High Frequency Trading: Economic Necessity or Threat to the Economy?

©2014 Textbook 44 Pages


In the last four decades, technological progress led to an electrification of stock trading systems. It was realized that the profitability of trading strategies could be increased by employing computer algorithms to trade autonomously. This led to the implementation of High Frequency Trading (HFT). Theoretically HFT should increase efficiency in financial markets but it seems that, at least under certain circumstances, it causes market instability. The aim of this paper is to discuss the effect of HFT on market quality and why HFT cannot be fully explained by the neoclassical theory of economics. Therefore, the controversial positions in literature will be presented and discussed. It is especially referred to the influence of HFT on liquidity, price discovery and volatility. Primarily, its negative effect on volatility seems to contravene the modern finance. Furthermore, in the course of this work it will be illustrated that, by employing strict regulation of financial markets, this negative impact cannot be reduced to a sufficient extent in order for HFT to be characterized as market optimizing, according to the neoclassical theory of economics.


Table Of Contents

1 Introduction
1.1 Problem
In the last four decades, technological progress led to an electrification of stock tra-
ding systems. Traders were enabled to place their orders, which were later pro-
cessed via electronic networks, with the help of computers. Soon they realized that
the profitability of trading strategies could be increased by employing computer algo-
rithms to trade autonomously, reducing time needed to analyze information, publish
quotes as well as trigger and process trades. This led to the implementation of Algo-
rithmic Trading (AT). High Frequency Trading (HFT) is a subset of AT, at which fi-
nancial instruments are traded by algorithms at very high speed.
The past has shown that negative developments on capital markets are intensified by
HFT. Andrei Kirilenko explains in his work "The Flash Crash: The Impact of High Fre-
quency Trading on an Electronic Market" that HFT did not trigger the Flash Crash but
intensified the volatility that resulted from the event. Also on the 19
of October 1987,
"Black Monday", the increasing computerization of stock trading processes led to a
significant price drop. As a consequence, the high and still growing market share of
HFT leads to an increase in risk that a simple correction turns into a serious drop in
prices causing market instability. Theoretically HFT should increase efficiency in fi-
nancial markets. However, due to the empirical observation mentioned above, it
seems that HFT takes effect the other way round. It seems that, at least under cer-
tain circumstances, HFT enlarges volatility. This cannot be explained by the econo-
mic neoclassical theory. This problem is discussed in a lot of literature in which se-
veral different approaches have been made to explain it.
1.2 Aim
The aim of this paper is to discuss why HFT cannot be fully explained by the neo-
classical theory of economics. Therefore, the controversial positions in literature will
be presented and discussed. Primarily, its negative influence on volatility seems to
contravene the modern finance. Furthermore, in the course of this work it will be illus-
trated that, by employing strict regulation of financial markets, this negative impact
cannot be reduced to a sufficient extent in order for HFT to be characterized as mar-
ket optimizing, according to the neoclassical theory of economics.

1.3 Research
As a result of this objective, the following research questions arise:
1) Is there research that perfectly describes the effect of HFT on capital mar-
kets or is it, based on a comparison of different researchers' findings, ne-
cessary to combine these findings in order to ensure a realistic description
of the matter?
2) Can the negative effect of HFT on market quality be reduced, by employing
strict regulation of financial markets, to a sufficient extent in order for HFT
to be explained by the neoclassical theory of economics?
1.4 Scientific
The scientific method is comprised of literature research. For this paper mainly wor-
king papers and professional articles have been used as references.
Structure of the paper
In the first part of this study, the basics of HFT are explained, with reference to its
history, the presence of HFT in current markets as well as its users and their strate-
The following chapter deals with the effect of HFT on capital markets and its capabi-
lity of optimizing financial markets according to the neoclassical theory of economics.
Findings on the effect of HFT on liquidity, price discovery and volatility in financial
markets are examined.
The importance of regulation and supervision of HFT as well as regulatory measures
and their influence on market quality are topics of the third section of this paper. Also,
the "Flash Crash of May 06, 2010", an incident impelling the regulation of HFT, is il-
lustrated in detail.
A conclusion is shown in the last part of the paper.

Fundamentals of HFT
History of HFT and its presence in current markets
Within the last 40 years, a drastic change in trading processes of securities and other
financial products has taken place. Technological development has led to an electrifi-
cation of trading systems, which extensively replaced physical trading floors (see
Gomber et al. 2011, p. 8). Lee defines ETSs as exchange-systems for financial ins-
truments, in which buyers and sellers are brought together (see Lee 1998, p. 282).
The first computer-assisted trading system ­ today it is known as NASDAQ ­ was put
into operation by the NASD in the USA, in 1971. Since then, market participants have
continuously aimed to improve trading processes (see Gomber et al. 2011, p. 8). To-
day almost all markets are electronic. According to Jain, the leading exchanges in
101 of 120 sample countries have electronic trading, 85 of them act fully electronical-
ly, without floor trading (see Jain 2005, p. 2965).
2.1.1 Algorithmic Trading (AT)
Electronic trading systems execute their trades with the help of computer algorithms.
These use direct market access in order to autonomously make investment deci-
sions, based on observed and analyzed real-time market information, submit orders
and manage them after submission without human intervention of any kind. AT can
be defined as the use of such means for trading (see Hendershott/Jones/Menkveld
2011, p. 1; see Gomber et al. 2011, p. 14).
The algorithms can be programmed in various ways, depending on the trading stra-
tegies the market participants aim to execute with the help of AT. Algorithms for AT
are generally characterized by holding periods of up to several weeks and months as
well as the aim to achieve a particular benchmark, among other things. Some strate-
gies, however, may require algorithms that have different features for their execution.
Such might be applicable to HFT, a subgroup of AT (see Gomber et al. 2011, p. 14).
2.1.2 Definition and characteristics of HFT
As with AT, there is not only one correct definition of HFT. Basically, algorithms used
by HFTs and those necessary for general AT, have common characteristics. Both are
used by professional traders to observe market data and initiate and manage trades
by using direct market access (see Gomber et al. 2011, p. 15). However, there are
certain differences. Unlike ATs, HFTs usually do several thousands of trades per day
(see Hasbrouck/Saar 2010, p. 1) on their own accounts (see Hasbrouck/Saar 2010,

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2011, HFT-activity amounted to 70% of the volume traded in securities in the US. In
the same year, 40% of the stock trades of Deutsche Börse AG were executed by
HFTs, and the trend is still growing. Eurex, one of the world's leading derivatives ex-
changes, is expecting a growth rate of 20% per year in the field of automated trading,
due to expected increases in the efficiency of arbitrage-trading, faster market-
making, rationalization and further automation of trading, among other things (see
Gomber 2011, p. 19).
Technology used for HFT
HFTs need a comprehensive IT-infrastructure in order to be able to execute their
trades within milliseconds or microseconds. The lower the time needed to receive
market information, make investment decisions and place an order at an exchange,
the more profitable is the system for its user. This is why HFTs strive to minimize this
so called latency period (see Hasbrouck/Saar 2010, p. 1). HFTs try to get the ad-
vantage over other traders by employing high performance supercomputers and ob-
taining information faster than others, for instance through direct connections of their
servers to exchanges (see Angel/Harris/Spatt 2010, p. 38). They continuously aim to
optimize their processes in terms of software as well as hardware.
2.2.1 Software for HFT
Depending on their strategies (see chapter "2.4 Strategies of HF-Traders"), HFTs use
different software, including standard trading programs available on the market as
well as individually programmed and customized software. Every piece of software
consists of a front-end, the user interface that shows the information the HFT needs,
and a back-end, the actual algorithm. Such algorithms are the core of the software
and a prerequisite for successful HFT, as they obtain and analyze market information
and make investment decisions based on these data. The fewer and simpler the re-
quired information is, the faster this process can be executed (see Gomolka 2011, p.
198). This results in lower latency and higher profitability of the system.
2.2.2 Hardware for HFT
Besides software, the quality of hardware has a big influence on an HFT's success.
Modern technology makes it possible for electronic signals to travel at the speed of
light (see Kumar et al. 2011, p. 4). According to Yan Ohayon, this fastest possible
way of transmitting electric signals actually slows the orders of HFTs down, due to
HFTs' enormous capacity in terms of speed which is created by the software (see

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trading itself. (2) Proprietary trading: A method of using strategies for the purpose of
pattern detection in order to generate profits from trading itself (see Hasbrouck/Saar
2010, p. 12).
As illustrated in chapter "2.1.2 Definition and characteristics of HFT", HFT is charac-
terized by the investment of the trader's own capital. As a consequence, agency
traders, such as investment banks, can only employ HFT to maximize the profits they
earn from their core business, the service they get paid for by their clients. Proprie-
tary trading firms, however, are meant to generate profits from the trading of financial
instruments, using their own capital (see Hasbrouck/Saar 2010, p. 12). These com-
panies represent a fraction of approximately 2% of the 20.000 hedge funds, mutual
funds and brokers operating in US-equities. Businesses like RGM usually do not em-
ploy more than 120 scientists and software engineers for the development of tech-
nology that automatically monitors market information and trades based on these
observed data. Doing that, without taking into account the real value of a company,
currency or commodity, this economic sector generates an annual profit of several
billion USD ­ USD 7.2 billion during the economic and financial crisis, in 2009 (see
Foroohar 2010). The most important of the strategies HFTs pursue, when trading
based on monitored and examined market data, are explained in chapter "2.4 Strate-
gies of HFTs" of this paper.
Strategies of HFTs
HFT itself is not a strategy. The strategies used by HFTs are often traditional and
well known to the market. What makes them so efficient is the use of modern tech-
nology to employ them. Depending on their business models, HFTs have various dif-
ferent strategies (see Gomber et al. 2011, p. 24). In the course of this chapter, three
well known and frequently used strategies are introduced: Liquidity Provision (Market
Making), Liquidity Detection and Arbitrage.
2.4.1 Liquidity Provision (Market Making)
By submitting buy- and sell-limit orders at the same time, HFTs provide liquidity to
market participants who want to trade. This way they create "artificial liquidity", liquidi-
ty that results from contractual obligations instead of trading interest (see Gom-
ber/Lutat 2007, p. 11). Such market makers usually profit from (1) buying at the bid
price and selling at the ask price and (2) receiving liquidity rebates from trading ven-
ues (see Gomber et al. 2011, p. 24).

Buying at a certain price and selling at a higher price, as described in (1), is an HFT-
strategy called "spread capturing". If this principle is followed in the majority of trades
executed by HFTs, it can contribute significantly to the revenues of these firms (see
Gomber et al. 2011, p. 26). (2) Outlines a strategy, employed by HFTs, that allows
them to generate revenues from trading fee discounts and liquidity rebates they re-
ceive from exchanges. Many trading venues charge lower fees and offer incentives
to liquidity providers while making trading more expensive for liquidity demanders
(see Gomber/Lutat 2007, pp. 10-12). They do that in order to attract more of such
passive traders (liquidity providers), and further to make it affordable to submit more
limit orders to always reflect the total market information. Thus they narrow the bid-
ask-spread (see Gomber et al. 2011, p. 26) and improve market quality. HFTs can,
but are not obliged to, pursue a market-making-strategy. However, such strategies
are frequently adopted by HFTs (see Gomber et al. 2011, p. 17), as they can be re-
lated to certain benefits, such as maker fees (see Authority for the Financial Markets
2010, p. 15).
2.4.2 Liquidity Detection
HFTs employing liquidity detection strategies use ultra-high speed technology in or-
der to analyze the market activity of other traders for the purpose of profiting from
information they have (see Gomber et al. 2011, p. 28). HFTs "sniff out" the market in
order to find out if a large order is placed. That would be an indicator that a market
participant has valuable information based on which he wants to trade. This can be
detected by simply posting an immediate-or-cancel order, priced at the bid-ask-
midpoint, in the market. If the order is executed, this might indicate that a large order
is posted (see Reynolds 2011, p. 24). The ability of trading much faster than other
market participants based on this data, gives HFTs an enormous advantage (see
Gomber et al. 2011, p. 28). For this reason, such strategies can be profitable for
HFTs, but they often are a matter of concern for institutional investors and other non-
HFTs. Jones (2013) describes this problem with the help of an example: If an institu-
tional investor is buying shares, HFTs might be able to deduce that from information
detected on the market. As a consequence, they can drive the price of these shares
up and sell them at a higher price, probably even to the institutional investor who ini-
tially posted the buy-order (see Jones 2013, p. 9). This means that the profit of HFTs
employing such strategies can be a loss for other investors. According to Brogaard
(2010), HFTs as a whole do not engage in such "anticipatory trading", but he also

states that, due to the complexity and large variety of HFT-strategies as well as their
influence on his approach to detect anticipatory trading, it cannot be concluded that
there is no anticipatory trading (Brogaard 2010, p. 22).
Arbitrage opportunities are market conditions in which market participants can ge-
nerate profit, almost without taking any kind of risk. The usage of ultra-high speed
technology makes it possible for HFTs to take such chances, as they often only last
for split seconds. Theoretically, the arbitrage strategies outlined in this chapter can
also be employed by non-HFTs, but due to the speed advantage of HFTs it is very
likely that they will get their trades filled first (see Gomber et al. 2011, p. 26).
Market Neutral Arbitrage ("Pairs Trading")
Companies employing a market neutral arbitrage strategy are interested in financial
instruments whose prices have historically correlated. They take advantage of situa-
tions, in which a spread between the two prices occurs. This means that, when the
spread is widening, they buy the relatively cheaper instrument while shorting the
higher priced. They expect the two prices to converge again and thus make a profit
by then liquidating the two positions (see Mori/Ziobrowski 2011, p. 409).
Cross Asset-, Cross Market- ETF-Arbitrage
According to the law of one price, two identical financial instruments that trade simul-
taneously in different markets ­ free of tariffs, transportation costs and other costs
related to trading ­ must trade for the same price in all these markets (Chami Batis-
ta/Borges da Silveira 2008, p. 3). Employers of cross market-arbitrage strategies take
advantage of pricing inefficiencies across markets. Profits are generated by buying
the financial instrument at the relatively cheaper price on one market and selling it at
the higher price on the other market, but only if the spread between the two prices is
higher than the total transaction fees (see Gomber et al. 2011, p. 28).
Cross asset-arbitrage follows a similar principle. Gomber et al. (2011) explain it with
the help of an example: If an option is overpriced relative to its asset, shorting the
option in combination with a long position in the underlying can be a promising stra-
tegy. The same method can be used in order to profit from inefficiencies between the
prices of an ETF and its underlying (see Gomber et al. 2011, p. 28).


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Financial Markets Regulation High Frequency Trading Market Quality Algorithmic Trading

Title: High Frequency Trading: Economic Necessity or Threat to the Economy?
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