Hidden Liquidity: Some New Light on Dark Trading

• Date: 01 October 2015
• DOI: http://doi.org/10.1111/jofi.12301
• Author: GIDEON SAAR,MAUREEN O'HARA,ROBERT BLOOMFIELD
• Published date: 01 October 2015

THE JOURNAL OF FINANCE •VOL. LXX, NO. 5 •OCTOBER 2015

Hidden Liquidity: Some New Light on Dark

Trading

ROBERT BLOOMFIELD, MAUREEN O’HARA, and GIDEON SAAR∗

ABSTRACT

Using a laboratory market, we investigate how the ability to hide orders affects

traders’ strategies and market outcomes in a limit order book environment. We find

that order strategies are greatly affected by allowing hidden liquidity, with traders

substituting nondisplayed for displayed shares and changing the aggressiveness of

their trading. As traders adapt their behavior to the different opacity regimes, how-

ever,most aggregate market outcomes (such as liquidity and informational efficiency)

are not affected as much. We also find that opacity appears to increase the profits of

informed traders but only when their private information is very valuable.

Hidden liquidity is now a standard feature of trading in equity markets. Virtu-

ally all exchanges allow traders to “hide” all or a portion of their orders on the

book, resulting in market liquidity having both a displayed and a nondisplayed

component. Although nondisplayed orders generally lose priority to displayed

orders at a given price, the invisibility of these orders can be valuable for

a variety of trading strategies. With orders hidden, however, market partici-

pants have only incomplete knowledge as to the overall depth in the market.

Moreover, the ability to put hidden orders inside the displayed spread means

that even the best prevailing prices are not observable. This evolution to “dark

trading” in exchange markets, where orders are not publicly displayed prior

to execution, has gained momentum in recent years, driven in part by the

rise of crossing networks (which also allow traders to hide their trading inten-

tions) and in part by competitive pressures from new exchanges and trading

∗Robert Bloomfield, Maureen O’Hara, and Gideon Saar are from the Johnson Graduate School

of Management, Cornell University. We thank Alyssa Andersen for valuable research assistance.

We would like to thank Thomas George, Michael Goldstein, Campbell Harvey (the Editor), An-

drew Karolyi, Yelena Larkin, an anonymous referee, and seminar or conference participants at

the 8th Annual Central Bank Workshop on the Microstructure of Financial Markets, Cornell Uni-

versity, DSF-TI Amsterdam, FINRA, Imperial College, Tilburg University, University of Illinois,

University of Massachusetts–Amherst, University of Notre Dame & NASDAQ OMX Conference on

Current Topics inFinancial Regulation, University of Texas at Austin, University of Warwick, and

Vanderbilt University for helpful comments. Wethank the Notre Dame finance faculty and NERA

Economic Consulting for awarding us the Best Paper Prize at the Conference on Current Topics

in Financial Regulations. Disclaimer: This research was not specifically supported or funded by

any organization. Maureen O’Hara serves as Chairman of the Board of Directors of ITG (Invest-

ment Technology Group). Gideon Saar serves as a member (uncompensated) of FINRA’sEconomic

Advisory Committee.

DOI: 10.1111/jofi.12301

2227

2228 The Journal of Finance R

platforms. Despite the 1975 Congressional mandate that U.S. equity markets

be transparent, the reality is that markets are becoming increasingly opaque.

Regulators both in the United States and abroad are questioning the role

that hidden liquidity plays in markets.1Much of this regulatory scrutiny has

focused on crossing networks, but the hidden liquidity in exchange settings is

actually of comparable or greater importance, with estimates of approximately

20% or more of marketable orders executing against nondisplayed depth in U.S.

markets.2Advocates argue that hidden orders enhance market performance by

helping traders shield their trading intentions from the predations of oppor-

tunistic traders. Critics counter that these advantages to individual traders

come at the expense of the market as a whole by degrading the liquidity and

informational efficiency of the market.

It is important to note that nondisplayed liquidity existed before the advent

of fully electronic markets. Blume and Goldstein (1997), for example, discuss

NYSE “not held” orders whereby clients instructed floor brokers to use their

discretion in executing the orders. Floor brokers often chose not to put such

orders in the book, but participated in the trading process in a discretionary

way. Nondisplayed orders in electronic markets can be viewed as replicating

at least some of the services performed by floor brokers. However, the current

form of hidden liquidity in electronic limit order books is also associated with

a variety of complications such as pinging (i.e., placing and cancelling orders

simply to discover hidden orders on the book) and increased message traffic.

There seems to be wide agreement that the existence of nondisplayed orders

in limit order books and the proliferation of crossing networks have increased

uncertainty about the level of liquidity in the market.

Resolving debates on how the ability to hide all, some, or no orders impacts

markets is complicated by a variety of factors. One is simply that all mar-

kets now feature hidden liquidity, making comparisons to the counterfactual

difficult. Moreover, trader behavior should be affected by market design, sug-

gesting that any analysis should examine how hidden liquidity affects order

strategies (which are typically unobservable to both market participants and

academic researchers). Even if all data were made available, however, markets

often adopt new opacity regimes in response to competitive pressures, compli-

cating before-and-after empirical analyses. On the theoretical side, models are

often limited to describing an economic environment simple enough to facilitate

closed-form solutions. It is notoriously difficult to solve models in which traders

with differential information pursue unconstrained strategies in a continuous-

time electronic limit order book. Theoretical studies therefore impose various

restrictions on trader strategies or their information sets, but it is not clear

1The Securities and Exchange Commission and the Ontario Securities Commission (the main

Canadian regulator) have introduced new rules for dark trading, while European regulators are

debating transparency rules in a revised MiFid framework.

2Exactly how much is trading in hidden orders on exchanges is a subject of debate. Hasbrouck

and Saar (2009) find that approximately 15% of marketable orders execute against hidden depth

in a sample of stocks traded on Inet in 2004. These numbers increase to 17.3% and 19.0% in 2007

and 2008, respectively, for a NASDAQ data set investigated in Hasbrouck and Saar (2013).

Hidden Liquidity: Some New Light on Dark Trading 2229

how well these restrictions accord with actual trader behavior. Taken together,

these difficulties have limited the ability of even the most insightful empiri-

cal and theoretical analyses to draw definitive conclusions as to the market

consequences of different opacity regimes.

In this paper,we use an experimental methodology to investigate how nondis-

played liquidity affects the market environment in a limit order book market.

Our analysis features competitive informed traders who receive signals about

the true value of securities and liquidity traders who must meet portfolio tar-

gets. Markets operate continuously, allowing traders to implement a variety of

trading strategies. Our trading platform features an electronic limit order book

in which traders can enter orders of different sizes that can be cancelled at any

time, choose to make liquidity (by placing limit orders in the book) or take liq-

uidity (by hitting existing limit orders), and choose to display or not display all

or part of any order depending on the rules of the market.3Execution priority

rules resemble those in actual markets, where displayed orders have priority

over nondisplayed orders. Overall, the functionality of our trading platform

mimics that of current electronic markets, and allows us to investigate the

effects of transparency on trader and market behavior.

Experiments provide a useful complement to theoretical and empirical work

on this topic. Until regulators or exchanges are willing to randomly assign se-

curities and traders to different opacity regimes, naturally created data face

challenges in making clean causal inferences relating opacity to trading be-

havior and market outcomes. Furthermore, real-world trading mechanisms

and regulatory regimes can be much more complex than those theorists model

to derive predictions. A laboratory experiment can employ a trading mecha-

nism that is rather close to that used in actual markets, while still having the

ability to control and manipulate variables to allow clean causal inferences.

We investigate three market structures (or opacity regimes): (1) visible mar-

kets, in which all orders must be displayed, (2) iceberg (or reserve) markets,

which allow both displayed and partially displayed orders (i.e., a minimum

size must be displayed and the remainder can be nondisplayed), and (3) hidden

markets, in which orders can be displayed, partially displayed, or completely

nondisplayed. Trading takes place in only one type of market at a time. We

then compare equilibria across the three market structures, having employed

experimental controls for learning, cohort, and other effects known to influence

experimental studies. Our focus is on investigating a new form of opacity in

today’s markets that arises endogenously when each trader decides how much

of his or her order to expose. We test hypotheses suggested by the literature on

how endogenous opacity affects trader behavior, with a particular emphasis on

the disparate effects on informed and liquidity traders. We also investigate how

endogenous opacity affects overall market performance as captured by various

liquidity and informational efficiency measures.

3See Bloomfield, O’Hara, and Saar (2005) for analysis of the make-or-take decision in an elec-

tronic market.