How Slow Is the NBBO? A Comparison with Direct Exchange Feeds

Date01 May 2014
Published date01 May 2014
The Financial Review 49 (2014) 313–332
How Slow Is the NBBO? A Comparison
with Direct Exchange Feeds
Shengwei Ding
Wells Fargo Securities
John Hanna
Terrence Hendershott
University of California
This paper provides evidence on the benefits of faster proprietary data feeds from stock
exchanges over the regulated “public” consolidated data feeds. We measure and compare
the National Best Bid and Offer (NBBO) prices in each data feed at the same data center.
Price dislocations between the NBBOs occur several times a second in very active stocks and
typically last one to two milliseconds. The short duration of dislocations makes their costs
small for investors who trade infrequently, while the frequency of the dislocations makes
them costly for frequent traders. Higher security price and days with high trading volume and
volatility are associated with dislocations.
Keywords: market data, transparency, high-frequency trading
JEL Classifications: G10, G14
Corresponding author: Hass School of Business, 545 Student Services Bldg #1900, University of
California, Berkeley, Berkeley, CA 94720; Phone: (510) 643-0619; Fax: (510) 643-1412; E-mail:
Redline provided technological support for the project. We thank Gideon Saar,two anonymous referees,
and the guest editor, Michael Goldstein, for helpful comments.
C2014 The Eastern Finance Association 313
314 S. Ding et al./The Financial Review 49 (2014) 313–332
1. Introduction
Financial markets have evolved from manual, human-based, single-venue floor
trading to ultra-fast, low-latency, multi-venue, fully automated electronic trading.
Regulators have continuously updated rules to cope with the change. In the United
States, the Securities and Exchange Commission (SEC)’s regulatory objectives in-
clude maintaining fair, orderly, and efficient markets (O’Hara and Macey, 1999).
By examining the differences between publicly provided market data and data sold
directly from the exchanges we provide empirical evidence pertinent to assessing the
transparency and fairness of the U.S. equity markets.1Our results characterize the
amount of latency, the frequency and magnitude of price differences due to latency,
and the potential costs to investors arising from latency. We study trading in Apple
for one day to illustrate the details of latency in the data. We then examinea set of 24
securities for 16 days in May 2012. We find that using public information imposes
small costs for investors trading infrequently and not trading at times when price
dislocations between the public/regulated and direct exchange data feeds are more
likely.In contrast, active traders are at a substantial disadvantage if they use the public
Broadly speaking, there are two trading systems in the United States: reg-
istered exchanges and alternative trading systems. The registered exchanges are
required to provide the best bids and offers to be included in the consoli-
dated quotation system (CQS) and are also required to file any rule changes
with the SEC. The alternative trading systems include electronic communica-
tion networks and dark pools which do not provide best quotes to CQS, but
are required to match trades within a National Best Bid and Offer (NBBO).
In this study, we deal only with the quotation system based on registered
Trading occurs on 13 U.S. equity exchanges during our sample period (see
O’Hara and Ye, 2011, for evidenceon trading across exchanges and
for more recent data). With many exchangestrading stocks simultaneously, how it can
be ensured that the submitted order is executed at the best bid and offerprice across all
exchanges? This concern prompted the SEC to establish Regulation National Market
System (Reg NMS) in 2007 to protect fair access to the best price for investors,
particularly retail investors. Based on Reg NMS, exchanges are required to provide
the quotes to the primary exchanges such as NYSE and NASDAQ. The Security
Information Processors, known as SIPs for NYSE and NASDAQ, gather the data
1We use public data to refer to market data provided under Section 11A of the Exchange Act. What we
refer to as proprietary data typically includes more detailed data, for example, limit orders not at the best
price, and is not consolidated before distribution. Both data feeds are available to any subscriber, butthe
proprietary data are significantly more expensive.

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