Dealer Networks

• Published date: 01 February 2019
• Author: NORMAN SCHÜRHOFF,DAN LI
• Date: 01 February 2019
• DOI: http://doi.org/10.1111/jofi.12728

THE JOURNAL OF FINANCE •VOL. LXXIV, NO. 1 •FEBRUARY 2019

Dealer Networks

DAN LI and NORMAN SCH ¨

URHOFF∗

ABSTRACT

Dealers in the over-the-counter municipal bond market form trading networks with

other dealers to mitigate search frictions. Regulatory data show that this network

has a core-periphery structure with 10 to 30 hubs and over 2,000 peripheral broker-

dealers in which bonds flow from periphery to core and partially back. Central dealers

charge investors up to double the round-trip markups compared to peripheral dealers.

In turn, central dealers provide immediacy by matching buyers with sellers more

directly and prearranging fewer trades, especially during stress times. Investors thus

face a trade-off between execution cost and speed, consistent with network models of

decentralized trade.

DEALERS PLAY A PIVOTAL ROLE in price formation and liquidity in over-the-

counter (OTC) financial markets. In frictionless markets, buyers and sellers

find each other immediately and realize gains from trade at competitive prices.

This is not the case, however, in decentralized markets. In particular, the

opaque nature of bilateral OTC transactions creates frictions in finding coun-

terparties that can reduce price and allocation efficiency (Duffie, Gˆ

arleanu,

and Pedersen (2005)). To address these search and matching frictions, broker-

dealers match buyers with sellers not only among their own clients, but also

warehouse securities and form trading networks with other dealers. The trad-

ing networks allow the dealers to find counterparties more quickly and real-

locate securities more efficiently. Given how crucial the dealer networks are

for liquidity provision in OTC markets, surprisingly little is known empirically

about them, as a lack of granular data so far has precluded serious empirical

study of the network structure and its impact on the terms of trade for investors.

∗Dan Li is with the Board of Governors of the Federal Reserve System, Washington,DC. She has

no conflict of interest to disclose. Norman Sch¨

urhoff is with the Faculty of Business and Economics

and Swiss Finance Institute, University of Lausanne. Sch¨

urhoff is also Research Fellow of the

CEPR. He gratefully acknowledges research support from the Swiss Finance Institute and the

Swiss National Science Foundation under Sinergia project CRSII1_154445/1, “The Empirics of

Financial Stability” and has no conflict of interest to disclose. We thank the Editor, Bruno Biais,

an Associate Editor, three anonymous referees, and discussants Jeff Harris, Larry Harris, Terry

Hendershott, Christian Julliard, Bernd Mack, Tarun Ramadorai, and Elvira Sojli for extensive

comments. Seminar audiences at many conferences and invited seminars have provided valuable

feedback, particularly Andrew Ang, Jean-Edouard Colliard, Fany Declerck, Darrell Duffie, Amy

Edwards, Burton Hollifield, Laurence Lescourret, Craig Lewis, Semyon Malamud, Sophie Moinas,

Artem Neklyudov, Christine Parlour, Michael Rockinger, and Pierre-Olivier Weill. We are deeply

indebted to Rick Green for advice. We thank the Municipal Securities Rulemaking Board for

providing the transactions data with dealer identifiers.

DOI: 10.1111/jofi.12728

91

92 The Journal of Finance R

In this paper, we employ regulatory audit trail data for the municipal bond

market to provide evidence on how dealer networks affect trade execution

in decentralized OTC markets during normal and stress times. In an effort

to improve market transparency, the Municipal Securities Rulemaking Board

(MSRB) has required broker-dealers to report all their trades since 1998. The

supervisory data set provides dealer identities for all 72 million municipal bond

trades between 1998 and 2012. The granularity of these trade data allows us

to trace the bonds through the dealer network, map the intermediation chains

and network structure, and compare the execution terms that investors receive

across different dealers.

We find that interconnectedness among dealers reduces search and allocation

frictions, but it also creates opportunities for dealers to employ market power in

privately negotiated transactions. The dealer network in municipal bonds has

a core-periphery structure with about 10 to 30 highly interconnected dealers at

the center and more than 2,000 broker-dealer firms sparsely connected at the

periphery.1Use of the interdealer network is pervasive, with more than 20%

of round-trip chains connecting buyer with seller through two or more dealers.

The dealers with the most connections act as hubs in intermediation chains that

can involve up to seven dealers, consistent with models of intermediation chains

(Colliard and Demange (2014), Glode and Opp (2014), Hugonnier, Lester, and

Weill (2015), Neklyudov and Sambalaibat (2015)). Moreover, dealers’ trading

relations with each other are long-lived and centrality rankings of the dealers

are persistent over time. These findings suggest that a basic random search

model cannot easily match the data, and that network-based models of trade

are better suited to capture the interdealer market for municipal bonds (Babus

(2012), Chang and Zhang (2015), Wang (2016)).

We next examine whether central dealers pass on the time and cost savings

that result from their network connections in the form of smaller markups,

or whether these dealers charge larger markups for reducing search delays

and speeding up trade execution. We find that central dealers charge larger

markups to investors than their peripheral competitors. The centrality pre-

mium is sizable, with markups at central dealers up to twice those charged by

peripheral dealers. At the same time, central dealers handle the majority of

the order flow. The premium of 0.4% to 0.7% of par value that central dealers

charge relative to peripheral dealers is equivalent to several months of a bond’s

interest income. Investors in municipal bonds thus face a trade-off between ex-

ecution speed and cost. This result is important, as Lagos and Rocheteau (2007)

show that a sufficient degree of market power is required to incentivize dealer

entry and welfare-maximizing allocations.

The centrality premium and the fact that central dealers handle most of

the order flow in spite of being more expensive may seem surprising given the

presence of a large number of active broker-dealers in the market. We explore a

1About 700 broker-dealer firms actively trade in municipals in a given month. Therefore, the

rolling dynamic dealer network that we use in the empirical analysis has, on average, about 700

dealers a month.

Dealer Networks 93

variety of potential explanations. The most obvious explanations do not appear

to play a role. In particular, central dealers do not systematically intermediate

riskier bonds and are not compensated for higher price risk, as they are less

likely to lose money in round-trip transactions than peripheral dealers.

Rather, consistent with central dealers providing more immediate execution

and getting compensated for doing so, we find that central dealers match buyers

with sellers more directly. After purchasing a bond from an investor, a central

dealer is more likely to sell it to the end-buyer than to off-load it to another

dealer. In addition, the length of the intermediation chain is shorter for round-

trips that start with a more central dealer: compared to sparsely connected

peripheral dealers, dealers that possess larger trading networks have better

access to clients and more information about which securities are available and

who wants to buy or sell, which results in shorter intermediation chains. When

we use detailed time stamps on each trade leg and associated dealer identifiers

to infer differences in investors’ execution delays based on the level of inventory

risk that dealers take, we find that, compared to peripheral dealers, central

dealers are more likely to offer immediacy by trading on a principal basis,

that is, by taking bonds into inventory, than to prearrange trades between a

buyer and a seller, which takes time to execute. Further, conditional on taking

a bond into inventory, central dealers tend to turn the bond around faster than

peripheral dealers, which suggests that central dealers also find buyers more

quickly. The higher propensity of central dealers to trade on a principal basis,

that is, to actively make a market, reduces execution delays and increases

trading gains for investors that value speed.

The municipal bond market provides a rich setting for studying search and

matching frictions. It is a typical OTC market in that trades are negotiated

bilaterally and search frictions are high, for a number of reasons. First, as-

set heterogeneity is vast, with more than 1.5 million different bonds and over

50,000 issuers, each with its own idiosyncrasies. Second, the natural holders

of municipals range from retail investors to sophisticated institutions such

as banks, mutual funds, and insurance companies. Because these are mostly

buy-and-hold investors, municipal bonds trade infrequently in the secondary

market (Green, Hollifield, and Sch ¨

urhoff (2007)). When investors do trade,

it is typically for liquidity reasons, not private information about fundamen-

tals.2Third, information about available bonds and suitable counterparties is

sparse. Indeed, it is known to be a buyer’s market in which identifying investors

willing to buy is a dealer’s most crucial task (Schultz (2012)). Locating bonds

and potential buyers thus requires that financial intermediaries have active

relationships with various types of investors as well as with other dealers. Fi-

nally, the municipal bond market is ideal for studying competition in liquidity

provision because of the large number of financial intermediaries—there are

over 2,000 registered broker-dealers—while the market’s segmentation along

2Adverse selection risks are low in the municipal bond market since 75% of municipal bonds

are AAA-rated and historical default rates are just around 0.1% per year.