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Roberto Merli, Ilaria Massa, Maria Claudia Lucc hetti High Fre quency Trad ing: Technolog y, Regulatio n and Ethical Is sues
Roberto Merli1, Ilaria Massa2, Maria Claudia Lucchetti1
Department of Business Studies, Roma Tre University, Italy
Management Department, “Sapienza” University of Rome, Italy
The significant development of IT enabled the employment of
algorithmic trading (thereafter AT) allowing market operators to have remote
access to a variety of trading venues not needing to be physically present. AT
involves the use of computer software operating on the basis of key parameters
in order to optimize trade execution through the reduction of buy-sell
decisions impacts (Chlistalla 2010). Investment firms or their clients can
automatically generate orders to trading platforms in response to market
changing due to relevant information identified through their key parameters.
Over the last years, the use of these algorithms for the ‘straightforward order
execution tasks’ has increased. High frequency trading (thereafter HFT) is AT
subcategory, and it is characterized by the ability to give effect to a large
amount of orders at very high speed, with a ‘round-trip’ of the execution time
input on the order of milliseconds. The aim is to benefit from market liquidity
imbalances or small pricing inefficiencies. HFT success critical aspects are
therefore related to the ability to dramatically reduce latency and taking
advantage from small temporal differences in price data transmission. Fast
market access by some participants entails intermediation costs reduction
(Biais & Foucault, 2014), though it is source of adverse selection. Firms
provided with HFT technology are able to adopt the best trading strategy as
they can have access to market data slightly before the other market
participants (Capgemini, 2014).
In 2011, the International Organization of Securities Commissions
(thereafter IOSCO) report highlighted HFT contribution to innovation and
improvement of market efficiency . However, the report emphasizes the
presence of the negative effects that technological developments may have on
market quality, such as excessive volatility of the processes or lack of
transparency. European Security and Market Authority (thereafter ESMA) has
issued guidelines that provide for disclosure requirements for both market
participants and trading platforms. There are needed policy interventions to
regulate latency time reduction services offered to market participants in order
to ensure fair access to markets, orderly conduct of trading, the efficient
execution of orders, market integrity and investor protection but above all
ensure the robustness and speed of operations of supervisors called to mitigate
negative effects, (Caivano et al. 2012). In redefining MiFID framework, EU
Commission intends to require operators who use HFT technology above a
certain threshold to comply with specific requirements such as bonds risk
management and capital requirements.
Figure 1. Reasons for using algorithms in trading
Source: Algorithmic Trading Survey, 2013
High Frequency Trading Characteristics
HFT algorithms are based on a third-generation intelligent logic able to
evaluate information about market variation and based on what draft their
trading strategy. Specifically, these algos consider market data acquired in real
time as input and as output trading decisions automatically started by entering,
editing or deleting a large number of orders placed per unit of time on different
trading venues (Kirilenko et al.2010; Gai et al. 2012). They are also
Trader Productivity
Reduced market
Execution consistency
Commission rates
Price improvement
Ease of use
Internal crossing
Match pre-trade
characterized by the production of high volumes of transactions due to low
profit margins per transaction.
Sudden changes in placed orders are used to adapt strategies even to
minor changes in the market (Hasbrouck & Saar 2012). This ability derives
from specific operational and technological characteristics. Operational
characteristics are (Fabozzi et al. 2009, SEC 2010 ):
Usage of sophisticated and high speed computer programs to purse a
number of different strategies, generating, rotting and exchanging orders;
Usage of individual data feeds from exchanges as well as collocated
servers in order to minimize network latency;
Maintenance of very short timeframes for establishing and liquidating
High daily portfolio turnover and a large number of order submissions that
are cancelled compared to executed trades;
Maintenance of overnight positions;
Enter into short-term position and end the trading day flat.
These operating characteristics are due to the use of specific
technologies that consist of (Capgemini, 2014):
Replacement of copper cables with optical fibers to reduce information
transmission speed
Bandwidth that allows you to transfer up to 10 gigabits of data per second
Field Programmable Gate Arrays (FPGAs), integrated circuits that
implement complex logical figures to reduce latency
Multi-core process, which consists in different processors working on a
single computer component and performing different tasks at the same
Co-located servers. Market participants and data vendors can lease racks
and place servers close to market platforms to reduce the physical distance
between trading servers and exchange servers. (Physical distance
reduction can also be achieved by central proximity hosting system which
differs from the co-location because spaces are provided by third parties).
Investments in technologies to reduce latency is the main entry barriers
for firms interested in doing business using HFT. Other relevant costs are
those for constantly updating algorithms. The system must be able to interpret
market orientations and to review strategies on the basis of changes in the
correlation among different factors such as price, interest rates and any events
that can significantly influence the market. Moreover, algorithms short utility
period requires permanent upgrade procedures. When HFT is used, it becomes
easily decipherable by competitors, causing loss of competitiveness for the
company that had originated it. Finally, updating algos allows the monitoring
and the correction of any errors or improprieties that might spoil the market.
Method of detection of HFT in markets
Currently, there are three categories of HFT firms: independent firms,
broker-dealers and hedge funds (Capgemini, 2014). However, it is extremely
difficult to identify those who resort to use HFT among all market participants
(IOSCO, 2011). Only a few countries developed strategies to tell apart HFTs
from other low speed algorithmic trading (Caivano et al., 2012). Estimated
current levels of HFT deployment is provided by the private sector.
Among the approaches currently used to identify high frequency traders
(thereafter HFTr) it possible to distinguish:
Direct Method: HFTr are identified by information provided by the
operators themselves who carry out proprietary high speed trading. This
method does not identify traders who do not use HFT primarily.
Indirect Method: HFTr identification is based on the presence of specific
operational criteria (e.g. low inventory at the end of the day, low variation in
inventory positions). However, depending on the criteria, there are likely to
keep out some HFTr.
Strategic Method: HFT strategies are identified through the analysis of a
large amount of data in order to pick up entry, modification and cancellation
flows of orders. This can lead to strategic approach disadvantages related to
the large amount of resources needed to carry out the collection and analysis
of data. Moreover, the present inability in identifying all HFT possibly
strategies could lead to the erroneous inclusion of operators that do not use
high speed systems, and vice versa.
HFT Strategies
High speed trading consist in technical tools usable in a wide range of
strategies. Very often, HFTr do not implement new trading strategies but they
implement strategies that have already been distributed in the market
employing fast computer technology (Angel & McCabe 2013; Biais &
Foucault 2014). This is the case of “market making strategy that involves
continuously posting passive orders on both sides of the order book in order
to offer liquidity to other market participants and earning the spread” (IOSCO
definition). Market making is useful as it aims to reduce bid/ask spread prices.
Increasing competition among market makers enables offering competitive
prices. Operators who can act quickly have great advantages over other
participants. Fast traders are able to capture more opportunities and to profit
before the spread is too much reduced (Foucault, Kadan & Kandel 2013).
HFTr due to their operating speed can place their proposal at the top of the
column of order (front-running), discouraging traditional market makers to
participate in the market and threatening to create temporary illiquidity
phenomena. This ‘predatory’ behavior has the predictable consequence of
market participants confidence erosion (Bhupathi 2010). Another strategy is
statistical arbitrage. Statistical arbitrage aims to make profit of short-term
price movements. Rather than to respect the historical prices trends, traders
simultaneously buy and sell securities for which the temporary movement of
prices is due to technical reasons. It might seem that the validity of this
strategy is based on the ability to discover mathematical relationships of
market prices, but success is not necessarily due to the development of a good
model. This is actually determined by the ability to perform the highest
number of transactions in terms of timing and cost efficiency. Statistical
arbitrage strategies involve many risks
Model risk: it is related to computer models defaults. It may happen that
an operative damage can influence other HFTr behavior. Because HFTr cross-
market operations, defaults can negatively impact the entire market.
Liquidity risk: it is involved when statistical arbitrage strategies are
implemented on low liquidity stocks where violent price fluctuations may
hinder the closing of positions at a loss.
Operational risk: the opening of a large number of transactions involves
a general operational risk that can spread its effects in extreme market
conditions or in case of malfunctioning of systems running.
HFT Effects
Theoretical and empirical contributions on HFT impacts on the financial
markets provide manifold results. The high dynamics of HFT entails
difficulties in isolating the effects that tightly depend on strategies in which
they are employed.
Main benefits of HFT use
Market efficiency is expressed by price capacity to reflect fast and
accurately market information. This is known as the ‘news reaction’
mechanism. Markets need to respond quickly to news, and for this reason,
market participants devote significant resources to information collecting and
analyzing processes. HFT make price updating in short-term possible,
improving market through all available information incorporation. Speed has
a key role in incorporation phase (Angel & McCabe 2013). In fact, the
capacity to quickly process information relevant for the market enables HFTr
to return to the market the necessary information for equilibrium price
formation, helping to speed up the entire adjustment process (Baron, Kirilenko
& Brogaard 2012). While the increased access to information promotes the
formation of the price, on the other hand the fear of offer devaluation due to
distort price formation can induce market participants to make use of dark
pools - electronic trading venues that do not display public quotes for stocks
(Rose, 2010). Another aspect for which the use of HFT can generate
ambiguous effects is liquidity. Some studies show that strategies in which
there is the use of HFT help to add liquidity to the market (Hendershott, Jones
& Menkveld 2011). HFTr search liquidity capacity is greater than other
operators, since expanding the capacity to store information and reducing
reaction times allows operators to take advantage of trading opportunities
before they vanish from the market (Biais Foucault & Moinas 2013).
Main disadvantages of HFT use
High speed trading benefits deriving should not overshadow risks that
occur to market efficiency and integrity. Because of its ability to negotiate
positions held for periods of time lasting only a few minutes, HFTr can affect
price fluctuations and volatility in the short term (Chakrabarty et al. 2014).
HFT effect on liquidity is ambiguous. Liquidity provided by HFTr
involves the overestimation of the effective one. This phenomena known as
‘ghost liquidity’ is due to the characteristic of disappearing at times when
market conditions are more turbulent (van Kervel 2012). This can happen if
HFTr place an order on different platforms to increase selling chances. If the
order is executed on a trading venue, ‘twins’ orders will be immediately
deleted from all other platforms on which they are present. Cancellations
result in a reduction of liquidity. High speed allows traders to send thousands
of orders in stock exchange, and then delete it immediately. This strategy is
called quote stuffing (Egginton et al. 2014). Defective HFT can act in
unexpected ways and lead to chain reactions that affect market liquidity in
very short time frame (IOSCO 2011). For example they can amplify
downturns, as it happened in Flash Crash of May 2010 (Menkveld & Yueshen
2013), (Figure 2).
Both in normal times and in market stress times, HFTr are not willing to
accumulate large positions and the attempt to rebalance their positions during
stress times it determines a subtraction of liquidity to the market and it
increases volatility. Another negative externality generated by HFTr is
adverse selection. HFT operations are based on a superior information system
which generates high costs (Jovanovic & Menkveld 2012). For this reason
traders could be pushed to prefer dark pools to avoid their strategies are caught
by HFTr. Displacement of traders in dark pools affects price discovery
process. Arguments opposed to those previously exposed argue that the
operating speed of HFTr would quick integrate market prices information
fostering price discovery. However, this can affect efficiency of decision-
making mechanisms of the market participants, (Biais et al. 2013).
Figure 2. The Dow Jones Industrial Average, May 6th, 2010
accessed April 2014
HFT and manipulation
Some HFT strategies can generate trading manipulations. The U.S.
Securities and Exchange Commission (SEC) highlighted the adoption of
momentum ignition strategies which consist in buy/sell orders submission to
lead to artificial price changes. In this way HFTr can efficiently change their
position, either selling at inflated price or buying at extremely low prices. This
practice undermines the ability to make predictions based on past order flow
(Biais & Foucault 2014). Another manipulative strategy consists in placing a
large number of orders in the market (quote stuffing). Quote stuffing may
affect slow traders market access. Instead, ‘smoking’ provides convenient
order submissions that will be then modified by placing less favorable terms
before slow traders attracted in the transaction can realized order changed
In case of ‘spoofing’ HFTr objective is to get the best buying price. To
pursue this aim, HFTr will submit sell orders in order to induce other investors
to believe that phase of decline started. Subsequently, high speed traders will
cancel orders before they are executed and they will enter buy orders, which
were previously affected by the pressure exerted on the supply side (Caivano
et al. 2012).
‘Wash sales’ procedures are fictitious sales designed to simulate a greater
trading activity than it is actually carried out in order to increase interest of
specific trading stocks. This practice has resulted in rising prices making
appear the market more liquid than it is in reality and increasing pressure on
the stock price (Angel & McCabe 2013).
Increased risk for market stability occurs furthermore when HFT
strategies are combined with ‘controversial tools’ (Bhupathi 2010). Flash
orders allow investors to have preview information on trade orders than other
market participants. This involves creating two-tier market and improper
disclosure of information(Rose 2010). ‘Naked access’ refers to the practice
put in place by market makers and broker dealers that enables their clients to
have direct access to their exchange servers using their Market Participant
Identifier (MPID) (Chakabatry et al., 2014). Naked access allows traders to
have direct access to the market without going through the pre-trade checking
systems to reduce latency.
Policy Issues
Recent changes in global market have lead European legislator to ensure
its proper functioning. In this broad view, HFT is one of the main issue. In
October 2011, EU Commission adopted a new Market Abuse Regulation
(MAR), where there are specified HFT strategies that are likely to constitute
a market abuse (COM (2011) 651). Instead, Markets in Financial Instruments
Directive (MiFID) (first draft in 2004 and revised in 2007) meets financial
intermediaries and investors requirements to conduct investment services
throughout the Community, providing for European regulatory framework
harmonization. According to the Directive, each member State must ensure
that investment firms execute orders to achieve the best results for their
clients. In addition, companies are required to comply customers instructions.
The directive has been revised several times in order to guarantee that the
financial system remains as safe as possible. Today a new legislation drafting
is in progress. Reforms will develop strict transparencyto [..ensure that dark
trading of shares and other equity instruments which undermine efficient and
fair price formation will no longer be allowed]. MiFID II will [.. ensure that
legislation will keep peace with technological developments ..] (EU
Commission Memo 14/15). Specific controls will be introduced to the AT
activities. In addition, firms that provide direct electronic access will be
required for the adoption of risk control systems to prevent practices that can
result in disorderly markets or market abuse.
Policy Responses
The possibility that HFT can produce negative externalities has sparked
debate on possible policies to minimize these effects. The following measures
highlight disadvantages and risks associated with their adoption.
Mandatory Notification of Algorithm: the aim is to mitigate software
malfunction risks. HFT organizations must notify authorities characteristics
of algos and systems for risk management used. However, notification
requirement has disadvantages in terms of costs associated with
communication of information because the high pace of change due to the
need to constantly adapt to changing market conditions.
Circuit Breaks: there are interruption of trading mechanisms. They are
used to facilitate the management of momentary orders imbalances that can
cause sudden price movements (Poirer 2012). Pausing market can be a good
way to allow market participants to recalibrate their strategies and to reset their
own algorithms parameters (IOSCO 2011). Risk associated with the use of
this tool is to slow down price discovery mechanisms. If traders are aware of
the threshold that triggers the interruption, they will begin trading activity
gradually approaching the threshold. This behavior will speed up threshold
achievement. It is therefore necessary to proceed by working with careful
Tick Size : A tick is the minimum level of price change that one tool can
cause and it depends on instrument characteristics. In literature there is not an
universally acknowledge method for determining optimal price tick (IOSCO
2011). Tick size reduction may encourage retail investors as it increases
competition, moreover it reduces spreads and it contracts trading costs.
However, when ticks are very small they are a great incentive for HFT firms
to submit orders that will be canceled before execution.
Minimum Order Exposure Time: this tool prevents cancellation of
submitted orders for a minimum period of time. This instrument aims to
mitigate quote stuffing and ghost liquidity effects (Jones 2013). Minimum
exposure time in order books can be differentiated on the basis of the
characteristics of the contingent market. An indirect effect of this tool consists
in reducing information flow that comes to trading platforms. That reduces
risks of technological problems related to co-management systems that
receive and process such data. Risk associated with the imposition of
minimum exposure time is an adverse effect on price formation. If market
participants are able to react to sudden events only after a certain time span,
that will impact relative price ability to incorporate new information.
Order to Trade Ratio: It imposes a maximum limit to order submissions
and executions.
Periodic Auctions: Periodic auctions are characterized by start time and
duration randomness. If this tool is scattered in trading phases they can
mitigate HFT competitive advantage in terms of speed. Negative effect of this
measure could consist in discouraging supply of liquidity to the market.
Controls to trading: The establishment of pre and post-trade minimum
requirements of market participants and the control of pre and post-trading of
their activities ensure platforms orderly functioning. ESMA has also proposed
the introduction of controls to prohibit unauthorized access to the trading
systems, imposing filters on prices and quantities (ESMA 2012).
HFT & Ethics
Finance could not exist without ethics. Delegating own asset
management requires trust. There are numerous ethical issues related to HFT
usage. Are HFTr imposing an unacceptable risk to the market? Is HFT usage
giving a fair advantage to its users or is it affecting fair market participation?
Is it right to impose limits on technology such as minimum orders exposure
time? Who is responsible for negative effects production?
In finance many ethical issue have been addressed by the legislation or
by the companies through self–regulation (Boatright 2014). Codes of conduct
conditions financial institutions governance processes intervening in areas not
governed by rules and obligations imposed by regulators and supervisory
authorities. UNESCO defined ‘good governance’ decision making and
decisions implementation processes developed in accordance with eight
parameters listed below (Figure 3), (Sheng 2009).
Figure 3. Characteristics of Good Governance
Source: UNESCO 2009
Ambiguity of market output production increases problems of ethical
behavior in finance. High level of uncertainty hinders perceptions of cause
and effect relationship between the adoption of a specific behavior and results
produced in training environment, (Davis, Kumiega & Van Vliet 2013). HFT
amplifies this problem due to rapid adoption of the trading strategies.
Universality of good governance principles allow them to be applied to HFT
firms and they can be used in corporate strategies in order to seek ethical goals
of fair behavior.
Accountability: HFT technologies and strategies require combined action
of traders, computer engineers and quantitative analysts. Cross disciplinary is
HFT strategy core. These three functional areas traditionally respond to
different ethical principles that lead them to analyze risks from their
professional perspective and from there deduce their priority scale. It is
necessary to develop a single ethical conscience that addresses company
policy based on contact points of various ethical codes. Competent internal
authority has to check compliance with these principles and it must be
accountable to the public.
Transparency: Firms should periodically produce reliable and clear
reports in order to inform stakeholders about their performances. They must
ensure a regular update on the state of the art of their procedures. In addition,
companies should keep detailed records of information about key decisions,
system properties, testing methodologies to enable competent authorities to
carry out their monitoring role (ESMA 2012).
Responsiveness: Prompt reaction to stakeholders needs is a key feature
of good governance. High speed trading takes place automatically in
milliseconds time frames. That does not allow real-time corrections. However,
algos are implicit ethical agents (Moor 2006), which can be programmed to
act in accordance with ethical principles (Anderson & Anderson, 2007).
Companies are then able to correct the system on the basis of their ethical
policy, of a constant monitoring of the interaction between the system and the
market, and the collection of information relating to the needs of their
Equitable and Inclusive: the main criticism leveled against HFT firms is
that they take unfair advantage because not all market participants can support
investments to develop algorithms. This advantage is likely to lead to a ‘crisis
of participation’ (Angel et al., 2013). Initiative of companies like Marketcetera
can solve this problem. Marketcetera is an open source project devoted to
democratizing access to high frequency trading (
HFT firms should ensure support to similar initiatives to contribute to
guarantee fair market access and to increased social gain resulting spreading
technology knowledge.
Effective and Efficient: Shareholders profit maximization is companies
main objective. In order to avoid that risks linked to this aim persecution could
provoke negative effects to the market, HFT firms must adopt testing system.
ESMA suggest that system of testing has to include performance
simulations/back testing or offline-testing within a trading platform testing
environment. By adapting testing methodology to the adopted HFT strategies,
companies would not only ensures the real efficiency of the system but also
they would be able to verify its compliance with regulatory framework.
Follows the rules of law: Boundaries of financial organizations actions
are well defined by regulatory framework and supervisory authorities
disposals. However, firms should not restrict themselves to compliance, they
should adopt a pro active behavior. Companies have to voluntary adopt
guidelines and best practices. They have to implement new standards and to
impede unethical behaviors of other market participants, (e.g. corruption,
financing of non-ethically correct companies/activities ).
Participatory: Firms must ensure compliance with shareholders rights,
encouraging their participation to decision meeting where they can exercise
their voting rights. Shareholders have to contribute in market strategies
development. They have also to monitor that strategies are implemented in
accordance with diligence and prudence principles. Companies must
contribute to the spread of a smart and prudent risk management culture
among internal (employees) and external (shareholders) stakeholders.
Consensus oriented: HFT can collect and analyze flurry data in a very
short time. That characteristic enables those algos to perform effective long-
term forecasts and thereby protect stakeholder interests and to operate in a
sustainable perspective.
HFT technological and operational characteristics make it difficult to
identified it in the market. However, its effects are noticeable and they can
affect market proper functioning. According to Kearns is possible to
distinguish between passive HFT and aggressive HFT (Kirilenko et al. 2010).
The presence of passive HFT in the market ensures all participants
contributing to efficient price formation and increased liquidity in the market.
Aggressive HFTr adopt predatory behaviours that manipulate markets,
eroding market participants trust.
Strategies and automated trading systems should plan their trading
decisions process basing it on fairness, prudence and diligence principles.
These principles are the basis of good governance. Adoption of quality
management systems able to guarantee the fulfillment of the eight strands of
good governance must become the real competitive advantage of HFT firms.
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A market should be the purest form of exchange with willing buyers and sellers, with perfect information, agreeing on a price for a stock or commodity. Unfortunately, there are sophisticated computer systems sometimes located in the very building that house the stock or commodity exchange servers, and those computers can execute millions of trades per second. Those computers are even allowed to secretly view available trades before the rest of the general public and sometimes those trades are even clandestinely made, without any broadcast of the buy or sell prices to others. What is clear is that far from being a pure form of exchange, today’s market is heavily skewed, with the average consumer being the loser.
In November 2011, the SEC completed the implementation of a ban on naked market access. In the US, this ban is a rare occurrence of a market-wide regulation that impacted high-frequency quoting and trading activity. We show that the ban resulted in a significant decline in quote submissions and trade executions, accompanied by a notable decline in trading costs. We also find that liquidity demanders became less informed, and short-term price efficiency declined. Using the ban as an instrument to examine the effect of quote intensity on market quality, we document that the type of quoting activity that was reduced by the ban is associated with higher execution costs.
This paper analyzes the May 6 2010 Flash Crash using public and proprietary trade data on E-mini (S&P500 future) and SPY (S&P500 ETF). Price cointegration broke down one minute before the E-mini halt and prices collapsed. A large seller, whose E-mini trading reportedly contributed to the crash, was relatively inactive in this period. Her net sells were only 4% of total E-mini net sells. Yet, their long-run price impact was 19 times higher. Most of it kicks in after 300 milliseconds when other traders suddenly aggressively sell. Further findings are (i) the large seller paid a disproportionately large ‘price pressure’, (ii) before the halt, she did not find ‘fundamental buyers’ in the E-mini market, after the halt she did (perhaps necessarily so due to broken arbitrage), and (iii) she sold more aggressively when bid depth was large, after positive midquote returns, and the more she fell short of a 9% volume target.
This paper reviews recent theoretical and empirical research on high-frequency trading (HFT). Economic theory identifies several ways that HFT could affect liquidity. The main positive is that HFT can intermediate trades at lower cost. However, HFT speed could disadvantage other investors, and the resulting adverse selection could reduce market quality.Over the past decade, HFT has increased sharply, and liquidity has steadily improved. But correlation is not necessarily causation. Empirically, the challenge is to measure the incremental effect of HFT beyond other changes in equity markets. The best papers for this purpose isolate market structure changes that facilitate HFT. Virtually every time a market structure change results in more HFT, liquidity and market quality have improved because liquidity suppliers are better able to adjust their quotes in response to new information.Does HFT make markets more fragile? In the May 6, 2010 Flash Crash, for example, HFT initially stabilized prices but were eventually overwhelmed, and in liquidating their positions, HFT exacerbated the downturn. This appears to be a generic feature of equity markets: similar events have occurred in manual markets, even with affirmative market-maker obligations. Well-crafted individual stock price limits and trading halts have been introduced since. Similarly, kill switches are a sensible response to the Knight trading episode.Many of the regulatory issues associated with HFT are the same issues that arose in more manual markets. Now regulators in the US are appropriately relying on competition to minimize abuses. Other regulation is appropriate if there are market failures. For instance, consolidated order-level audit trails are key to robust enforcement. If excessive messages impose negative externalities on others, fees are appropriate. But a message tax may act like a transaction tax, reducing share prices, increasing volatility, and worsening liquidity. Minimum order exposure times would also severely discourage liquidity provision.
We examine the profitability of a specific class of intermediaries, high frequency traders (HFTs). Using transaction level data with user identifications, we find that high frequency trading (HFT) is highly profitable: 31 HFTs earn over $33 million in trading profits in one E-mini S&P 500 futures contract during one month. The profits of HFTs are mainly derived from fundamental (institutional) and small (retail) traders, but not from non-HFT market makers. While HFTs bear some risk, they generate an unusually high average Sharpe ratio of 10.2. These results provide insight in to the efficiency of markets at high-frequency time scales and raise the question of why we don’t see more competition among HFTs.
We show that two exogenous technology shocks that increase the speed of trading from microseconds to nanoseconds do not lead to improvements on quoted spread, effective spread, trading volume and variance ratio. However, there is a dramatic increase in the cancellation/execution ratio from 26:1 to 32:1, an increase in short term volatility and a decrease of market depth. We find evidence consistent with “quote stuffing,” which involves submitting an extraordinarily large number of orders followed by immediate cancellation in order to generate order congestion. The stock data are handled by six independent channels in the NASDAQ based on alphabetic order of ticker symbols. We detect abnormally high levels of co-movement of message flows for stocks in the same channel using factor regression, a discontinuity test and diff-in-diff test. Our results suggest that an arms race in speed at the sub-millisecond level is a positional game in which a trader’s pay-off depends on her speed relative to other traders. Private benefit then leads to offsetting investments on speed, or effort to slow down other traders or the exchange with no observed social benefit.
All of finance is now automated, most notably high frequency trading. This paper examines the ethical implications of this fact. As automation is an interdisciplinary endeavor, we argue that the interfaces between the respective disciplines can lead to conflicting ethical perspectives; we also argue that existing disciplinary standards do not pay enough attention to the ethical problems automation generates. Conflicting perspectives undermine the protection those who rely on trading should have. Ethics in finance can be expanded to include organizational and industry-wide responsibilities to external market participants and society. As a starting point, quality management techniques can provide a foundation for a new cross-disciplinary ethical standard in the age of automation.
Algorithms enable investors to locate trading opportunities, which raises gains from trade. Algorithmic traders can also process information on stock values before slow traders, which generates adverse selection. We model trading in this context and show that, for a given level of algorithmic trading, multiple equilibria can arise, some of which generate market exclusion for slow traders and sharp increases in the price impact of trades. We offer a theoretical interpretation for the "flash-crash" of may 2010. Next, we analyze the equilibrium level of investment in algorithmic trading. Because when others become fast it increases adverse selection costs for slow investors, algo-trading generates negative externalities. Therefore the equilibrium level of algo-trading exceeds its utilitarian welfare maximizing counterpart. Furthermore, since it involves fixed costs, investment in algorithmic trading is more protable for large institutions than for small ones. This generates equilibrium informational asymmetries between large fast traders and small slow traders.
The Flash Crash, a brief period of extreme market volatility on May 6, 2010 raised questions about the current structure of the U.S. financial markets. We use audit-trail data to describe the structure of the E-mini S&P 500 stock index futures market on May 6. We ask three questions. How did High Frequency Traders (HFTs) trade on May 6? What may have triggered the Flash Crash? What role did HFTs play in the Flash Crash? We conclude that HFTs did not trigger the Flash Crash, but their responses to the unusually large selling pressure on that day exacerbated market volatility.