The increasing competition in HFT among hedge funds and other market participants will inevitably reduce alpha opportunities in developed markets and cause hedge funds to focus more on emerging markets that are less efficient.

1. Mark J. Finn – mark@finsight.net 1/7
The Effect of HFT on Markets:
Introduction:
The rise of computer technology and the internet has led to an explosion in “High Frequency Trading” (HFT).
Traditionally, market participants would place orders individually, or through a broker. There was little automation in
the process and it was very much dependent on the time it took a human to place and execute an order. The increasing
adoption of computer power to automate this process has resulted in a rapidly reduced order execution time. Although
not an exact application of Moore’s law (that transistor counts double every year), it is clear that reduction of execution
time is accelerating each year.1 A report by the SEC into the “Flash Crash” of May 6, 2010 found that the average speed
of execution for small immediately executable orders had fallen from 10.1 seconds in January 2005 to 0.7 seconds in
October 2009.2 This has ultimately led to HFT becoming a dominant influence on US capital markets, accounting for over
70% of dollar volume trade.3
This paper seeks to explore the impact of HFT on the market itself. There is a growing body of literature which has
analysed the effect of HFT on price discovery and volatility. Although it is broadly accepted that HFT results in greater
liquidity and market quality overall, there is contradictory evidence regarding its effect on volatility. Naturally, the
impact of this form of trading also has an impact on market participants, traders and hedge funds. It is contended that
the inevitable continuation and acceleration of HFT will reduce the opportunities for alpha generation in established
markets and cause hedge funds and other alpha seekers to focus more on emerging markets.
What is HFT?
Despite the rapid growth of HFT there is no clear, all-encompassing definition. Distinctions have been made between
algorithmic trading and HFT,4 although for the purposes of this paper, they are used interchangeably as they are both
computer driven.
Commonly agreed upon characteristics of HFT are that investments are “held for very short periods of time and typically
(but not necessarily) positions are not carried overnight.” Below are a number of definitions provided in academic and
regulatory literature:
 High frequency traders are professionals acting in a proprietary capacity and able to generate a large number of
trades per day - U.S. Securities and Exchange Commission (SEC)5
 HFT is a form of trading that leverages high-speed computing, high-speed communications, tick-by-tick data,
and technological advances to execute trades in as little as milliseconds. A typical objective of high frequency
traders is to identify and capture (small) price discrepancies present in the market. They do so with no human
intervention, using computers to automatically capture and read market data in real-time, transmit thousands
of order messages per second to an exchange, and execute, cancel, or replace orders based on new information
on prices or demand. High Frequency Trading – Methodologies and Market Impact6
 High-frequency trading firms deploy fully automated trading strategies across one or more asset classes which
identify and profit from short-term (e.g., intra-day) price regularities. HFT strategies try to earn small amounts
of money on each trade—often just a few basis points, and the small profits from individual trades are amplified
by high trading volume. The Effect of High-Frequency Trading on Stock Volatility and Price Discovery7
Hedge Funds & HFT
The origin of classical economics is that an invisible hand determines market prices and those market prices follow a
random walk.8 The underlying premise of this school of thought is that everyone reacts in the same way to market
events. As news events are typically random and unpredictable, it follows that market movements are also random and
thus unpredictable.9

2. Mark J. Finn – mark@finsight.net 2/7
HFT, however, assumes that market participants are not homogeneous and do not react to market events in the
same way. They have defined characteristics which influence their behaviour. Proponents of HFT claim that market
participants exhibit two key properties that ultimately results in heterogeneous pricing and trading activity. Firstly,
market participants have differing degrees of risk aversion and secondly, they have different time horizons for holding
investments. The second point is arguably the cornerstone of high frequency trading (as opposed to other algorithmic
trading that seeks to exploit fundamental pricing inefficiencies) which generates alpha by reacting to market events (i.e.
news) faster than other market participants.
Broadly, high frequency trading assumes that there are market participants that do not follow the market on an
intraday basis. The consequence is that there is a time lag between market events and the ability, or willingness, of the
longer term traders to react to the new information. This phenomenon allows hedge funds to profit by reacting quicker
to information, which essentially pre-empts trades from market participants that have a longer term trading horizon.
Hedge funds employing HFT techniques are essentially trying to identify the ‘visible hand’ that operates as a
consequence of differing characteristics of market participants. One of the most successful hedge funds that harness
HFT is Renaissance Technologies. Renaissance have been able to generate compound returns in excess of 30 per annum
for over 20 years by exploiting mathematical relationships that can be deduced from trading activity in liquid markets.10
As HFT techniques become more advanced, however, the lag times required for market participants to ‘digest’
information will become shorter and lead to a more ‘efficient’ market. The corollary of this, however, is that the ability
to generate alpha from HFT will require greater and greater investment which will ultimately reach a point where the
ability to generate alpha does not offset the investment required. This will inevitably lead to hedge funds targeting
emerging markets that are not as efficient.
Impact on Markets:
There is a growing body of academic literature on the effect of HFT on price discovery and volatility. Although there is a
general consensus that HFT leads to greater liquidity and market quality overall, its effects are not always clear. This
section of the paper canvasses the dominant views on the effects of HFT on price discovery and volatility. It also
provides a brief overview and analysis of the events surrounding the ‘Flash Crash’ of May 6, 2010.
Price Discovery:
Analyzing the effect of high frequency trading on price discovery in markets is inherently difficult because it requires the
comparison of an actual outcome with a hypothetical one. The general view however, is that HFT trading impounds
information faster than traditional trading, 11 which all else equal should lead to a more ‘efficient’ market. It also brings
liquidity to the market (evidenced through higher volumes and narrowed bid-ask spreads)12, which can allow other
market participants to more easily adjust their portfolios to reflect their fundamental views on the company’s
performance. HFT should therefore, reduce transaction costs and drive market prices to converge to their intrinsic
values.13
This analysis is, however, somewhat paradoxical as HFT is not necessarily driven by fundamentals; it is driven by
discrepancies in market participants and their reactions to news. Because of the indifference to fundamental value, HFT
can lead to a stock trading 400 million units at a price of $5 or $10. Trades occur irrespective of the fundamental value
of the stock, which obviously has a distorting effect on true price discovery. Terry Hendershott from the University of
California observed that “if you consider the actual price as having fundamental information plus noise, high frequency
data has no long-term fundamental information, but HFT can help get short-term information into prices faster.”14
An interesting consequence of HFT is that its increasing application will ultimately eliminate opportunities for alpha
generation. Naturally, this implies that it must have a positive effect on price discovery and improving market quality as
mispricings are quickly eliminated. As trading speeds approach a natural or physical limit (as trading can never be
instantaneous in the absolute sense), high frequency traders will no longer be able to exploit the different investment

3. Mark J. Finn – mark@finsight.net 3/7
(and reaction) profiles of market participants. This is why the application of HFT will inevitably shift toward emerging
markets where trading speeds have not reached a natural / physical limit.
Some commentators have argued that although HFT tends to reduce bid offer spreads, the interaction with large
fundamental investors could also create price momentum or reversal.15 If a large fundamental investor placed a trade
that had an effect on the market price, high frequency momentum traders could follow the investors position (or even
front-run) leading to a potential amplification of the initial trade and a deviation away from the theoretical long term
value. Despite the potential for distorting effects on price discovery, it is however, generally accepted that price
discovery is improved by competition and automation.16
Volatility
Perhaps as a result of the conflicting forces affecting price discovery, there appears to be no clear relationship between
HFT and volatility. Although there is a large body of academic research that suggests HFT does not increase volatility
(but actually decreases it), there are a number of recent papers and empirical examples to the contrary. The most
prominent empirical example that suggests a positive correlation between HFT and volatility was the ‘Flash Crash’ of
May 6, 2010. A survey conducted in the June 2010, showed that over 80 per cent of US retail advisors believed that the
crash was due to an “overreliance on computer systems and high-frequency trading”.17
Jonathan Brogaard is one academic who contends that HFT does not have a statistically positive effect on volatility. In
his paper “High Frequency Trading and Its Impact on Market Quality”18, Brogaard presents the results of a regression of
volatility against volume for 120 US stocks over five days with 10 second price point intervals. The results actually
suggested that there was a statistically significant negative relationship between the two when volatility is the
dependent variable (implying the HFT does not, in and of itself, cause greater volatility). To validate the results, he also
estimated what the price impact would have been if there were no HFT demanding or supplying liquidity. Based on his
estimates, only one stock’s volatility would not be reduced if HFT were not in the market (essentially meaning that the
other 119 would exhibit increased volatility if HFT were absent). From these results, Brogaard concluded that HFT leads
to “lower volatility” and that it plays “a very important role in price efficiency and the price discovery process”.
Interestingly, Brogaard also points out that high frequency traders make more money in volatile times, suggesting a
significantly positive correlation between volatility and volume when volume is the dependent variable. The problem of
endogeneity in the results, however, makes it difficult to accept the conclusion that HFT tends to reduce volatility.
The contrary (and most prevalent view empirically) is, however, that HFT increases volatility. In the paper “The Effect of
High-Frequency Trading on Stock Volatility and Price Discovery”19, Frank Zhang argues that there are at least three
reasons why the interaction between HFT and fundamental investors can lead to increased volatility.20 The three
reasons are outlined below:
1. High trading volume generated by HFT is not necessarily a reliable indicator of market liquidity, especially in
times of significant volatility. The automated execution of large orders by fundamental investors, which typically
use trading volume as the proxy for liquidity, could trigger excessive price movement, especially if the
automated program does not take prices into account.
2. HFT is often based on short-term statistical correlations among stock returns. A large number of unidirectional
trades can create price momentum and attract other momentum traders to the stock, a practice that amplifies
price swings and thus increases price volatility. Positive feedback investment strategies may therefore result in
excess volatility even in the presence of rational speculators
3. High frequency traders detect and front-run large orders by institutional investors, a practice that pushes the
stock price up (down) if institutional investors have large buy (sell) orders, thereby increasing stock price
volatility.

4. Mark J. Finn – mark@finsight.net 4/7
In a paper released on the 19th of August, 2011, Victor Marinez and Ioanid Rosu21 expressly acknowledged that
there is disagreement between market participants and academics on the effect of HFT on volatility. In addressing the
issue, they provided an alternative model of the phenomenon by assuming that high frequency traders are informed
traders and that they do not take directional bets. Rather, they act in response to a continuous stream of news that has
varying degrees of precision. They show (through the application of a mathematical model) that in the presence of news
HFTs generate “most of the volatility and trading volume in the market”. Where the degree of precision in news is
higher, the contribution of the informed HFTs is greater to volatility, largely because they do not take directional bets. In
a news rich world where HFTs typically only hold short positions, it is therefore expected that the presence of HFT will
tend to increase volatility. This effect is again a consequence of the co-existence of market participants who have
differing investment horizons.
Flash Crash
Perhaps the most apt case study of the potential effect HFT on volatility is the Flash Crash. On the 6th of May, 2010, US
stock market and futures indices experienced a sudden drop of around 5% in 30 minutes. As there was no clear
explanation at the time, many market participants, analysts and commentators concluded that it was driven by
automated HFT. On the 11th of August 2010, a number of high profile asset management companies and market
intermediaries suggested that the nature of electronic market place was such that the event could easily occur.
In the time that has passed since the crash, however, no conclusive answer has been provided for its cause, despite
significant research and investigation. After studying the events of the day, along with the subsequent SEC
investigations, one group of academics released a paper addressing the causes and the involvement of HFT.22 Their
conclusion was that although there was a larger than normal amount of HFT on the day, it was caused primarily by a
large fundamental selling program that did not have sufficient corresponding demand for prices to remain stable. They
argued that the HFTs most likely bought large portion of the initial stock from the fundamental sellers, but as they do
not typically hold inventory for that long, and trade on the markets direction, they sold in line with the broader market.
The reversal was also largely attributed to fundamental investors who formed the view that prices had fallen below
their intrinsic values. They concluded that “irrespective of technology, markets can become fragile when imbalances
arise as a result of large traders seeking to buy or sell quantities larger than intermediaries are willing to temporarily
hold.”23 Interestingly, their analysis of the actual events shows that it is not HFT that causes volatility in its own right,
but its potential interaction with differing (i.e. fundamental) investors.
Conclusion:
HFT now represents a significant portion of trading in US markets, as well as other developed markets around the
world. The rise of HFT is driven by a belief that markets are not perfectly efficient and that differences in market
participants, particularly in risk appetite and investment horizon, create opportunities to generate alpha. Hedge funds
have invested heavily in computer technology to gain a trading edge in responding to news and compete aggressively to
exploit. Despite their competitive short term edge, fundamental investors still make up a large portion of overall volume
and have a significant effect on the market. The co-existence of the two can lead to excessive volatility, as was evidence
in the Flash Crash. Although many market participants and commentators consider HFT to be the cause of excessive
volatility, the academic and empirical evidence is inconclusive. There are varying views of the effects of HFT on price
discovery and volatility, however, it is generally accepted that because markets are not purely efficient, circumstances
can arise that are conducive to extreme volatility. The increasing competition in HFT among hedge funds and other
market participants will inevitably reduce alpha opportunities in developed markets and cause hedge funds to focus
more on emerging markets that are less efficient.

5. Mark J. Finn – mark@finsight.net 5/7
Bibliography:
* The paper referred directly to the following academic papers. Specific references are provided in the endnotes.
1. Álvaro Cartea and José Penalva, “Where is the value in High Frequency Trading?”, Banco De Espana, 2011
2. Kirilenko, Kyle, Samadi, Tuzun, “The Flash Crash: The Impact of High Frequency Trading on an Electronic
Market”, 18 Jan 2011
3. X Zhang, “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Nov 2010
4. Olsen, “High frequency finance the hedge fund category of the future”, 2001.
5. Fabozzi, Focardi, Jonas, “High-Frequency Trading: Methodologies and Market Impact”, 2010
6. Jonathon Brogaard, “High Frequency Trading and its impact on market quality”, 16 Jul 2010
7. Victor Martinez and Ioanid Rosu, “High Frequency Traders, News and Volitlity”, 19 Aug 2011

6. Mark J. Finn – mark@finsight.net 6/7
APPENDIX:
* HFT Literature Review – June 2011 (provided for information purposes only)
Paper Data Set Key Findings
Angel, Harris, Spatt U.S. equities, 1993 – Trading costs have declined, bid‐ask spreads have
"Equity trading in the 21st 2009 narrowed and available liquidity has increased
century" - February 2010
RGM Advisors “Market Efficiency U.S. equities, Bid-ask spreads have narrowed, available liquidity has
and Microstructure Evolution in US 2006-2010 Increased and price efficiency has improved
Equity Markets: A High Frequency
Perspective” -
October 2010
Credit Suisse “Sizing Up US U.S. equities, Bid-ask spreads have narrowed, available liquidity has
Equity Microstructure” - April 2003-2010 increased and short term volatility has declined
2010
Saar Hasbrouck: Full NASDAQ order Low latency automated trading was associated with
‘Low-Latency Trading’ - book lower queated and effective spreads, lower volatility
May 2011 June 2007 to Oct 2008 and greater liquidity
Riordan Hendershott, Deutsche Automate trades made prices more efficient and did
‘Algorithmic Trading and Börse equities, Jan not contribute to higher volatility
Information’ – Aug 2009 2008
Brogaard "High HFT vs. other trades. HFT helped to narrow bid-ask spreads, improved price
Frequency trading and its impact U.S. equities on Nasdaq discovery and may have reduced volatility
on market quality" - 2008 – 2010
August 2009
Chaboud, Hjalmarsson, Vega and EBS forex market Automated trades increased liquidity and may have
Chiquoine, 2006-07 lowered volatility
‘Rise of the Machines: Algorithmic
Trading in the Foreign Exchange
Market’ -
Oct 2009
Hendershott, Riordan HFT vs. other trades. HFT trades were positively correlated with permanent
“High Frequency Trading and U.S. equities on price changes and negatively correlated with transitory
Price Discovery” (working paper) Nasdaq, various price changes, suggesting that HFT
periods in Improves price discovery
2008 – 2010
Jarnecic, Snape HFT vs. other trades. HFT improved liquidity and was unlikely to have
"An analysis of trades by high LSE equities, April - increased volatility
frequency participants on the June, 2009
London Stock Exchange".
June 2010

7. Mark J. Finn – mark@finsight.net 7/7
CME Group "Algorithmic trading Automated vs. other Automated trading was associated with improved
and market dynamics", trades. CME futures, liquidity and reduced volatility
July 2010 May 2008 - May
2010
Menkveld Dutch equities traded A single high frequency trader played an
“High Frequency Trading and the On Chi-X and Euronext, Important role in the development of a competitive
New-Market Makers”, 2007 market center, resulting in better liquidity and lower
April 2011 trading costs
Hendershott, Jones, Menkveld Automated quoting Automated trading narrowed bid--‐ask spreads,
“Does Algorithmic Trading Improve facility, NYSE equities, lowered trading costs, and improved price efficiency
Liquidity?”, February 2003
2011
Riordan, Storkenmairm Xetra high--‐speed Higher system speeds led to increased liquidity and
“Latency, Liquidity and Price trading system, improved price discovery
Discovery”, 2009 Deutsche Börse, 2007
Hendershott, Moulton NYSE TAQ database Introduction of automation via the NYSE hybrid system
“Automation, Speed and Stock plus others, June 1, improved price discovery and made prices more
Market Quality: The NYSE’s 2006 – May 31, 2007 efficient
Hybrid”, February 2010
Gomber, Arndt, Lutat, Uhle Various Survey paper that highlights beneficial aspects of HFT,
“High-Frequency Trading”, while noting that perceived problems are largely a
March 2011 result of U.S. market structure
REFERENCES:
1
Álvaro Cartea and José Penalva, “Where is the value in High Frequency Trading?”, Banco De Espana, 2011
2
Kirilenko, Kyle, Samadi, Tuzun, “The Flash Crash: The Impact of High Frequency Trading on an Electronic Market”, 18 Jan 2011
3
X Zhang, “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Nov 2010
4
Ibid.
5
Securities and Exchange Commision Papers, 17 CFR PARTS 240 and 249, 2011.
6
Fabozzi, Focardi, Jonas, “High-Frequency Trading: Methodologies and Market Impact”, 2010
7
X Zhang, “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Nov 2010
8
Olsen, “High frequency finance the hedge fund category of the future”, 2001.
9
Ibid.
10
Ibid.
11
Ibid.
12
X Zhang, “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Nov 2010
13
Ibid.
14
Fabozzi, Focardi, Jonas, “High-Frequency Trading: Methodologies and Market Impact”, 2010
15
X Zhang, “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Nov 2010
16
Literature Review (as provided in appendix).
17
Kirilenko, Kyle, Samadi, Tuzun, “The Flash Crash: The Impact of High Frequency Trading on an Electronic Market”, 18 Jan 2011
18
Jonathon Brogaard, “High Frequency Trading and its impact on market quality”, 16 Jul 2010
19
X Zhang, “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Nov 2010
20
Ibid.
21
Victor Martinez and Ioanid Rosu, “High Frequency Traders, News and Volitlity”, 19 Aug 2011
22
Kirilenko, Kyle, Samadi, Tuzun, “The Flash Crash: The Impact of High Frequency Trading on an Electronic Market”, 18 Jan 2011
23
Kirilenko, Kyle, Samadi, Tuzun, “The Flash Crash: The Impact of High Frequency Trading on an Electronic Market”, 18 Jan 2011