di erent

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

If order 

ow contains

private information, its e ect on exchange rates is likely to be persistent. Second, there

could be a price pressure (liquidity) e ect 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 di erent 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 di erences across the four segments of end-users. Asset managers’ 

ows are

associated with permanent shifts in future exchange rates, suggesting that their order 

ow is

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 e ects (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 e ects 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 di erent motives for risk sharing. First, we nd

that the trades of various end-user groups react quite di erently 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, e ectively

providing liquidity for institutional investors. Di erent from their results, however, private

clients do not directly bene t 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 di erent 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 signi cant

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 signi cantly 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 e ectively 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 di erent 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

that order 

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

whether order 

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

by order 

ow in the rst place (see, e.g., Sager and Taylor, 2008). Our work is also related

to a di erent 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

that order 

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 

ows. This

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 a ected 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

di erent 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 di erent groups of

market participants. These ndings indicate that there is signi cant risk sharing between -

nancial and non- nancial customers as well as between di erent 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 bene t 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 di erent 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.