Information Flows in Foreign Exchange Markets: Dissecting Customer Currency Trades

• DOI: http://doi.org/10.1111/jofi.12378
• Author: MAIK SCHMELING,LUKAS MENKHOFF,ANDREAS SCHRIMPF,LUCIO SARNO
• Published date: 01 April 2016
• Date: 01 April 2016

THE JOURNAL OF FINANCE •VOL. LXXI, NO. 2 •APRIL 2016

Information Flows in Foreign Exchange Markets:

Dissecting Customer Currency Trades

LUKAS MENKHOFF, LUCIO SARNO, MAIK SCHMELING,

and ANDREAS SCHRIMPF∗

ABSTRACT

We study the information in order flows in the world’s largest over-the-counter mar-

ket, the foreign exchange (FX) market. The analysis draws on a data set covering a

broad cross-section of currencies and different customer segments of FX end-users.

The results suggest that order flows are highly informative about future exchange

rates and provide significant economic value. We also find that different customer

groups can share risk with each other effectively through the intermediation of a

large dealer, and differ markedly in their predictive ability, trading styles, and risk

exposure.

THE FOREIGN EXCHANGE (FX) MARKET is the largest financial market in the world,

with a daily trading volume of about five trillion U.S. dollars (USDs; Bank for

International Settlements (BIS, 2013)). Also, the FX market is largely orga-

nized as an over-the-counter (OTC) market, meaning that there is no central-

ized exchange and that market participants can have only partial knowledge

about the trades of other market participants and available liquidity in differ-

ent market segments. Hence, despite its size and sophistication, the FX mar-

ket is fairly opaque and decentralized because of its market structure when

compared to, for example, the major equity markets. Adding to this lack of

∗Menkhoff is with the DIW Berlin and Humboldt University Berlin. Sarno is with Cass Business

School, City University London, and the Centre for Economic Policy Research (CEPR). Schmeling

is with Cass Business School, City University London. Schrimpf is with the Bank for Interna-

tional Settlements. The authors would like to thank Campbell Harvey (the Editor), an anonymous

Associate Editor, an anonymous referee, Alessandro Beber, GeirBjønnes, Claudio Borio, Michael

Brandt, Steve Cecchetti, Jacob Gyntelberg, Hendrik Hakenes, Joel Hasbrouck, Terrence Hen-

dershott, Gur Huberman, Søren Hvidkjær, Alex Kostakis, Jeremy Large, Albert Menkveld, Roel

Oomen, Richard Payne, Alberto Plazzi, Lasse Pedersen, Tarun Ramadorai, Jesper Rangvid, Paul

S¨

oderlind, Michel van der Wel, Adrien Verdelhan, as well as participants at several conferences,

workshops, and seminars for helpful comments and suggestions. Sarno acknowledges financial

support from the Economic and Social Research Council (No. RES-062-23-2340) and Menkhoff

and Schmeling gratefully acknowledge financial support from the German Research Foundation

(DFG). We have read the Journal of Finance’s disclosure policy and have no conflict of interest

to disclose. We agreed to allow the company that provided the data to review our paper before

publication. Their feedback was helpful but did not impact our narrative. The views expressed in

this paper are those of the authors and do not necessarily reflect those of the Bank for International

Settlements.

DOI: 10.1111/jofi.12378

601

602 The Journal of Finance R

transparency,various trading platforms have been introduced and market con-

centration has risen dramatically over the last decade, with a handful of large

dealers now controlling the lion’s share of FX market turnover (see, e.g., King,

Osler, and Rime (2012)). In centralized, exchange-based markets, there is a sin-

gle price at any point in time—the market price. In decentralized markets, by

default, there is no visible common price. The FX market is the largest market

of this kind.

This paper addresses several related questions that arise in this market

setting. First, does customer order flow contain predictive information for fu-

ture exchange rates? Answering this question is relevant for studies on market

microstructure and market design, and is useful for understanding the impli-

cations 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 or is their trading positively correlated and unloaded onto

dealers (as in, e.g., Lyons (1997))? Answering these questions is also relevant

for market design and provides a better understanding of the functioning of

OTC markets. Third, what characterizes different customer groups’ FX trad-

ing? For example, do they speculate on trends or are they contrarian investors?

And, what way are they exposed to or do they hedge against market risk?

Answering these questions can improve our understanding of what ultimately

drives different end-users’ demand for currencies and about the ecology of the

world’s largest financial market.

We tackle these questions empirically using a data set covering more than

10 years of daily end-user order flow for up to 15 currencies from one of

the top FX dealers. The data are disaggregated into two groups of financial

FX end-users (long-term demand-side investment managers and short-term

demand-side investment managers) and two groups of nonfinancial FX end-

users (commercial corporations and individual investors). We thus cover the

trading behavior of various segments of end-users that are quite heterogeneous

in their motives for market participation, informedness, and sophistication.

We find that (i) order flow by end-users is highly informative about future

exchange rate changes, (ii) different end-user segments actively engage in

risk sharing with each other through the intermediation of a large dealer,

and (iii) end-user groups show heterogeneous behavior in terms of trading

styles and strategies as well as their exposures to risk and hedge factors. This

heterogeneity across players is crucial for risk sharing and helps explain the

vast differences in the predictive content of flows across end-user segments

that we document in this paper.

To gauge the impact of order flow on currency excess returns, we rely on a

simple portfolio approach. This multicurrency framework allows for straight-

forward measurement of the economic value of the predictive content of or-

der flow and is a pure out-of-sample approach in that it only conditions on

past information. Specifically, we sort currencies into portfolios to obtain a

cross-section of currency excess returns, which mimics the returns to customer

Information Flows in Foreign Exchange Markets 603

trading behavior and incorporates the information contained in (lagged) flows.1

The information contained in customer trades is highly valuable from an eco-

nomic perspective. We find that currencies with the highest lagged total order

flows (i.e., the strongest net buying pressure across all customer groups against

the USD) outperform currencies with the lowest lagged flows (i.e., the strongest

net selling pressure across all customer groups against the USD) by about 10%

per annum (p.a.).

For portfolios based on disaggregated customer order flow, this spread in

excess returns is even more striking. A zero-cost long-short portfolio that mim-

ics long-term demand-side investment managers’ trading behavior yields an

average excess return of 15% p.a., while conditioning on short-term demand-

side investment managers’ flows leads to a spread of about 10% p.a. Flows

by commercial corporations basically generate no spread in returns, whereas

individual investors’ flows lead to a highly negative spread (about −14% p.a.).

In sum, we find that order flow is highly informative about future exchange

rates. This information is further enhanced by the nonanonymous nature of

transactions in OTC markets, as trades by different categories of customers

convey fundamentally different information for price movements.

What drives the predictive content in flows? We investigate three main chan-

nels. First, order flow could be related to the processing of information by mar-

ket participants via the process of “price discovery.” According to this view,

order flow acts as the key vehicle that impounds views about (economic) fun-

damentals into exchange rates.2If order flow contains private information, its

effect on exchange rates is likely to be persistent. Second, there could be a price

pressure (liquidity) effect due to downward-sloping demand curves (e.g., Froot

and Ramadorai (2005)). If such a mechanism is at play, we are likely to observe

a positive correlation between flows and prices for some limited time, followed

by a subsequent reversal as prices revert to fundamental values.3Third, we

consider the possibility that order flow is linked to returns due to the different

risk-sharing motives and risk exposures of market participants. For example,

order flow could reflect portfolio rebalancing of investors tilting their portfo-

lios toward currencies that command a higher risk premium. Related to this,

risk-sharing could lead to the observed predictability pattern if nonfinancial

customers are primarily concerned about laying off currency risk and implicitly

paying an insurance premium, while financial investors are willing to take on

that risk.

1Lustig and Verdelhan (2007) were the first to build cross-sections of currency portfolios.

2See, for example, Payne (2003), Love and Payne (2003), Evans and Lyons (2002, 2008), Evans

(2010), and Rime, Sarno, and Sojli (2010). Other papers relate order flow in a structural way to

volatility (Berger, Chaboud, and Hjalmarsson (2009)) or directly to exchange rate fundamentals

(Chinn and Moore (2011)).

3Several studies explore the underlying mechanism for the impact of order flow and discuss

the evidence in terms of information versus liquidity effects (e.g., Berger at al. (2008), Cerrato,

Sarantis, and Saunders (2011), Osler, Mende, and Menkhoff (2011), Menkhoff and Schmeling

(2010), Phylaktis and Chen (2010), Moore and Payne (2011), Ito, Lyons, and Melvin (1998)).