Quote Stuffing

• Author: Robert A. Van Ness,Bonnie F. Van Ness,Jared F. Egginton
• DOI: http://doi.org/10.1111/fima.12126
• Published date: 01 August 2016
• Date: 01 August 2016

Quote Stuffing

Jared F. Egginton, Bonnie F. Van Ness, and Robert A. Van Ness∗

In this study we examine intense episodic spikes in quoting activity (frequently referred to as quote

stuffing) on market conditions. We find that quote stuffing is pervasive and that over 74% of US

exchange-listed securities experienced at least one episode during 2010. Wealso f ind that stocks

experience decreased liquidity, higher trading costs, and increased short-term volatility during

periods of intense quoting activity. We find that most quote-stuffing events occur on the NYSE,

ARCA, NASDAQ,and BATS and that during these quote-stuffing events, the number of new orders

and canceled orders increases substantially while the order size and order duration decrease.

Quote stuffing is the practice where a large number of orders to buy or sell securities are placed

and then canceled almost immediately. These intense episodic spikes in order submissions and

cancellations have come under scrutiny from the media and regulators (see, e.g., Lauricella and

Strasburg, 2010). Market participants criticize the practice, stating that it creates a false sense

of the supply and demand for a stock. Sean Hendelman, chief executive officer at T3 Capital,

expressed his concern stating, “People are relying on the [stock quote data] and the data is not

real” (Lauricella and Stasburg, 2010, p. A.1). Others liken the practice to an auctioneer placing

“plants” and “shills” in the audience in an attempt to manipulate prices through fake bidding

(Elder, 2010). Are these concerns justified? How prevalent is quote stuffing? Does quote stuffing

adversely affect market conditions, and if so, to whatdegree? Are quote-stuff ing eventslocalized

on one exchange or are quoting and trading altered on all exchanges during quote-stuffing events?

This article seeks to address these questions.

Although quote stuffing is often linked to high-frequency trading (HFT), smart order routers

and other algorithmic traders, who are not high-frequency traders, may also be quote stuffing.

Nevertheless, HFT has garnered increased attention in the wake of the May 6, 2010 flash crash

when the Dow Jones Industrial Average collapsed 998.5 points in a few minutes. HFT is a

strategy where securities are rapidly purchased and sold through the use of computer algorithms.1

Holding periods for securities bought and sold by high-frequency traders are typically very short,

lasting just seconds or milliseconds. Furthermore, high-frequency traders may move in and out

of positions thousands of times per day. The Securities and Exchange Commission (SEC, 2010)

calls HFT “one of the most significant market structure developments in recent years.” SEC

Chairwoman Mary Schapiro (2010) describes the regulatory scheme that applies computer-based

We thank an anonymous referee, Marc Lipson (Editor), Robert Battalio, Kathleen Fuller, Michael Goldstein, Andreas

Heinen, PawanJain, Michael Lewis, Ananth Madhavan, Sandra Mortal, Pamela Moulton, TomMcInish, Andriy Shkilko,

Jim Upson, Bob Wood, Adam Yore, and seminar participants at Auburn University, Louisiana Tech University, The

Universityof Mississippi, Mississippi State University, Ohio University, the University of Richmond, Utah State University,

the Eastern Finance Association annual meeting (2013), the Southern Finance Association annual meeting (2012), the

Midwest Finance Association annual meeting (2014), the Magnolia Finance Conference (2015), and the Financial

Management Association annual meeting (2012).

∗Jared F.Egginton an Assistant Professor at Louisiana Tech University in Ruston, LA. Bonnie F. Van Ness is a Professor

of Finance at The University of Mississippi in University, MS. Robert A. Van Ness is a Professor of Finance at The

University of Mississippi in University, MS.

1HFT is a subset of algorithmic trading, where algorithmic trading is broadly defined as the use of a computer algorithm

to automatically submit, cancel, and otherwise manage orders.

Financial Management •Fall 2016 •pages 583 – 608

584 Financial Management rFall 2016

low-latency trading as “[an] area that warrants close review.” Today HFT makes up a significant

portion of US equities market volume.2

Despite the criticism of HFT and algorithmic trading (AT) by the popular press and market

participants, early academic work finds little evidence that the practice is detrimental to financial

markets. Recent studies show that, in aggregate, HFT improves traditional measures of market

quality and contributes to price discovery (Hasbrouck and Saar, 2013; Brogaard, 2010). Addi-

tionally, Menkveld (2011) examines the high-frequency trader’s role as a modern market maker

and finds it to be crucial to the operation of a new market.

Many AT and HFT strategies rely on the ability to trade fast and frequently.3Latency arbitrage

is one such strategy in which high-frequency traders attempt to profit from inefficiencies in

data between exchanges or other market centers. By submitting large numbers of orders that are

canceled very quickly, a high-frequency trader may create exploitable latency arbitrage oppor-

tunities. Brogaard (2010) explains that latency arbitrage opportunities from quote stuffing may

arise from requiring other traders to process large amounts of volume, giving the high-frequency

trader submitting the orders an advantage.4A large number of order submissions may also cause

the exchange receiving the quotes to lag other exchanges, creating arbitrage opportunities.

It is also possible that large bursts of quoting activity may not be caused for manipulative

purposes. Large episodic spikes in quoting activity may be generated for technological reasons

where two algorithms interact with each other and fail to converge. For example, one algorithm

submits a quote that causes another algorithm to reply, causing the first algorithm respond. If

this process of multiple algorithms “chasing” each other continues, a large burst of quotes will

be generated. Although the large burst of message flow may not be part of a nefarious plan

to manipulate the market, these quoting episodes may still be associated with degraded market

conditions.

In this study, we identify and analyze intense episodic spikes in quoting activity. We examine

market conditions, including liquidity and volatility around these episodic spikes. We find that

episodes of large bursts of quote updates are pervasive, with events occurring every trading

day and affecting over 74% of US-listed equities. Our results suggest that in periods of intense

quoting activity, stocks experiencedecreased liquidity, higher trading costs, and increased short-

term volatility. Thus, quote stuffing may exhibit some market-degrading features (and could be

creating the latency arbitrage opportunities described by Brogaard, 2010 and Biais and Woolley,

2011).

This study is also related to the broader research on market manipulation (see Allen and

Gorton, 1992; Jarrow, 1992; Kumar and Seppi, 1992; Mei, Wu, and Zhou, 2004; Goldstein and

Guembel, 2008). Aggarwal and Wu (2006) present a theoretical model and empirical evidenceof

stock market manipulation. They find that in the presence of stock price manipulation, volatility

increases and market efficiency worsens. Aggarwal and Wu’s(2006) model suggests a strong role

for regulation to discourage manipulation.

In 2010 the Financial Industry Regulatory Authority (FINRA) fined Trillium Brokerage Ser-

vices LLC and nine of its traders $2.26 million for illicit market manipulation. FINRA accused

Trillium of creating a “false appearance of buy- or sell-side pressure” through an illicit “layer-

ing” strategy. Layering involves traders entering multiple fake orders to create a false buying or

selling interest with a goal of improperly baiting unsuspecting market participants into executing

2Brogaard (2010) estimates that HFT makes up 77% of dollar trading volume in US equities.

3See Gomber et al. (2011) and Brogaard (2010) for detailed descriptions of HFT strategies.

4Biais and Woolley(2011) also discuss high-frequency traders using quote stuffing to create congestion in the market by

submitting a large number of orders to the market and thus impairing the market for slow traders.