Finance Project

Bard College
Contemporary Development in Finance
Final Project
High Frequency Trading, Price
Discovery and Regulations
Author:
M Ahnaf Khan
Supervisor:
Dimitri B. Papadimitriou
Taun Toay
December 19, 2014

CONTENTS 1
Contents
1 Introduction 3
2 Emergence of High Frequency Trading 5
3 Trading Strategies 6
3.1 Market Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Arbitrage Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3 Momentum ignition strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.4 Liquidity Detection Trading and Pinging . . . . . . . . . . . . . . . . . . . . 8
3.5 Dark pools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4 Price Discovery 9
4.1 Momentum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2 Liquidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.3 Systemic Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5 Regulations 12
5.1 Naked Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.2 Large Trader Reporting Rule . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.3 The Consolidated Audit Trail . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.4 New Circuit Breakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.5 MIDAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6 Transaction Tax 14

CONTENTS 2
7 Modiﬁed Transaction Tax 17
8 Conclusion 19
9 Bibliography 20
10 Appendix 22

1 INTRODUCTION 3
1 Introduction
On May 6, 2010 between 2 PM and 3 PM the Dow Jones Industrial average plunged 998.50
points or 9.2% and then recovered slowly for the remained of the day to finally close at a
3.2% below opening. The S&P 500 and Nasdaq also fell 3.2% and 3.4% respectively. Figure
1 shows the changes in stock price on May 6, 2010. This event is known as the Flash Crash,
and it was the second largest daily point change in the history of the DJIA. A large debate
persists about the role of High Frequency Trading firms in exacerbating the downfall caused
on this day.
High Frequency Trading (HFT) is a form of algorithmic trading that uses proprietary
trading strategies to trade large volumes shares in milliseconds . They use statistical and
econometric algorithms along with super computers to optimize their returns and maximize
their trading speeds. HFT firms make small margins on each share; however, because of
their speed they are able to make large number of transactions. The sheer volume of their
transactions, ranging in the millions, make up for the small margins they make on each.
They tend to not hold any assets on their portfolio overnight, this is done to minimize the
risk of overnight value changes. Instead they hold shares for very short periods; they buy
and sell shares in fractions of seconds in order to capture small arbitrages in the market.
Due the excess volumes of trades conducted by the HFT firms, market liquidity improves,
trading costs reduces and efficiency of the market increases.
Recent HFT trading firms went back into the spot light after Michael Lewis (2014)
criticized HFT firms for front running. Front running involves conducting a swift transaction
before a very large buy or sell order using nonpublic information. HFT firms are also known

1 INTRODUCTION 4
to create phantom liquidity in the market. This is done to placing very large order and almost
immediately cancelling those orders. HFT also have recently been known to participate in
two-tiered market. HFT firms pay other companies with dark pools great sums of money to
receive information about the large share of exchange transactions they are participating in.
Lewis argues that because of these practices by HFT firms other individual or institutional
investors are at a disadvantage.
Jones (2013) reflects that regulators have added regulations such as short term individ-
ual stock price limits and trading halts following the aftermath of the Flash Crash. He
acknowledges the importance of these regulations in preventing such incident from reoccur-
ring; however, he argues that regulators should not simply rely on these regulations and
market competition to prevent future crashes. The market would require more robust regu-
lations such as minimum order exposure times, security transaction taxes, order cancellation
fees, consolidated order-level audit trails.
Brogaard, Hendershott and Riordan (2014) argues that overall HFTs improve price dis-
cover in the market by increasing price efficiency by trading in direction of long term price
change and in opposite direction to short term price fluctuations. Based on this regulation
on HFT might affect the price discover in the market. Price discovery is the process of
determining the real market price of a security. Liquidity is a measure used to determine
the market value an asset will trade at in relation to its actual value. Barclay and Hender-
shott claim that high trading volume leads to more efficient price discovery in the market.
Therefore based on this theory, by trading in large volumes, HFT trading firms should have
positive effects on price discovery.
Other findings have found that HFTs do not actually improve the market. Zhang (2010)

2 EMERGENCE OF HIGH FREQUENCY TRADING 5
argues that high frequency trading disable a market from valuing an asset price based on
it fundamentals. He finds that due to the high volume transactions of HFT, HFTs tend to
overreact to news regarding fundamentals of a firm. Keynes (1936) uses a beauty contest
example to argue that rational investors dont always have an incentive to correct the market;
it is profitable to rational trader in the short run to trade on the same side of the market as
irrational traders do. Brunnermeier and Nagel (2007) discusses Keynes notion further and
show that even hedge funds with expert managers tend to ride bubbles.
This paper focuses on the price discovery issue associated with transactions performed
by HFT firms. It will start off discussing the emergence of HFT firms. It move to determine
whether or not HFT firms influence overall market price as theory dictates. It will discuss
how liquidity and systemic risk are involved with HFT. The paper will move to discuss a few
regulatory policies currently in place in the U.S and then propose a new regulation: financial
transaction tax. It will discuss how transaction tax would affect the transactions performed
by a HFT firms, their profitability and how these affects will roll over into volatility, liquidity
and price discovery in the market.
2 Emergence of High Frequency Trading
HFT is an form of computerized algorithmic trading that became more popular since the
early 2000. Other than technological growth, changes in regulations also played a crucial
role in the emergence of HFT firms. HFT was one of the results of the large technological
advancements that occurred in the financial markets. Trading in the market become faster
and more complex. This led to the birth of electronic communication networks (ECN).

3 TRADING STRATEGIES 6
This allowed market participants to trade outside of the exchanges. This form of trading
became extremly demanded after the Alternative Trading Systems (Reg. ATS) was passed
by the U.S. Securities and Exchange Commission. Another regulation that was important
was one that the SEC passed in 2000 which asked the stock market to no longer quote prices
in fractions, but instead quote prices in decimals. This made the smallest trade difference
drop from $0.0625 to $0.01. The smaller minimum trade difference led to increased liquidity
followed by an ability for firms to have capital gains from even smaller price fluctuations.
Firms decided to maximize on the small margins from these trades by trading is larger bulks.
The last important regulatory change that occurred was Regulation National Market System
( Reg. NMS). Reg. NMS implemented the Trade Through Rule, Access Rule, the Sub-Penny
Rule, and the Market Data Rule. This new regulation ensured that investors now received
better information about prices in the market and that they got the best price when buy
and sell orders are executed. Also, another important change that allowed HFT to generate
greater profits and become a strong force in the market was that the market exchanges such
as New York Stock Exchange allowed HFT firms faster access to market information given
a certain fee.
3 Trading Strategies
Before moving on discuss the effects of HFT firms on overall market liquidity it is important
to realize how HFT firms operate in the market. According to Shorter and Miller (2014) HFT
firms take part in various strategies including: market making, arbitrage trading, momentum
ignition strategies, and liquidity detection trading. Lewis also adds another form of trading

done in dark pools
3.1 Market Making
HFT firms simultaneously post limit order for both buy and sell. They place buy orders
as low as possible and sell order as high as possible. They constantly modify their orders
to adjust for market fluctuations and new information. By posting these limit orders they
provide liquidity to individuals or institutions who wish to trade immediately at market bid
or ask price. In doing so HFT firms act as market makers and often times they receive a
liquidity rebate for their participation.
3.2 Arbitrage Strategies
There are sometimes very small price differences that exist on the price of the same stock
traded at different exchanges. These price differences usually last for very short periods of
time. HFT firms are able to capitalize on such windows even if they remain open for fractions
of a second. Lewis refers to this as slow market arbitrage.
3.3 Momentum ignition strategies
HFT firms can sometime place a series of order on security either to stimulate its price to go
up or down. HFT firms then wait for other market participants to behave as rationally as
Keynes beauty contest participants and drive the price up or down even more. HFT firms
generate returns by staying getting into this race early and leaving early to ensure they are
not the greater fool.

3.4 Liquidity Detection Trading and Pinging
HFT firms tend to place small orders on large number of stocks. These orders are placed
in order to determine how quickly they are being realized. HFT uses complex algorithms
on this data to determine whether a large buy or sell order is being conducted by a large
institutional or individual investor. HFT firms then front run these large investors. In doing
so they move the market price in the direction that benefits them. Lewis calls this form of
front running strategy pinging.
3.5 Dark pools
Often times large investors tend to not want to trade in conventional exchanges in order
to reduce exposure or to not fall prey to predatory trading by HFT firms, instead they
conduct their business on unconventional exchanges known as dark pools. Lewis explains
that HFT firms pay dark pools to gain access to their transaction information. Dark pools
are mainly associated with large sum transactions. Gaining non-public information of these
transactions allow HFT firms to understand the future movements of the market and place
orders accordingly. If a large buy order is placed the HFT firm will place an equally large
buy order. The HFT firm will be able to front run the dark pool trade because of its
technological advantages and because orders tend to have a lag time while in the dark pool.
By front running the buy order, HFT firms increase the price of the stock in the market and
the large investor loses out. Once the large investors order is placed and the stock prices
moves up even more, the HFT firm experiences capital gain by selling the shares back out.

4 PRICE DISCOVERY 9
4 Price Discovery
Price Discovery is the method of using the supply and demand curve to determine the price
of a security. Price discovery is the ability of an investor to determine the correct price of a
security. In the stock market the deviations in the spread between the bid-ask price affects
price discovery. Traditionally it is expected that with high volume of trade, the markets will
tend to become more efficient and lower the spread between the bid and ask price making
price discovery easier and vice versa.
Brogaard, Hendershott and Riordan (2014) claim that HFT firms have the same beneficial
roles towards price discovery as theory claims. By trading in large volumes high frequency
trading firms lower the spread in bid-ask price. This may be the case but it should also
be understood that even though some empirical studies suggest that HFT improve price
discovery, one of the main drawbacks of trying to research effects of HFTs on the market is
that availability of data. Most of the empirical analysis on this topic are done using limited
data. This is mainly why they have such varying results: Zhang (2010) finds that HFT firms
have negative effects towards price discovery.
4.1 Momentum
Brogaard, Hendershott, and Riordan (2014) assume that HFT firms trade mainly against
transitory pricing errors and in the direction of permanent price change. A lot of times, HFT
firms trade in the direction of stock movement outside of its fundamentals and in doing so
they create price momentum and attract other momentum traders to the stock, a practice
that amplifies price swings and thus increases price volatility.

4 PRICE DISCOVERY 10
As mentioned before high frequency trading firms also perform momentum ignition strate-
gies whereby they raise the value of the stock to large speculative levels. A lot of times HFT
firms see greater value in riding the upstream of a speculative stock bubble than to arbitrage
on its price deviation. This is mainly because HFT firms trade on a very short run and
even if they know that stock prices are going to drop they are uncertain about when that
will occur. To short an overvalued stock that is increasing in value means that the HFT
firm would have to hold their position for a significant amount of time. For HFT firms,
who conduct large number of trades on small margins, longer trade times have very large
opportunity costs.
Brogaard, Hendershott, and Riordan (2014) use state space model to show that HTF
firms help make markets more efficient by changing the price of stocks back to its long term
permanent value. However, this presumably permanent value is already at a speculated high
price. Cambell and Shiller points out that stock prices are currently being traded at very
high prices. They determine this by showing that valuation ratios are at extreme values and
that they have mean reverting tendencies. They also showed that prices change is the main
driving force for the extreme valuation ratios. This means that price too will mean revert
very soon. Given that is the case the HTF firms are only able to move stock values to short
term highs and not long term permanent values based on fundamentals.
Thus even if HFT improve price discovery, its only for the short run. High frequency
trading firms trade large volumes which may lower the spread between bid and ask price.
However, the range of the spread is still outside the correct price that a long term investor
would receive from the market. This means that long term investors are being penalized for
their slow trading. Investors who constantly feel that they are being penalized in the market

4 PRICE DISCOVERY 11
will lose confidence in the market and move to other venues such as dark pool, or they may
switch entirely to bond market. Figure 2 shows that volume of transactions in the NYSE
have decline significantly in the time period between 2009 and 2014. This may or not be a
direct cause of loss of investor confidence due to HFT regardless regulators need to address
the issues prompted by HFT.
4.2 Liquidity
Liquidity a measure to determine the market value an asset will trade at in relation to its
actual value. Assets that are highly liquid trade at values close to its actual value; whereas,
assets that are illiquid trade at values lower than its actual value. Therefore based on
theory, by trading in large volumes, HFT trading firms tend to make securities more liquid.
By increasing the liquidity in the market HFT firms make price discovery easier and improve
volatility in the market.
Shoter and Miller (2014) claim that HFT trading have been largely accused for partic-
ipating in market manipulation by creating phantom liquidity. HFT firms create phantom
liquidity by positing order for the sole purpose of canceling them. Order cancellation is a
method of artificially altering the demand and supply of a security in order to drive the price
of security up or down. This can be done by for example placing a large sell order below the
bid price. This lowers the price of the stock and starts the drive the momentum down. This
order is then quickly cancelled to prevent it from being realized as prices drop. Once the
price change caused by order cancelling takes place the HFT firm executes its actual orders.
One key thing to note here is that by doing this process, HFT firms give the impression

5 REGULATIONS 12
that their actual transactions are opposite to transitory pricing errors and in the direction
of permanent price change. That may be the case but it was done by first inducing a layer
of volatility that did not exist.
4.3 Systemic Risk
HFT firms provide a large deal of liquidity to the market. The removal of this liquidity could
have drastic effects on the market including a large market crash. Kirilenko et. al. (2011)
discuss that the 2010 Flash Crash may not have been caused by HFT firms but the effects
of the crash was significantly increased by an algorithmic trade performed by HFT. The
trade involved taking out large amounts of liquidity from the market. Jones (2013) mentions
another such event occurred in 2012 by an error in algorithm by a HFT firm known as Knight
Capital Group. Due to an incorrect algorithm Knight suffered from a $440 million loss in
a matter of 45 minutes. Luckily the losses incurred by Knight did not spill over to other
parts of the market. Regulators should not just assume that since Knight bore the cost of
its mistake that losses by HFT firms will always be self-absorbed. This experience should
come as a warning that HFT firms can take on extremely large losses extremely fast. If
a number of HFT firms incur losses simultaneously they will reduce market liquidity by a
severe amount. Which in turn could affect the entire market.
5 Regulations
Shorter and Miller (2014) explain that following the events of the Flash Crash in 2010 and
the downfall of Knight in 2012, the SEC implemented a few regulations to provide investor

5 REGULATIONS 13
protection and to maintain fair, orderly and efficient markets. The regulations currently
being implemented are as follows:
5.1 Naked Access
In 2010, SEC prohibited HFT firms from receiving naked access to the exchanging. HFT
firms were able to gain special to the exchanges, which allowed to compete more effectively
in the market compared to other market participants. This process allowed the HFT firms
to trade a greater speeds and not have to go through the risk and capital requirement check
that normal broken have to go though.
5.2 Large Trader Reporting Rule
In 2011 the SEC implemented this rule, which ensure that all large traders would have to
undergo specific reporting requirement if they wish to continue to participate in the market.
They defined large trader as any individual or institution that trades more than 2 million
shares or $20 million on any specific day, or if they trade more than 20 million shares or
$200 million a month. This allowed the SEC to monitor the movements of large trades and
also determine fluctuations in liquidity at all times.
5.3 The Consolidated Audit Trail
Implemented in 2012 by the SEC, this rule forced all exchanges to keep record of all traders
and orders. This rule will allow the SEC access to data necessary after large market fluctu-
ations, use it to determine market movements before and after the events and gain better

6 TRANSACTION TAX 14
understanding about the cause of those fluctuations.
5.4 New Circuit Breakers
This new circuit breaker was set after the small glitch in Knights algorithm which caused
them to lose $440 million in a matter of minutes. This circuit breaker would prevent any
trade outside of a specific percentage range from the price of the stock from occurring.
The value of the percentage depends on the value of the stocks with some stocks having
percentage ranges of 5% while others have ranges of 75%.
5.5 MIDAS
The Market Information Data Analytics System was a trade monitoring system that allowed
the SEC to gain access to all trade information in exchanges and off exchanges. This includes
information about trade cancellation and trade execution times.
6 Transaction Tax
Other than the Naked Access regulation, which prevented HFT firms from getting special
access to the exchanges most of the other regulations implemented by the SEC where more
for the purpose of monitoring HFT firms. No proper actions have taken place to prevent
events such as the Flash Crash from reoccurring. Whereas, other countries such as Canada
already has some regulations in place. Germany placed regulations impose fees for firms that
perform excessive HFT. A few market observers have since criticized the SEC for not placing
greater regulations against HFT firms. One very highly discussed regulation for controlling

HFT is transaction tax.
Some critics suggest that the SEC should implement a transaction tax on HFT. The tax
would be in the form of a small percentage (eg. 0.2%) of the trade value. This would prevent
HFT trading firms from overtrading and force HFT firms to try and capture a greater margin
on each trade. This is because HFT firms will now only execute trades which they are certain
will provide them with returns above the transaction tax of each trade.
Transaction Tax would be advantageous towards long term value or growth investors
over short term speculative investors. To long term investors a transaction tax would be a
small fractional decrease in their return. However, for short term speculative investors or
HFT firms, these costs would make a large difference. The added focus towards long term
value over HFT firms investors would allow the price of stocks to be more dependent on their
fundamentals by forcing investors to pay less attention to short term momentums preventing
the creations of bubbles in the market. However by lowering the participation of HFT firms
the market may have problems with price discovery, liquidity and volatility.
Transaction tax is not a new concept, it existed before even in the U.S. and also in
other countries so we have instructive data to give us a better understanding of how the
implementation of such a policy will affect the market. Pomeranets and Weaver (2013)
analyzed the U.S. federal transaction tax law between 1914 and 1981 and its effect on the
volatility, liquidity.
Theory suggests that during the tax period of 1914 and 1981, taxation would reduce
the participation of speculative investors or noise traders (trader who do no base trades
on fundamental values of securities) and in doing so reduce the volatility in the market.
Pomeranets and Weavers (2013) on the other hand found that volatility actually increases

with increase in tax percentage. This suggests that the decreasing market participation
factor for rational and informed investor is greater than that of noise traders.
Transaction tax would also have a negative effect on stock market liquidity. This is
because a tax would only work towards increasing the bid-ask spread. Pomeranets (2012)
explains that bid-ask spreads compensates traders for three things, order-processing costs,
inventory risk and information risk. She suggests that a decline in volume of trade due to
transaction tax would increase the cost of order-processing components compensation. She
also claims that the inventory-risk components for liquidity providers would increase. She
also suggests that by reducing the number of noise traders in the market, transaction tax
increases information risk, which is the risk of dealing with a trader with more information
on the fundamentals of the asset. Pomeranets and Weavers (2013) ascertains the theory by
showing a positive correlation between tax percentage and bid-ask spread from 1914 and
1981. Figure 3 below shows this relationship.
The strong effects of transaction tax on liquidity and volatility suggests that an imple-
mentation of transaction tax may put pressure on HFT firms but it will have strong negative
effects on price discovery in the market. A transaction tax will lower the overall demand in
the market, since investors on average now have less incentive to participate in the market.
The supply of stocks in the market would also fall because less trades would takes place,
lowering investors access to stocks.

7 MODIFIED TRANSACTION TAX 17
7 Modified Transaction Tax
When transaction tax was used before all market participants were penalized. Short term
investors took on a larger hit; however, a large proportion of rational investors also reduced
their overall participation. Amihud and Mendelson (2003) explains that transaction tax
used before also lowered informed trading which may have been responsible for increasing
the bid-ask spread. If transaction tax is reused it should be used for targeting HFT firms
to prevent them for using their predatory strategies. Two possible solutions could be either
using an even smaller tax rate than one used before or by penalizing the HFT firm directly
for their misconduct.
When transaction taxes were used previously in the US the rates were 0.04% to 0.1%.
These rates may be very small but they are still large enough to affect investors incentive to
buy and sell stocks. Also when these taxes were used previously HFT firms, who perform
millions of high volume trade did not exist. If these taxes are to be used today they have
to significantly smaller (eg. 0.0025%)simple to ensure that HFT firms do not go out of
business. Also If transaction taxes are to be used for targeting HFT firms solely, these rates
can actually be made even smaller than that. The high volume trades of HFT firms will
amplify the minimal tax. A tax rate of this small magnitude would not drive the HFT
firm out of the market but it would still ensure that they take a few more measures before
participating in a trade. Another solution could be to simulate the new transaction tax
implemented in Italy. According to Shorter and Miller, Italy imposed a trade tax in the
financial market in 2013. He explains that the tax rate is 0.02% and the tax is only applied
to all transactions (including cancelled or changed orders) that over a 60% mark of a ratio.

7 MODIFIED TRANSACTION TAX 18
The ratio used in Italy is the number of changed and cancelled orders less than half a second
duration over the total number of transaction. The only type of firms that will be targeted by
this type of tax are HFT firms. This is because most other firms cannot or do not participate
in transactions in such high speeds on a regular basis. The main affect this type of tax would
have on high frequency firms is for them to reduce the number order cancellations or order
changes they perform. This will work towards reducing the amount of front running, pinging,
and phantom liquidity HFT firms provide to the market.

8 CONCLUSION 19
8 Conclusion
HFT firms have existed effectively since the early 2000. Since their existence they have
added a whole different dynamic towards the financial market. Their rapid trades have
allowed increase in liquidity, decrease in transaction costs and improved market efficient.
However, they have also participated in a large number of misconducts such as momentum
ignition strategies, dark pool front running, phantom liquidity and order cancellations. These
misconducts causes destabilization in the market. Prices are manipulated and move from
permanent levels based on fundamentals. High frequency trades also take advantage of
slower individual and other institutional investors. HFTs reduce the return other investors
should receive. This paper also a possible solution to the problems associated with HFT.
Transaction taxes will work towards lowering the misconducts of HFT firms but it may also
reduce their market participation which could have some negative effects in market liquidity,
and volatility. However, it should improve price discovery in the market because it will
reduce the amount of frontrunning and momentum investment in the market. By lowering
these forms of trading in the market, these forms of taxes would work towards allowing prices
of stocks to revert back to their long term permanent prices.

9 BIBLIOGRAPHY 20
9 Bibliography
• Barclay, M. J., and Terrence Hendershott. ”Price Discovery and Trading After Hours.”
Review of Financial Studies 16.4 (2003): 1041-073. Web.
• Baron, Matthew, Jonathan Brogaard, and Andrei A. Kirilenko. ”Risk and Return in
High Frequency Trading.” JEL-Working Paper (2014): n. pag. Web.
• ”BATS Exchange — Market Volume History.” BATS Exchange Market Volume His-
tory. N.p., n.d. Web. 10 Dec. 2014. < http : //www.batstrading.com/market data/market volumeh
• Berman, Gregg E. ”What Drives the Complexity and Speed of Our Markets?” SEC.gov.
U.S. Securities and Exchange Commission, n.d. Web. 10 Dec. 2014.
• Brogaard, Jonathan, Terrence Hendershott, and Ryan Riordan. ”High-Frequency
Trading and Price Discovery.” Review of Financial Studies (2014): 2268-306. Print.
• International Organization F Securities Commission, Technical Committee. ”Regula-
tory Issues Raised by the Impact of Technological Changes on Market Integrity and
Eﬃcience.” Consultation Report (2011): n. pag. Web.
• Jones, Charles M. ”What Do We Know About High Frequency Trading.” Working
Paper (2013): n. pag. SSRN. Web.
• Kirilenko, Andrei A., Albert S. Kyle, Mehrdad Samadi, and Tugkan Tuzun. ”The
Flash Crash: The Impact of High Frequency Trading on an Electronic Market.” JEL-
Working Paper (2011): n. pag. Print.

9 BIBLIOGRAPHY 21
• McGowan, Michael J. ”THE RISE OF COMPUTERIZED HIGH FREQUENCY TRAD-
ING: USE AND CONTROVERSY.” Duke Law and Technology Review 16 (2010): n.
pag.
• Pomeranets, Anna. ”Financial Transaction Taxes: International Experiences, Issues
and Feasibility.” Bank of Canada Review (2012): n. pag. Web.
• Shorter, Gary, and Rena S. Miller. ”High Frequency Trading: Background, Concerns
and Regulatory Development.” Congressional Research Service (2014): n. pag. Con-
gressional Research Service. Web.
• Pomeranets, Anna, and Daniel Weaver. ”Securities Transaction Taxes and Market
Quality.” Bank of Canada Working Paper 2011-26.
• Zhang, X. Frank. ”High- Frequency Trading, Stock Volatility, and Price Discovery.”
Yale University School of Management (2010).

10 APPENDIX 22
Table 1: Change in price of DJIA, S&P 500 and E-Mini S&P 500 on May 6, 2010 from 8:30
AM to 3:00 PM ( Kirilenko et al. 2011)
10 Appendix

10 APPENDIX 23
Figure 1: The ﬁgure shows the change in volume of trade that occurred in the NYSE from
2009 to 2014. The data for this ﬁgure was collected from www.batstrading.com
Figure 2: Shows the relationship of transaction tax percentage and bid ask spread in the
U.S. using data from 1914 and 1981. (Pomeranets and Weavers 2011).

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