Regulation and high-frequency trading:

Unknown unknowns

Andre Oviedo

August 25, 2016

Abstract

High-frequency trading presents a controversial field nowadays. Algo-

rithms used by traders who take advantage the speed of advanced com-

puters have been accussed of being the cause of periods of volatility never

seen before. Policymakers find themselves in a hard situation, being pres-

sured by more than one party to begin the regulation of these algorithms.

A broad number of market participants advocate reforms by the means of

taxation. This paper wraps up different points of view regarding impact

on the market and recommendations, finding that the complexity of the

environment does not allow academia to conclude in a concrete effect on

market behaviour.

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1 Introduction

Recent developments of financial markets have resulted in a new work framework

not only for market agents but for policy makers. The disruption in develop-

ment for the financial markets found an extraordinary shelter in recent day’s

technology and technicians (UK’s Gov Office, 2012). The development of new

IT infrastructure accompanied with deliberate intentions by regulators to im-

prove competition between markets gave birth to a new trading approach which

is, nowadays, challenging the speed of light. High-frequency trading (HFT)

presents another page in the market disruption book. The floor-filled stock ex-

change image that most people have in mind when thinking about trading is

rapidly disappearing, computers are taking over, physicians with PhD’s are be-

ing hired in job positions which were unthinkable in the last century. The race

for maximum profit has been slowly transitioning from who finds the arbitrage

opportunity to who can do it faster. HFT is no longer black magic for financial

agents. The markets have adopted this new framework and are moving towards

a new generation of trading on the top of this new techniques. Time has become

the most valuable asset as it translates into taking advantage of an opportunity

or not.

However, every disruption is accompanied with dangerous freedom. Algo-

rithmic trading sets the market behavior according to some given patterns that

sound reasonable among a competitive market. According to Morgan Stanley,

the 2012 level of computer-based trading amounts for 86% of the US market

(Demos, 2012), even though the number of firms that engage high speed com-

puter trading is still small and there is little information about their procedures

(Kirilenko & Lo, 2013). Even if these agents do not follow the same patterns

for the algorithms, most of them follow signals from statistical arbitrage. What

would happen if there is a false alarm in markets? Under this circumstances

speed becomes a problem. An scenario similar to this case is depicted in Donefer

(2010) using the example of United American Airline (UAL) and the six-year-

old news which broke headlines by mistake in 2008. Under only 12 minutes the

algorithms made UAL shareholders lose more than one billion dollars when the

stock price dropped from 12 USD to 3 USD before quickly recovering. More

recently, false news of a bomb which just exploded in the White House and in-

2

Figure 1: The Twitter flash crash (Source: CNNMoney)

jured the U.S. president emerged in the social network Twitter (Farrell, 2013).

This false news apparently went directly in to the HFT firms’ feed and plunged

the Dow Jones and S&P500 indexes (See Figure 1). Can we blame some fat

finger mistakes? Not anymore.

If there is a date that will not be forgotten about this situation it is May 6,

2010 . The algorithms which worked wonders for market liquidity suddenly van-

ished more than 1,000 points from the Dow Jones Index (CFTC & SEC, 2010),

losing more than 9% of its value after quickly recovering minutes afterwards -

again. This is the most common event to recall when discussing HFT implica-

tions: the so-named flash crash. Many politicians and analysts have different

opinions on the core of this problem: Is it the freedom? Is it the speed? Is it

the computers? Is it the programmers? Is it the greed? Agents who argued in

favor of HFT’s freedom explaining that this occurrence was an outlier viewed

themselves ashamed: October 15, 2014. This time it was the U.S. Treasury mar-

ket which experienced an abnormal volatility during a short period of time (US

Department of the Treasury, Board, & US Securities and Exchange Commision,

2015). Many questions arise. The goal of this discussion paper is to present

the framework in which HFT works, its recent issues and the recommendations

proposed to control the risk of future chaos scenarios.

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2 HFT and the financial markets

2.1 History

Algorithmic trading has been around for a long time. For this, the difference

between algorithmic trading (AT) and HFT must be clear. AT refers to the

broad number of techniques which utilize computer-driven programs to auto-

matically when and how to carry out a trade, based in conditions and external

information (Agarwal, 2012). Since 1990, when the introduced the Electronic

Communication Network (ECN), which allowed investors to trade securities di-

rectly outside of the exchange network, an enormous quantity of investment in

alternative trading systems has been held. According to (Puorro, 2006) and

(Agarwal, 2012) this introduction was only the first step into a new wave of

opportunities for institutions which adopted AT as a way to minimize human

effort. This platform was only the beginning to a set of rules passed by the SEC

after the new century. In 2005, the SEC introduced three new rules regarding

the national market system (NMS) in the U.S.(SEC, 2005):

1. Order Protection Rule, which reinforces the fundamental principle of ob-

taining the best price for investors when such price is represented by au-

tomated quotations that are immediately accessible.

2. Access Rule, which promotes fair and non-discriminatory access to quo-

tations displayed by NMS trading centers through a private linkage ap-

proach.

3. Sub-penny rule, which establishes a uniform quoting increment of no less

than one penny for quotations in NMS stocks equal to or greater than 1.00

USD.

These three new rules aimed to strengthen the market environment in the

U.S. bringing more transparency to the exchange market. The third rule stated

above is acknowledged as one of the more important for AT traders. Given

that four years before the U.S. stock exchanges begun using the decimal system

instead of the 1/6 USD as the smallest change in any security price, narrowing

spread across the market, ”led operators to develop systems able to exploit the

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new minimum range of possible profit, since the risk of this strategy is pro-

portionally decreased (1 cent per share) and opportunities of trading, although

lower expected value, increased” (Puorro, 2006). These changes in the market

rules has increased the advantages which companies can take from speeding-up

their trading. The growth in complexity of financial markets was accompanied

with the astonishing development in the IT sector. In 1967 Gordon Moore pre-

dicted that the transistors in a CPU will be doubled every two years and history

since has been according to this prediction, called Moore’s Law. The processing

power in computers is, nowadays, the biggest leverage on the financial market

and HFT development. As research in finance started to get more quantitative,

increased processing power has been required. For example, the basis of mod-

ern portfolio theory is the mean-variance asset allocation introduced by Harry

Markowitz analyzes covariance between assets in the portfolio. Would a 1980

computer win a race against a 2010 computer in calculating (in the least time)

the correct variance-covariance matrix of, for example, a whole index? These

challenges can be achieved with today’s computing power, which has feed the

HFT companies into a race for processing power and speed.

3 Characteristics of HFT

Taking advantage of faster processing speeds, there are different types of actors

in the HFT market:

• Proprietary firms, which uses private money and strategies commonly for

market-making via automatic buy and sell orders,

• Broker-dealer firms, the traditional firms which have an specialized area

for HFT,

• Hedge funds, focused on asset pricing inefficiencies in markets.

As stated in the first section, the quote of market that HFT has captured should

make us question whether the way markets behave has some human rationality

behind. The strategies behind the day to day activity of these firms focus

on simple thing: time. Time, in normal day to day, is measured in seconds.

Nowadays, the term which is most important for HFT firms is milliseconds

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or even microseconds. With the introduction of AT and computerized stock

exchanges, arbitrage opportunities are measured on how much its takes to place

the order. According to Puorro (2006), this speed can be separated into four

branches: emission, cancellation, execution and modification. HFT firms can

also be divided according to the type of strategies they follow1 (Donefer, 2010):

• Liquidity seekers, users of AT with usual large blocks to place in the

market, therefore slicing the big order into small orders. Their only goal

is to buy or sell as much as they require

• Automated market makers, firms which try to benefit from little spread

changes and maintain small inventories.

• Statistical Arbitrage Traders, users who rely mostly in time series analysis

to profit from quick market imbalances and irrationalities. This users

are similar to the Automated market makers, but take into account the

Efficient Market Theory (EMT).

• Rebate seekers, who look for double-side rebates (sell side and buy side)

profits for providing liquidity. This is possible due to the super high speed

of their transactions.

Baron, Brogaard, Hagstromer, and Kirilenko (2016) and Kirilenko and Lo

(2013) distinguish between liquidity-demanding firms (aggressive) and liquidity-

providing firms (passive). These two type of behavior provide different results

for the use of speed. The aggressive type of firms tend to race against the market

reaction to news before the others, being one step ahead. Meanwhile, passive

HFT firms tend to behave more calm. In terms of risk and return, aggressive

firms collect profits from predicting price movements in the long run (intra-day

prices) but loses money in the short run, while passive firms show opposite

results, receiving profits in the short run and losses in the long run (Hasbrouck

& Sofianos, 1993).

1More on this in the next subsection

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3.1 Strategies

As stated above, speed is the most valuable asset owned by HFT firms. In a

recent study, Scholtus and Dijk (2012) show that short intervals between order

execution is necessary for trading rules with positive performance. Among the

most used strategies by firms, there are three pillars for this activity: pricing

inefficiency, low-latency advantage and market power abuse.

Arbitrage opportunities of the simple case buy low sell high can be easily

exploited by firms with instant information about the market. Speed and news

feed are a core for price change predictions by HFT firms. A common proce-

dure used by HFT to extract news from live feeds (and the cause of the Twitter

flash crash) is the automated news reading to predict stock prices. According

to Brogaard, Hendershott, and Riordan (2014), HFT plays an important role

clearing deviations from efficient prices by placing orders in the opposite direc-

tion. These firms trade against transitory price changes and in the direction

of future permanent price changes. This is supported by Menkveld (2013) who

finds that in Chi-X and Euronext there is evidence of HFT pressure in short-

term prices, negative relationship in HFT positions throughout the day and a

positive relation of these positions for the overnight period.

Low-latency advantage can be defined as a race for the fastest transaction.

Communication infrastructure turns out to be on par with financial develop-

ment, with huge bandwidth and lighting-fast fiber connections. Baron et al.

(2016) finds that competition among firms for faster connections is logical be-

cause of the characteristics in this market. The HFT trading structure can

lead to a winner-takes-all ending, when the minimal difference in trading speed

means to capture the most profit. Their study showed evidence of an ”industry

dominated by a small number of increasingly-fast, liquidity- taking incumbents

with high and persistent returns”. Firms pay millions to have the lowest latency

and fastest connections due to the trading edge they acquire for minimum frac-

tions of seconds (Cartea & Penalva, 2011). This competition reached the point

where connection is no longer an issue but geography is. According to Puorro

(2006) and Agarwal (2012), a new practice by HFT firms to ensure minimum

speed is to co-locate the firm’s infrastructure the closest possible to the stock

exchange, buying properties next to these buildings or renting servers offered

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by co-location companies.

As stated in the first section, HFT accounts for more than half of the trading

volume in the US market. This share grants HFT firms the capacity to utilize

some strategies which abuse the nature of financial markets and stock exchanges.

Puorro (2006) and Cartea and Penalva (2011) present a brief summary of most

of the common strategies deployed by HFT firms:

• Liquidity providing. HFT firm acts as market-makers in most stock ex-

changes. This role allows for some analysis of the bid-ask spread before

accepting an order. As they get information quicker than most price-

acceptant agents, the high speed in which they work allows them to react

faster to high volatility scenarios.

• Rebate arbitrage. A combination of Liquidity providing and statistical ar-

bitrage. As stated in sections before, the new wave of alternative platforms

(ECNs) parallel to the big stock exchanges derived in a strong competi-

tion between them. Because of this, ECNs are able to offer negative fees

for liquidity providers, which encourages HFT firms to take part in this

service.

• Ignition momentum. Also known as spoofing. A very sophisticated strat-

egy used to abuse the liquidity providing role of HFT firms. The trader

sends large amounts of orders over a short period of time, igniting the

other AT trader algorithms because of a sudden change in the scenario’s

volatility. As the whole ATs (and other HFTs) react, the HFT firm cancels

a considerable amount of the initial orders, closing positions and taking

advantage of new market prices.

3.2 Impact on markets

A big part of the recent discussion regarding the positive and negative effects of

these firm’s activities in the market. Race for maximum profit seems to be con-

verging into a predatory environment where firms abuse of market imperfections.

The multiple scenarios where high volatility caused, for example, tremendous

losses in stock indexes have caused multiple eyes to be put on this almost non-

regulated activity. The effects on market behaviour has been broadly analysed

8

by different authors, approaching the issue in various ways. Some positive and

negative effects are described in Puorro (2006) and Agarwal (2012), such as in-

creased market liquidity, smaller bid-ask spreads, decreased transactional cost

and more efficient price discovery. On the other side, the negative effects ex-

plained by these authors mark the possibility of high volatility periods due to

the automatic nature of the strategies, increased barriers to new investors and

adverse selection.

At first sight, the most recognizable positive effect of HFT firms for the mar-

ket have been described in the previous section: liquidity providing. Cartea and

Penalva (2011) study the role of HFT firms as an added value to the market,

providing a quick way for equity holders to get a fair price and fast transactions.

Using a three period model and two types of agents (liquidity traders and mar-

ket makers), they show that HFT’s role as an intermediary increases the price

impact of liquidity traders, has no effect on market makers and increases price

volatility as well as trade volume. In their model, they conclude that the loses

are beard mostly by liquidity traders as the prices are affected by the pressure

HFT firms exercise over them.

Hoffmann (2014) analyses the market separating two agents: slow traders

and fast traders. The implications in terms of welfare are interesting: the speed

of HFT eliminates the necessity of caution limit ordering, because there is less

risk of being picked off by extreme changes, meanwhile slow traders have to

account for this risk, routing orders with less probability of execution. This,

as a whole, concludes as a reduction on total welfare, because slow traders lose

market power and are always worse off than in identical capabilities.

Another approach, more data intensive than the others, is followed by Chaboud,

Chiquoine, Hjalmarsson, and Vega (2014). They analyse the market of euro-

dollar, dollar-yen and euro-yen from 2003 to 2007 and the arbitrage opportu-

nities that arise if prices are not in harmony between exchange rate markets.

Using both structural and reduced form vector auto-regression (VAR), they try

to analyse the marginal effect of an hypothetical market without HFT firms.

They find that AT in general helps price discovery and that there is no con-

crete evidence on the higher volatility caused by the correlation in algorithms

behaviour across firms.

9

Hasbrouck and Saar (2013) contributes to the discussion by offering a mea-

sure for low-latency activity. They argue that market quality has improved

thanks to the high-speed traders but it is not safe to say that this practises

deteriorate its quality when there is stress in the market. In general, it is not

possible to assure that ”high-frequency traders contribute to a market failure”.

One interesting finding of their research is the importance of co-location in the

nowadays fragmented market. The reasoning behind this is the ”ultimate dis-

advantage” of these fragmented markets.

4 Regulation and future of HFT

The recent inquiries about the actual effect of HFT on markets’ stability. As

mentioned in the introduction section, the results of the called flash crashes in

recent times has raised the question whether the current regulatory framework

is adequate for the type of trading activities that reign in the market. There is

plenty of literature regarding the events of May 6, 2010 (CFTC & SEC, 2010)

(UK’s Gov Office, 2012) and a review of similar events is not in the scope of

this paper. What is most interesting, regarding these events, is the type of

recommendations that are proposed by academia.

The core problem of HFT regulation is the lack of measures for the high-

speed environment. Budish, Cramton, and Shim (2015) set the goal in one

specific problem of the market: continuous-time trading. What they propose

as a solution for this characteristic of the market is discrete-time trading. The

arms race which takes places due to this market design places a huge obstacle for

regulation. Specifically, they argue for a frequent batch auction type of market,

with fixed time frames where all orders that are received during this period are

treated as if they arrived at the same time (characteristic of a discrete-time

environment). Also, in the framework of their theoretical model, they find that

frequent batch auctions deletes the arms race between trading firms because

they decide not to pay for higher speeds but stay in a slower latency. As a

whole, the social welfare outcome is unsure because of the loss firms can incur

due to the delay in transactions, even though it is believed that this cost is

negligible.

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Figure 2: The optimal intermediate nodes

Hoffmann (2014) attacks the problem of speed arguing that this represents

negative externalities for slow traders. As mentioned in the last section, his

model concludes that the less time one firm can take to process an order the

more market power it has. This, combined with the problem mentioned in the

last paragraph, places a question mark on how to avoid this winner-takes-all

behaviour in traders. Hoffmann proposes a fee to order cancellations (one of

the strategies deployed by HFT firms) to reduce the profits high-speed traders

get from pushing their latency to the limit. The goal of this recommendation is

to change market’s equilibria from a clear profitable environment of high-speed

seekers, to a more calm, and regulable, market.

Pointing into the future of HFT, the most intriguing question is ”Where can

we imagine HFT in years to come?”. The race for speed is not only reaching

geographical boundaries but physical boundaries. An study from MIT (Wissner-

Gross & Freer, 2010) shows that HFT firms are beginning to challenge the speed

of light, where not even co-location can save the firms one or two milliseconds.

At the end of their research, they find that this relativistic statistical arbitrage

opportunities arise from these boundary: as we can not go beyond speed of

light, there are specific points in the globe where this limit should impose a

new standard. They find that, due to this restriction, there are points on Earth

where statistical arbitrage find its optimum subjected to the speed of light

constraint. Curiously, they find a large number of optimal points in between

the most important stock exchanges of North America and Europe 2.

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Bundling all of these recommendations we can see a similar pattern: there is

no concrete argument against HFT. But this does not mean that there should

not be regulatory changes for a better understanding of the market behaviour.

The large share of AT

5 Conclusion

As every new technology arises, the problem of analysing the concrete effects on

the environment are extremely improtant. Even more if we are talking about

financial markets. The interconnection of the markets around the globe was a

huge dilemma when the events of the financial crisis of 2008 arose. Financial

development is in an stable growth, levering on technological development. The

markets do not have the same rules as decades ago and the awareness of who

or what moves it should be part of the agenda in most big countries.

But, as showed, there is a problem of some sort of blindness. The big role-

players in the regulation of HFT are clueless about the certain behaviour of the

prominent market makers, as if they do not realize the market is moving not

in seconds but in milliseconds and even microseconds. The relevant literature

regarding this topic shows that, even taking this into account, it is too hard to

find a feasible reason to aggressively regulate the HFT environment. The are

positive and negative effects (as in everything) but no concrete evidence of a

constant correlation with high volatility scenarios. Even though it has been on

agenda for quite long time for regulators, empirical evidence shows that there

is no clear effect of bad behaviour of markets due to HFT firms.

Moreover, the literature suggests two important ways to tame this new tech-

nology. The most intriguing one is the suggestion to stop continuous-time trad-

ing as it gives a huge advantage to firms which take part of the first-takes-

everything arms race. A discrete-time trading environment should be a good

start point for further regulation, as it ought to exist. The current scene for

new traders is tremendously expensive, slow traders get punished for not being

lightning-fast when this should not happen.

This discussion paper’s goal was simply to wrap up the definition and mod-

ern recommendations for HFT regulation. The cited arguments use descriptive

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empirical data to retrospectively analysis when the assumption of the future will

mirror the past has been broadly proved as inaccurate, but efficient. Further re-

search should be accomplished with predictive dynamic models using machine

learning prototypes, playing with factors in a pseudo-market environment to

back-test different policy proposals without risking the market stability.

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