High-frequency trading involves using computer algorithms to analyze market data and make trades within microseconds. It has grown significantly since the 2000s due to advances in technology that allow for extremely low latency trading. In 2010, the Tokyo Stock Exchange introduced a new system that reduced latency to 2 milliseconds, enabling high-frequency traders to dominate trading, executing over 1,000 orders per second and accounting for 72% of trades. While high-frequency trading provides benefits like increased liquidity and narrower spreads, it is also criticized for strategies like front-running that see order flows and trade based on that information.

1. High-Frequency TradingHigh-Frequency Trading
and 2010 Flash Crashand 2010 Flash Crash
Yoshiharu SatoYoshiharu Sato
University of Warsaw, 2015University of Warsaw, 2015
(https://sites.google.com/site/yoshi2233/)(https://sites.google.com/site/yoshi2233/)

2. What Is High-Frequency Trading?What Is High-Frequency Trading?
・・ A type of algorithmic trading technology that analyzes marketA type of algorithmic trading technology that analyzes market
data and transacts high volumes of trades at very high speedsdata and transacts high volumes of trades at very high speeds
(usually in microseconds, even in nanoseconds)(usually in microseconds, even in nanoseconds)
・・ HFTs use computer algorithms to arbitrage away the mostHFTs use computer algorithms to arbitrage away the most
infinitesimal price discrepancies that only exist over the mostinfinitesimal price discrepancies that only exist over the most
infinitesimal time horizonsinfinitesimal time horizons
・・ HFTs invest heavily to keep their technology at the forefront,HFTs invest heavily to keep their technology at the forefront,
and co-locate their servers at exchanges / trading venuesand co-locate their servers at exchanges / trading venues
to minimize the latency of their market connectionsto minimize the latency of their market connections
・・ HFT strategies involve extremely short holding periods andHFT strategies involve extremely short holding periods and
high turnover, with positions rarely held overnighthigh turnover, with positions rarely held overnight

3. History of HFT – Need for SpeedHistory of HFT – Need for Speed
・・ 1815: Rothschilds front-ran competitors by using courier1815: Rothschilds front-ran competitors by using courier
pigeons to relay news of Napoleon's defeat at Waterloopigeons to relay news of Napoleon's defeat at Waterloo
・・ 1892: Bell established the first NY to Chicago telephone1892: Bell established the first NY to Chicago telephone
・・ 1976: Introduction of NYSE DOT (the first electronic order1976: Introduction of NYSE DOT (the first electronic order
routing system)routing system)
・・ 1983: Bloomberg launched the first computerized system1983: Bloomberg launched the first computerized system
to provide real-time market data and financial analyticsto provide real-time market data and financial analytics
・・ 1998: SEC introduced Reg ATS1998: SEC introduced Reg ATS

4. History of HFT – Need for SpeedHistory of HFT – Need for Speed
(cont.)(cont.)
・・ 2005: HFT made up 13% of equity trades in the US2005: HFT made up 13% of equity trades in the US
・・ 2007: SEC introduced Reg NMS2007: SEC introduced Reg NMS
・・ 2009: HFT accounted for 61% of all US equity volumes2009: HFT accounted for 61% of all US equity volumes
・・ 2011: Fixnetix developed a microchip that is capable of2011: Fixnetix developed a microchip that is capable of
executing trades in nanosecondsexecuting trades in nanoseconds
・・ 2013: Laser beams and Microwave dishes are the latest2013: Laser beams and Microwave dishes are the latest
technologies to shave milliseconds off dealing timestechnologies to shave milliseconds off dealing times

5. Tokyo Stock Exchange – Case StudyTokyo Stock Exchange – Case Study

6. TSE & HFTTSE & HFT
・・ TSE introduced a new exchange system named 'arrowhead'TSE introduced a new exchange system named 'arrowhead'
in 2010, offering ULLDMA (Ultra-Low Latency Direct Marketin 2010, offering ULLDMA (Ultra-Low Latency Direct Market
Access) to HFTsAccess) to HFTs
・・ Development started in 2007 by 500 personnel (Fujitsu)Development started in 2007 by 500 personnel (Fujitsu)
based on 4,000 pages of system requirementbased on 4,000 pages of system requirement
・・ Software development based on “V-model with feedbacks”Software development based on “V-model with feedbacks”
・・ More than 200 servers connected via high-speed networks,More than 200 servers connected via high-speed networks,
each server using IMDB (In-Memory Database)each server using IMDB (In-Memory Database)
・・ Significant reduction in latency: Order now executed withinSignificant reduction in latency: Order now executed within
2 milliseconds and new price disseminated also in 2 ms2 milliseconds and new price disseminated also in 2 ms

7. TSE & HFT (cont.)TSE & HFT (cont.)
・・ Trade volume shares of HFTs increased from 10% in 2010Trade volume shares of HFTs increased from 10% in 2010
to 72% (270 trillion yen or $2.3 trillion) in 2014to 72% (270 trillion yen or $2.3 trillion) in 2014
・・ HFTs now trade $9 billion a day at TSE with max 1,000HFTs now trade $9 billion a day at TSE with max 1,000
orders a secondorders a second
・・ 8,000+ human dealers lost their job over the past 5 years8,000+ human dealers lost their job over the past 5 years
due to HFTs (due to HFTs (Japan Securities Dealers Association)Japan Securities Dealers Association)
・・ TSE charges a small fee on every stock order, whether it’sTSE charges a small fee on every stock order, whether it’s
executed or not, to curtail 'spoofing' by HFTsexecuted or not, to curtail 'spoofing' by HFTs

8. Who Are HFTs?Who Are HFTs?
・・ HFT was first made successful by an American hedge fundHFT was first made successful by an American hedge fund
Renaissance Technologies (a group of math PhD's)Renaissance Technologies (a group of math PhD's)
・・ Virtu Financial made money on 1,484 of 1,485 trading daysVirtu Financial made money on 1,484 of 1,485 trading days
(99.93%!) from 2009 to 2014, and made money EVERY(99.93%!) from 2009 to 2014, and made money EVERY
trading day in 2014, generating profit of $190 million fromtrading day in 2014, generating profit of $190 million from
revenue of $723 millionrevenue of $723 million
・・ Intense competition and arms race in the industry:Intense competition and arms race in the industry:
KCG (GETCO), Jump Trading, Citadel, Tradebot Systems,KCG (GETCO), Jump Trading, Citadel, Tradebot Systems,
Tower Research (Spire Europe), Global Trading Systems,Tower Research (Spire Europe), Global Trading Systems,
Hudson River Trading, Optiver, IMC Trading, Flow Traders,Hudson River Trading, Optiver, IMC Trading, Flow Traders,
Two Sigma Investments, etc, etcTwo Sigma Investments, etc, etc

9. Virtu FinancialVirtu Financial
From January 2009 to December 2014,From January 2009 to December 2014,
Virtu had only one overall losing trading day [1]Virtu had only one overall losing trading day [1]

10. Is HFT a Bad Thing?Is HFT a Bad Thing?
・・ HFT increases liquidity,HFT increases liquidity,
narrows the spreads,narrows the spreads,
and lowers the tick sizes,and lowers the tick sizes,
all of which are beneficial toall of which are beneficial to
every market participantevery market participant
from small retail tradersfrom small retail traders
to large institutional tradersto large institutional traders
・・ Has been criticized for front-running andHas been criticized for front-running and flash tradingflash trading
(viewing orders from other market participants fractions of(viewing orders from other market participants fractions of
a second, typically 30 milliseconds, before others do)a second, typically 30 milliseconds, before others do)
・・ HFTs make prices more efficient because they react quicklyHFTs make prices more efficient because they react quickly
and simultaneously to new information as it arrivesand simultaneously to new information as it arrives

11. HFT Strategies and TechniquesHFT Strategies and Techniques
・・ ETF Market MakingETF Market Making
・・ Statistical ArbitrageStatistical Arbitrage
・・ News Feed ArbitrageNews Feed Arbitrage
・・ Rebate Arbitrage / ELP (Electronic Liquidity Provision)Rebate Arbitrage / ELP (Electronic Liquidity Provision)
・・ Momentum DetectionMomentum Detection
・・ Momentum IgnitionMomentum Ignition
・・ Order Flow DetectionOrder Flow Detection
・・ Order Flow PredictionOrder Flow Prediction
・・ Latency ArbitrageLatency Arbitrage
・・ Front-runningFront-running
・・ SpoofingSpoofing
・・ Quote StuffingQuote Stuffing
・・ Flash TradingFlash Trading
・・ etcetc

12. Rebate Arbitrage / ELPRebate Arbitrage / ELP
・・ A market-making strategy that seeks to earn both theA market-making strategy that seeks to earn both the
bid-offer spread and the rebates paid by trading venuesbid-offer spread and the rebates paid by trading venues
as incentives for posting liquidity. Theas incentives for posting liquidity. The Maker-TakerMaker-Taker modelmodel
gives rebates to liquidity providers (passive flowgives rebates to liquidity providers (passive flow with limitwith limit
ordersorders) while charging liquidity takers (active flow with) while charging liquidity takers (active flow with
market orders)market orders)
・・ These ELPs can afford to breakeven or even lose moneyThese ELPs can afford to breakeven or even lose money
on each trade as long as the rebates they receive coverson each trade as long as the rebates they receive covers
their coststheir costs
・・ ELP can also be Order Flow Detection. When ELPs areELP can also be Order Flow Detection. When ELPs are
adversely affected by a price that changes the current bid-adversely affected by a price that changes the current bid-
ask spread, this may indicate the presence of a large blockask spread, this may indicate the presence of a large block
order. An HFT can then use this information to initiate anorder. An HFT can then use this information to initiate an
active strategy to extract alphaactive strategy to extract alpha

13. Rebate Arbitrage / ELP – ExampleRebate Arbitrage / ELP – Example
At some point during the day, due to temporary selling pressure, there is a total of justAt some point during the day, due to temporary selling pressure, there is a total of just
100 contracts left at the best bid price of 1000.00. Recognizing that the queue at the100 contracts left at the best bid price of 1000.00. Recognizing that the queue at the
best bid is about to be depleted, HFTs submit executable limit orders to aggressivelybest bid is about to be depleted, HFTs submit executable limit orders to aggressively
sell a total of 100 contracts, thus completely depleting the queue at the best bid, andsell a total of 100 contracts, thus completely depleting the queue at the best bid, and
very quickly submit sequences of new limit orders to buy a total of 100 contracts at thevery quickly submit sequences of new limit orders to buy a total of 100 contracts at the
new best bid price of 999.75, as well as to sell 100 contracts at the new best offer ofnew best bid price of 999.75, as well as to sell 100 contracts at the new best offer of
1000.00. [2]1000.00. [2]

14. Rebate Arbitrage / ELP – Example (cont.)Rebate Arbitrage / ELP – Example (cont.)
If the selling pressure continues, then HFTs are able to buy 100 contracts at 999.75 andIf the selling pressure continues, then HFTs are able to buy 100 contracts at 999.75 and
make a profit of $1,250 dollars among them. If, however, the selling pressure stops andmake a profit of $1,250 dollars among them. If, however, the selling pressure stops and
the new best offer price of 1000.00 attracts buyers, then HFTs would very quickly sellthe new best offer price of 1000.00 attracts buyers, then HFTs would very quickly sell
100 contracts (which are at the very front of the new best offer queue), "scratching" the100 contracts (which are at the very front of the new best offer queue), "scratching" the
trade at the same price as they bought, and getting rid of the risky inventory in a fewtrade at the same price as they bought, and getting rid of the risky inventory in a few
milliseconds. [2]milliseconds. [2]

15. ETF Market Making - ExampleETF Market Making - Example
The S&P500 futures (blue) and SPY (green) should be perfectly correlated,The S&P500 futures (blue) and SPY (green) should be perfectly correlated,
and they are at minute intervals. But this correlation disappears at 250ms intervals. Thisand they are at minute intervals. But this correlation disappears at 250ms intervals. This
is the "market inefficiency" that HFT makes less so. [3]is the "market inefficiency" that HFT makes less so. [3]

16. Momentum Ignition - ExampleMomentum Ignition - Example
By trying to instigate other participants to buy or sell quickly, the instigator of momentumBy trying to instigate other participants to buy or sell quickly, the instigator of momentum
ignition can profit either having taken a pre-position or by laddering the book, knowingignition can profit either having taken a pre-position or by laddering the book, knowing
the price is likely to revert after the initial rapid price move, and trading out afterwards.the price is likely to revert after the initial rapid price move, and trading out afterwards.
[4][4]

17. Spoofers vs Front-RunnersSpoofers vs Front-Runners
・・ HFT's share of US equity trading has fallen from 61% inHFT's share of US equity trading has fallen from 61% in
2009 to 51% in 2012. Why?2009 to 51% in 2012. Why?
→→ HFTs are now 'spoofing' to draw each other outHFTs are now 'spoofing' to draw each other out
・・ Spoofing means to make a bid or offer with the intent ofSpoofing means to make a bid or offer with the intent of
cancelling the order before it is executed. It creates a falsecancelling the order before it is executed. It creates a false
sense of investor demand in the market, thereby changingsense of investor demand in the market, thereby changing
the behavior of other traders and allowing the spoofer tothe behavior of other traders and allowing the spoofer to
profit from these changesprofit from these changes
・・ Front-running HFTs profit by gleaning the intentions ofFront-running HFTs profit by gleaning the intentions of
market participants and jumping in front of their orders,market participants and jumping in front of their orders,
thereby causing the original traders to buy or sell at athereby causing the original traders to buy or sell at a
less favorable price (i.e.less favorable price (i.e. adverse selectionadverse selection))

18. Spoofers vs Front-Runners (cont.)Spoofers vs Front-Runners (cont.)
・・ Front-running HFTs are profitable against human traders,Front-running HFTs are profitable against human traders,
but not against spoofing HFTs. When the front-runningbut not against spoofing HFTs. When the front-running
HFT algo jumps ahead of a spoof order, the front-runnerHFT algo jumps ahead of a spoof order, the front-runner
gets fooled and loses money because the algo can't easilygets fooled and loses money because the algo can't easily
distinguish between legitimate orders and spoofsdistinguish between legitimate orders and spoofs
・・ Spoofing therefore poses the risk of making front-runningSpoofing therefore poses the risk of making front-running
unprofitable, thus the front-runners make the rationalunprofitable, thus the front-runners make the rational
choice to do less front-runningchoice to do less front-running
・・ Anti-spoofing regulations not only fail to safeguard theAnti-spoofing regulations not only fail to safeguard the
integrity of the market; they exacerbate the very marketintegrity of the market; they exacerbate the very market
instability that lawmakers sought to remedy by enactinginstability that lawmakers sought to remedy by enacting
the prohibitions in the first place.the prohibitions in the first place. If front-running isIf front-running is
allowed to exist, spoofing is its best remedy.allowed to exist, spoofing is its best remedy. [5][5]

19. May 6, 2010: Flash CrashMay 6, 2010: Flash Crash

20. 2010 Flash Crash - Timeline2010 Flash Crash - Timeline
・・ 13:32 CT13:32 CT: Mutual fund Waddell & Reed sold a total of 75,000: Mutual fund Waddell & Reed sold a total of 75,000
S&P500 E-Mini futures contracts ($4.1 billion). This sellS&P500 E-Mini futures contracts ($4.1 billion). This sell
pressure was initially absorbed by HFTs and otherspressure was initially absorbed by HFTs and others
・・ 13:45: As the E-Mini prices rapidly declined, the fund13:45: As the E-Mini prices rapidly declined, the fund hadhad
sold 35,000 contracts ($1.9 billion) of the 75,000 intendedsold 35,000 contracts ($1.9 billion) of the 75,000 intended
・・ 13:45:28: There were less than 1,050 contracts of buy-side13:45:28: There were less than 1,050 contracts of buy-side
resting orders in the E-Mini, representing less than 1% ofresting orders in the E-Mini, representing less than 1% of
buy-side market depth at the beginning of the daybuy-side market depth at the beginning of the day

21. 2010 Flash Crash – Timeline (cont.)2010 Flash Crash – Timeline (cont.)
・・ 13:45:28: E-Mini trading was paused for 5 sec when CME's13:45:28: E-Mini trading was paused for 5 sec when CME's
Stop Logic Functionality was triggered in order to preventStop Logic Functionality was triggered in order to prevent
a cascade of further price declines [2]a cascade of further price declines [2]

22. 2010 Flash Crash – Timeline (cont.)2010 Flash Crash – Timeline (cont.)
・・ 13:45:33: Trading resumed; the E-Mini prices stabilized and13:45:33: Trading resumed; the E-Mini prices stabilized and
began to recover shortly thereafterbegan to recover shortly thereafter
・・ 13:40 - 14:00: Over 20,000 trades across more than 30013:40 - 14:00: Over 20,000 trades across more than 300
separate securities, including many ETFs, were executedseparate securities, including many ETFs, were executed
at prices 60% or more down from their 2:40 pricesat prices 60% or more down from their 2:40 prices
・・ 14:08: The E-Mini prices were back to nearly their pre-drop14:08: The E-Mini prices were back to nearly their pre-drop
level and most securities had reverted back to trading atlevel and most securities had reverted back to trading at
prices reflecting true consensus valuesprices reflecting true consensus values

23. 2010 Flash Crash – Postmortem2010 Flash Crash – Postmortem
・・ High VolumeHigh Volume: During the 36-minute period of the Flash: During the 36-minute period of the Flash
Crash, trading volume per minute was nearly 8 timesCrash, trading volume per minute was nearly 8 times
greater than trading volume per minute earlier in the daygreater than trading volume per minute earlier in the day
・・ High VolatilityHigh Volatility: On May 6, the log-difference between the: On May 6, the log-difference between the
high and low prices of the day clocks at 9.82% or 6.4 timeshigh and low prices of the day clocks at 9.82% or 6.4 times
higher than the 1.54% average during the previous 3 dayshigher than the 1.54% average during the previous 3 days
・・ Hot Potato TradingHot Potato Trading: Between 13:45:13 and 13:45:27, when: Between 13:45:13 and 13:45:27, when
prices were plunging with a tremendous velocity, HFTsprices were plunging with a tremendous velocity, HFTs
traded over 27,000 contracts or 49% of the total volume,traded over 27,000 contracts or 49% of the total volume,
but their net position changed by a mere 200 contractsbut their net position changed by a mere 200 contracts

24. 2010 Flash Crash – HFTs2010 Flash Crash – HFTs
・・ As HFTs detected the sharp drop in price and sharp rise inAs HFTs detected the sharp drop in price and sharp rise in
volume in the futures market, many of them pausedvolume in the futures market, many of them paused tradingtrading
in thein the equities marketequities market
・・ As a result, the liquidity in the equities market evaporated,As a result, the liquidity in the equities market evaporated,
causing some large-cap companies like Procter & Gamblecausing some large-cap companies like Procter & Gamble
and Accenture to trade down as low as a penny or as highand Accenture to trade down as low as a penny or as high
as $100,000 per shareas $100,000 per share
・・ HFTs that remained in the markets exacerbated priceHFTs that remained in the markets exacerbated price
declines during the crash. How?declines during the crash. How?

25. 2010 Flash Crash – HFTs (cont.)2010 Flash Crash – HFTs (cont.)
・・ IIn the ordinary course of business, HFTs aggressivelyn the ordinary course of business, HFTs aggressively
remove the last few contracts at the best bid or ask levelsremove the last few contracts at the best bid or ask levels
and then establish new best bids and asks at adjacentand then establish new best bids and asks at adjacent
price levels (i.e. rebate arbitrage / ELP)price levels (i.e. rebate arbitrage / ELP)
・・ Under calm market conditions, this trading activityUnder calm market conditions, this trading activity
somewhat accelerates price changes and adds to tradingsomewhat accelerates price changes and adds to trading
volume, but does not result in a directional price movevolume, but does not result in a directional price move
・・ When prices are moving directionally due to an order flowWhen prices are moving directionally due to an order flow
imbalance, this activity can exacerbate a directional priceimbalance, this activity can exacerbate a directional price
move and contribute to volatility. Higher volatility furthermove and contribute to volatility. Higher volatility further
increases the speed at which the best bid and offer queuesincreases the speed at which the best bid and offer queues
get depleted, which makes HFTs act faster, leading to aget depleted, which makes HFTs act faster, leading to a
spike in volume and setting the stage for a flash crashspike in volume and setting the stage for a flash crash

26. Navinder Singh SaraoNavinder Singh Sarao
・・ On April 21, 2015, a 36-year-old UK resident Nav Sarao wasOn April 21, 2015, a 36-year-old UK resident Nav Sarao was
arrestedarrested after US Department of Justice charged him withafter US Department of Justice charged him with
market manipulation in S&P500 E-Mini futures (violationmarket manipulation in S&P500 E-Mini futures (violation
of CME Rule 575), which CFTC accused of having contributedof CME Rule 575), which CFTC accused of having contributed
to the 2010 Flash Crashto the 2010 Flash Crash
・・ Sarao was an independent trader operating from his parents'Sarao was an independent trader operating from his parents'
house in West London. He used to spoof the market usinghouse in West London. He used to spoof the market using
bespoke software which allowed him to execute abespoke software which allowed him to execute a layeringlayering
algorithm against HFTsalgorithm against HFTs
・・ On May 6, 2010, his algorithm was turned on from 09:20,On May 6, 2010, his algorithm was turned on from 09:20,
selling 2,100 contracts, then again between 11:17 and 13:40,selling 2,100 contracts, then again between 11:17 and 13:40,
selling 3,600 contracts. These orders represented persistentselling 3,600 contracts. These orders represented persistent
downward selling pressure on the E-Mini price [6]downward selling pressure on the E-Mini price [6]

27. LayeringLayering
・・ Layering is a type of spoofing which takes the form of aLayering is a type of spoofing which takes the form of a
trader placing a number of bogus sell orders – often attrader placing a number of bogus sell orders – often at
several price levels – to give the false impression ofseveral price levels – to give the false impression of
strong selling pressure and to drive the price downstrong selling pressure and to drive the price down
・・ By manipulating the price downward, the trader can thenBy manipulating the price downward, the trader can then
buy the stock at an artificially cheap price and trade outbuy the stock at an artificially cheap price and trade out
when the price reverts (the same holds for buying)when the price reverts (the same holds for buying)
・・ Layering is more viable for HFTs – their speed allows themLayering is more viable for HFTs – their speed allows them
to mitigate the risk of someone trading against those falseto mitigate the risk of someone trading against those false
orders by canceling immediately in response to anyorders by canceling immediately in response to any
upward moves [4]upward moves [4]

28. Sarao's Layering AlgorithmSarao's Layering Algorithm [7][7]

29. Did Sarao Cause the Flash Crash?Did Sarao Cause the Flash Crash?
・・ The sell orders of 3,600 contracts his layering algorithmThe sell orders of 3,600 contracts his layering algorithm
spoofed betweenspoofed between 11:17 and 13:4011:17 and 13:40 was much smaller thanwas much smaller than
the 75,000 contracts Waddell & Reed sold from 13:32the 75,000 contracts Waddell & Reed sold from 13:32
・・ His algorithm was already stopped at 13:40 when the FlashHis algorithm was already stopped at 13:40 when the Flash
Crash was ignited at 13:42Crash was ignited at 13:42
・・ Still, in addition to the layering algorithm, Sarao spoofedStill, in addition to the layering algorithm, Sarao spoofed
aggressively, selling 32,046 contracts manually betweenaggressively, selling 32,046 contracts manually between
12:33 and 13:45 [6]12:33 and 13:45 [6]
→→ He did not cause the Flash Crash directly, but contributedHe did not cause the Flash Crash directly, but contributed
to the extreme order book imbalance in the E-Mini marketto the extreme order book imbalance in the E-Mini market

30. ConclusionsConclusions
・・ HFTs generally have poorer risk controls because ofHFTs generally have poorer risk controls because of
competitive time pressure, lacking in the more extensivecompetitive time pressure, lacking in the more extensive
safety checks that are normally used in slower tradessafety checks that are normally used in slower trades
・・ HFTs did not cause the 2010 Flash Crash but exacerbated itHFTs did not cause the 2010 Flash Crash but exacerbated it
by reducing the liquidity and inducing directional priceby reducing the liquidity and inducing directional price
moves at an accelerated ratemoves at an accelerated rate
・・ Nav Sarao did not cause the crash either, but contributedNav Sarao did not cause the crash either, but contributed
to the order book imbalance by intensive spoofingto the order book imbalance by intensive spoofing
・・ The Flash Crash was a result of multiple complex factorsThe Flash Crash was a result of multiple complex factors
・・ The speed race continues as long as HFT is profitableThe speed race continues as long as HFT is profitable

31. ReferencesReferences
[1] http://www.sec.gov/Archives/edgar/data/1592386/000104746915001003/[1] http://www.sec.gov/Archives/edgar/data/1592386/000104746915001003/
a2219372zs-1a.htma2219372zs-1a.htm
[2] Kirilenko et al., “The Flash Crash: The Impact of High Frequency Trading on[2] Kirilenko et al., “The Flash Crash: The Impact of High Frequency Trading on
an Electronic Market,” 2014.an Electronic Market,” 2014.
[3] Budish et al., "The High-Frequency Trading Arms Race: Frequent Batch[3] Budish et al., "The High-Frequency Trading Arms Race: Frequent Batch
Auctions as a Market Design Response," 2013Auctions as a Market Design Response," 2013
[4] Tse et al., “High Frequency Trading – Measurement, Detection and[4] Tse et al., “High Frequency Trading – Measurement, Detection and
Response,” 2012Response,” 2012
[5] http://www.bloombergview.com/articles/2015-01-23/high-frequency-trading-[5] http://www.bloombergview.com/articles/2015-01-23/high-frequency-trading-
spoofers-and-front-runningspoofers-and-front-running
[6] http://www.cftc.gov/ucm/groups/public/@lrenforcementactions/documents/[6] http://www.cftc.gov/ucm/groups/public/@lrenforcementactions/documents/
legalpleading/enfsaraocomplaint041715.pdflegalpleading/enfsaraocomplaint041715.pdf
[7] https://twitter.com/nanexllc/status/592315463482216448/photo/1[7] https://twitter.com/nanexllc/status/592315463482216448/photo/1

32. ThanksThanks