Asymmetric news responses of high‐frequency and non‐high‐frequency traders

Date01 August 2019
Published date01 August 2019
DOI: 10.1111/fire.12200
Asymmetric news responses of high-frequency and
non-high-frequency traders
S. Sarah Zhang
Alliance Manchester Business School, University
of Manchester, Manchester, UK
S.Sarah Zhang, Alliance Manchester Business
School,University of Manchester, Booth Street
East,Manchester M15 6PB, UK.
Using NASDAQ trade and Reuters news data, I show that the
response of aggressive non-high-frequency traders (nHFTs)to news
is stronger than that of aggressive high-frequency traders (HFTs).
Classifying news into quantitative (“hard”) and less quantitative
(“softer”) news, the trading response of aggressive nHFTs to softer
news exceeds HFTs’ response. Positive news elicits greater return
and nHFT responses than negative news during the 2008 financial
crisis period. As this phenomenon persists even after excluding the
2008 short-sale ban, the results support the hypothesis of nHFTs
exhibiting stronger asymmetric responses during crisis periods.
financial crisis, high-frequency trading, information processing,
G10, G14
Thelast decade has witnessed a significant increase in the use of computer algorithms in financial markets (cf. Deutsche
Bank, 2016). High-frequency traders (HFTs)1utilizespecific groups of computer trading algorithms and contribute to
price discovery through the application of different information processing strategies. While some people claim that
computerized traders follow complexand sophisticated trading strategies that surpass human traders in “scale, speed,
and complexity,”2others argue that HFTs and algorithmic traders (ATs) merely focus on simplistic strategies and do
not possess the equivalent abilities to human traders for processing complex information.3Regulatory concerns, such
as those of the U.S. Securities and Exchange Commission (SEC) and the European Securities and Markets Authority
(ESMA), focus on the effects of HFTson market quality and price discovery (cf. ESMA, 2011; SEC, 2010).
To analyze the regulatory concerns related to price discoveryand the question of whether HFTs are better able
to process complex information than non-high-frequency traders (nHFTs),I investigate the role of HFTs and nHFTsin
1HFTis a subcategory of AT,which is commonly defined as the use of computer algorithms to support the trading process (cf. Hendershott, Jones, & Menkveld,
2011).HFTs distinguish themselvesfrom other groups of traders through their use of high-speed trading and information processing, their high trading volume,
andtheir sophisticated algorithms for use in a proprietary capacity rather than for agency purposes (see SEC, 2010).
2See“The Irreversible Rise of the Investing Machines,” FT View, 2018 (
3See“Human Traders Can Still Beat Computers,” FT Opinion Smart Money, 2015 (
Financial Review.2019;54:451–475. c
2019 The Eastern Finance Association 451
processing stock-specific news information. Using an HFT data set from NASDAQ and news data from Reuters News-
Scope Sentiment Engine (RNSE), I provide insight into the intradaynews responses of stock returns and net trading of
HFTs and nHFTs. I further analyze whether responses differ according to news types, such as “hard” quantitative or
“softer” less quantitative news, and news direction, such as positive or negative news.
The results show that stock-specific news has significant effects on stock returns and net trading and that the
strength of response of aggressive nHFTs (liquidity-demanding traders initiating a transaction bysubmitting a mar-
ketableorder) to news releases exceeds that of aggressive HFTs.Classifying news announcements based on their quan-
titative (hard) and less quantitative (softer) content, I show that nHFTsrespond more strongly to softer news. There is
some evidence of a more pronounced response of nHFTsto positive rather than negative news during financial crisis
periods, which is in contrast to previous findings in the literature (e.g.,Tetlock, 2007). The analysis of time variation of
the effect in different periods during the 2008–2009 financial crisis reveals an enhanced asymmetry effect during the
peak of the financial crisis in 2008, supporting the hypothesis of differential behavior in recession periods by Garcia
(2013). As the short-sale ban of 2008 cannot explain those effects, thedifferential processing and responses of nHFTs
to positive and negative news are the more likelycause of an asymmetry effect rather than short-selling restrictions.
The results have important implications for our understanding of information processing and price discoveryin the
U.S. stock market. Theyserve to alleviate regulatory concerns that HFTs may initiate an overreaction to news. In con-
trast, nHFTsreact more to news than HFTs and nHFTsappear more prone to asymmetric responses. Furthermore, this
paper shows that nHFTs and HFTs specialize in different types of information releases, and HFTs provide enhanced
liquidity after news releases, which facilitates price discovery.This study is the first to document HFT behavior around
stock-specific news releases as well as the differential responses of HFTs and nHFTs to different types of news. The
paper's contributions are threefold.
First, I contribute to the growing body of literature that analyzes HFT and nHFT behavior around news announce-
ments.4I show that nHFTsoverall exhibit a stronger response to news information than HFTs,which parallels the find-
ings of Hautsch, Noe, and Zhang (2017) for macroeconomic announcements. Furthermore, this paper providesa more
detailed insight into the types of information processed by HFTs. Jovanovic and Menkveld (2016) suggest that the
advantage of HFTslies in analyzing “hard” quantitative information. Following Liberti and Petersen (2018), I distinguish
further between hard quantitativeand softer less quantitative information events.5I show a slightly stronger response
of HFTsto hard news, whereas nHFTs respond more strongly to softer news than HFTs. These results are in line with
the theoretical predictions of Jovanovic and Menkveld (2016) and Foucault, Hombert, and Rosu (2016), which state
that HFTs are better able to process quantitative information and might trade on more “precise”information. Using
the NASDAQHFT data set, I compare the trading responses of HFTs and nHFTsand test the predictions for both types
of traders and different kinds of news.
Second, I extend the literature on the intradayeffects of stock-specific news announcements using the same RNSE
data set as Kyle, Obizhaeva, Sinha, and Tuzun (2012),6Groß-Klußmann and Hautsch (2011), and Riordan, Storken-
maier, Wagener, and Zhang (2013). Specifically, I revealsignificant return and trading responses to news. My results
therefore complement the results of Groß-Klußmann and Hautsch (2011) and Riordan et al. (2013), who find an
increase in trading activity and higher adverse selection costs around news arrivals. I further show that the classifi-
cation of news based on its hardness or sentiment can yield significantly different results for return and net trading
responses. Therefore, I provide deeper insight into the cause for the documented differential effects of news on liquid-
ity,as demonstrated by Riordan et al. (2013).7
4Ageneral overview of the literature on HFT and its impact on financial markets is providedby Biais and Foucault (2014), Goldstein, Kumar, and Graves(2014),
Lattemannet al. (2012), Menkveld (2016), and O'Hara (2015), among others.
5Asdiscussed by Liberti and Peterson (2018, p. 2), the classification of information into hard and soft is a simplification and it is possible to change the nature
ofinformation, such as through the “hardening of soft information.” For example, Reuters has made considerable effort to “harden” news information and make
itusable for practitioners and academics. Therefore, the classification applied here regard the hardness of news information rather than distinctly classifying
informationas purely “hard” or “soft.”
6Kyleet al. (2012) use Reuters news as a proxy for public information flow and relate it to the invariance hypothesis of trading games.
7Inthis context, Foucault et al. (2016) predict that stocks with higher news informativeness can be more liquid despite the attraction of informed HFTs.Other
studies on HFT and price discoveryshow positive effects of HFTs on price discovery and efficiency in general (see Brogaard, Hendershott, & Riordan, 2014;

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