Front‐Running Scalping Strategies and Market Manipulation: Why Does High‐Frequency Trading Need Stricter Regulation?

AuthorViktor Manahov*
Date01 August 2016
Publication Date01 August 2016
DOIhttp://doi.org/10.1111/fire.12103
The Financial Review 51 (2016) 363–402
Front-Running Scalping Strategies and
Market Manipulation: Why Does
High-Frequency Trading Need Stricter
Regulation?
Viktor Manahov*
The University of York
Abstract
Regulators continue to debate whether high-frequency trading (HFT) is beneficial to
market quality. Using Strongly Typed Genetic Programming (STGP) trading algorithm, we
develop several artificial stock markets populated with HFT scalpers and strategic informed
traders. We simulate real-life trading in the millisecond time frame by applying STGP to
real-time and historical data from Apple, Exxon Mobil, and Google. We observe that HFT
scalpers front-run the order flow,resulting in damage to market quality and long-term investors.
To mitigate these negative implications, we propose batch auctions every 30 milliseconds of
trading.
Keywords: high-frequency trading, market regulation, market efficiency,algorithmic trading,
evolutionary algorithms, genetic programming
JEL Classifications: G10, G12, G14, G15, G18, G19, G20, G23, G28, G29
Corresponding author: Management School, The Universityof York, Heslington East, YorkYO10 5GD,
United Kingdom; Phone: +44 1 904 325847; Fax: +44 1 904 32502; E-mail: viktor.manahov@york.ac.uk.
I thank Richard Warr(the editor) and the three anonymous referees for their constructive comments, which
significantly improved the paper.
C2016 The Eastern Finance Association 363
364 V.Manahov/ The FinancialReview 51 (2016) 363–402
1. Introduction
Wehave witnessed a great transformation in recent years, from a world in which
humans traded face-to-face to one in which computers trade with other computers
(Angel, 2014). The use of computers in high-frequency trading (HFT) has increased
over time, resulting in computer algorithms that execute electronically targeted trad-
ing strategies at superhuman speed (Goldstein, Kumar and Graves, 2014). While the
blink of a human eye lasts 400 milliseconds the current trading speed races occur at
microseconds (millionths of a second) and even nanoseconds (billionths of a second).
Ding, Hanna and Hendershott (2014) suggest that the average time it takes to execute
a market order is approximately 300 microseconds. However, the apparent lack of
conclusive evidence has enabled HFT to operate with limited regulatory understand-
ing, forcing policy makers worldwide to debate whether HFT is beneficial or harmful
to market efficiency (Manahov, Hudson and Gebka, 2014).
Most empirical work lacks the ability to identify which trades and quotes come
from HFT making it difficult to examinehow HFT affects the market and other market
participants (Egginton, Van Ness and Van Ness, 2012; Hirschey, 2013; Goldstein,
Kumar and Graves, 2014). This is because no publicly available data set, including
NASDAQ 120, allows researchers to identify all HFT directly (Baron, Brogaard and
Kirilenko, 2012). Egginton, Van Ness and Van Ness (2012) and Goldstein, Kumar
and Graves (2014) argue that is hardly possible to identify orders generated by
HFT computer algorithms in the U.S. equities markets, and most previous studies
use proxies to measure the level of algorithmic trading and HFT.1In addition, the
huge number of variables and the very complicated cause-effect relation between
these variables and potential outcomes imposes further research obstacles (Felker,
Mazalov and Watt, 2014).
In contrast, this study uses a special adaptive form of Strongly Typed Genetic
Programming (STGP) and real-time and historical millisecond data from the three
most capitalized companies—Apple, Exxon Mobil, and Google. This demonstrates
how HFT scalpers step ahead of investororders, thus breaching the price-time priority
in trade execution and generating a cascade of canceled orders that lead to a deteri-
oration of market quality. Wah and Wellman (2013) argue that questions regarding
the implications of HFT are inherently computational in nature, because the speed
of trading indicates details of internal market activities and the structure of commu-
nication channels. The STGP, described in the Appendix, is a sophisticated trading
algorithm that is suitable for the successful replication of HFT scalping strategies
(the successful application of STGP and Genetic Programming [GP] has been shown
1Some of these proxies include the rate of electronic message traffic normalized by trading volume
(Hendershott, Jones and Menkveld, 2011; Viljoen, Westerholm and Zheng, 2014), the adoption of pro-
prietary data to identify HFT companies (Brogaard, Hendershott, Hunt and Ysusi, 2014) and the use of
account-level trade-by-trade data for identifying HFT (Baron, Brogaard and Kirilenko, 2012; Brogaard,
Hendershott and Riordan, 2013; Hendershott and Riordan, 2013).
V.Manahov/ The FinancialReview 51 (2016) 363–402 365
in several trading exercises).2Usually, financial markets are studied using various
econometric tests and mathematical models based on rational market participants
(representative rational agents). However, the empirical characteristics of financial
markets cannot be fully investigated by such statistical methods. LeBaron (2004)
suggests that large moves, excess kurtosis, and market crashes can only be explored
in a model where market participants and their trading strategies are able to adapt
and adjust over time. In reality, prices of all financial instruments are established by
a large diversity of boundedly rational market participants with different decision-
making rules, risk preferences and time horizons. The complex nature and dynamics
of these market participants and their price formation behavior require a simulation
method that comprises multiple heterogeneous traders and an artificial stock market.
Perhaps the greatest advantage of STGP comes from the ability to execute real-
time trading orders rapidly. When trading is deemed HFT, it involves the implemen-
tation of sophisticated trading algorithms for submitting and canceling orders rapidly
and frequently (Goldstein, Kumar and Graves, 2014). The availability of real-time
data enables forward testing (real-time simulation), where submitted trading orders
change the market depth, and these changes can be observed in real-time. This cannot
be simulated in backtesting with historical data because no econometric technique is
capable of recreating the market reaction to a market depth change. More importantly,
scalping strategies require forward testing with real-time data.3In addition, there is
always broker latency, such as exchange latency or Internet connection latency, with
constantly changing values (it can be 100 milliseconds in one particular moment of
time, and one millisecond five seconds later). Consequently, this process cannot be
simulated in backtesting with historical data by specifying a fixed latency time (e.g.,
100 milliseconds).4
2Dunis, Laws and Karathanasopolous (2013) note that GP models perform remarkably well even in
simple trading exercises. Potvin, Soriano and Valee (2004) use GP to generate short-term trading rules
on the stock markets. Between 1981 and 1995, Neely, Weller and Dittmar (1997) applied GP to six
exchangerates, reporting strong evidence of significant out-of-sample excess returns. Allen and Karjalainen
(1999) developed genetic algorithm to find positive excess returns in the out-of-sample test period of the
Standard & Poor’s (S&P) Composite Stock Index. Dempster and Jones (2001) applied GP to a set of U.S.
Dollar/British Pound spot foreign exchange tick data from 1994 to 1997 to find profitable trading rules
in the presence of realistic transaction costs. Kaboudan (2000) performed another trading exercise with
GP and six stocks to generate relatively high returns on investment.Manahov, Hudson and Hoque (2015)
applied STGP to several financialinstruments to provide new evidence of return predictability. El-Telbany
(2015) implemented GP to forecast Egyptian stock market returns and demonstrates the capability of GP
in generating accurate predictions.
3For instance, a scalping strategy with averageprofit per trade of one basis point (Strategy 1) will generate
fewer realistic results in backtesting with historical data than a buy and hold strategy with 500 basis
points per trade (Strategy 2). This is because one or two basis point variations between backtesting and
forward-testing will havelittle impact on Strategy 2, while the backtesting performance of Strategy 1 could
be completely different from real-time trading due to these variations.
4While broker latency has little impact on buy and hold trading, HFT requires a short holding time. For
example, the average holding time in HFT is several milliseconds (Strategy 1). Buy and hold trading
(Strategy 2) can have a holding time of hours or days. In Strategy 2, the latency factor is negligible;

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