High-Frequency Trading High-Frequency Trading and 2010 Flash Crashand 2010 Flash Crash

Yoshiharu SatoYoshiharu SatoUniversity of Warsaw, 2015University of Warsaw, 2015

(https://sites.google.com/site/yoshi2233/)(https://sites.google.com/site/yoshi2233/)

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 marketdata 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 mostinfinitesimal price discrepancies that only exist over the mostinfinitesimal price discrepancies that only exist over the mostinfinitesimal 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 venuesto 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 andhigh turnover, with positions rarely held overnighthigh turnover, with positions rarely held overnight

History of HFT – Need for SpeedHistory of HFT – Need for Speed

・・ 1815: Rothschilds front-ran competitors by using courier 1815: 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 order 1976: Introduction of NYSE DOT (the first electronic order routing system)routing system)

・・ 1983: Bloomberg launched the first computerized system1983: Bloomberg launched the first computerized systemto 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

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 ofexecuting trades in nanosecondsexecuting trades in nanoseconds

・・ 2013: Laser beams and Microwave dishes are the latest2013: Laser beams and Microwave dishes are the latesttechnologies to shave milliseconds off dealing timestechnologies to shave milliseconds off dealing times

Tokyo Stock Exchange – Case StudyTokyo Stock Exchange – Case Study

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 Market in 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 within Significant 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

TSE & HFT (cont.)TSE & HFT (cont.)

・・ Trade volume shares of HFTs increased from 10% in 2010 Trade 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 1,000 HFTs 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 yearsdue to HFTs (due to HFTs (Japan Securities Dealers Association)Japan Securities Dealers Association)

・・ TSE charges a small fee on every stock order, whether it’s TSE 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

Who Are HFTs?Who Are HFTs?

・・ HFT was first made successful by an American hedge fund HFT 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 from trading 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

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]

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 toevery market participantevery market participantfrom small retail tradersfrom small retail tradersto large institutional tradersto large institutional traders

・・ Has been criticized for front-running and Has been criticized for front-running and flash tradingflash trading (viewing orders from other market participants fractions of(viewing orders from other market participants fractions ofa second, typically 30 milliseconds, before others do)a second, typically 30 milliseconds, before others do)

・・ HFTs make prices more efficient because they react quickly HFTs make prices more efficient because they react quickly and simultaneously to new information as it arrivesand simultaneously to new information as it arrives

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

Rebate Arbitrage / ELPRebate Arbitrage / ELP

・・ A market-making strategy that seeks to earn both theA market-making strategy that seeks to earn both thebid-offer spread and the rebates paid by trading venuesbid-offer spread and the rebates paid by trading venuesas incentives for posting liquidity. The as incentives for posting liquidity. The Maker-TakerMaker-Taker model model gives rebates to liquidity providers (passive flow gives rebates to liquidity providers (passive flow with limit with 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 moneyon each trade as long as the rebates they receive covers on each trade as long as the rebates they receive covers their coststheir costs

・・ ELP can also be Order Flow Detection. When ELPs are ELP 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 block ask spread, this may indicate the presence of a large block order. An HFT can then use this information to initiate an order. An HFT can then use this information to initiate an active strategy to extract alphaactive strategy to extract alpha

Rebate Arbitrage / ELP – ExampleRebate Arbitrage / ELP – Example

At some point during the day, due to temporary selling pressure, there is a total of just At 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 the 100 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 aggressively best 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, and sell 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 the very 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 of new 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]

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 and If 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 and make 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 sell the 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" the 100 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 few trade at the same price as they bought, and getting rid of the risky inventory in a few

milliseconds. [2]milliseconds. [2]

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. This and 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]

Momentum Ignition - ExampleMomentum Ignition - Example

By trying to instigate other participants to buy or sell quickly, the instigator of momentum By 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, knowing ignition 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]

Spoofers vs Front-RunnersSpoofers vs Front-Runners

・・ HFT's share of US equity trading has fallen from 61% in HFT'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 of Spoofing means to make a bid or offer with the intent of cancelling the order before it is executed. It creates a false cancelling the order before it is executed. It creates a false sense of investor demand in the market, thereby changing sense of investor demand in the market, thereby changing the behavior of other traders and allowing the spoofer to the behavior of other traders and allowing the spoofer to profit from these changesprofit from these changes

・・ Front-running HFTs profit by gleaning the intentions of Front-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 aless favorable price (i.e. less favorable price (i.e. adverse selectionadverse selection))

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-running but not against spoofing HFTs. When the front-running HFT algo jumps ahead of a spoof order, the front-runner HFT algo jumps ahead of a spoof order, the front-runner gets fooled and loses money because the algo can't easily gets 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-running Spoofing therefore poses the risk of making front-running unprofitable, thus the front-runners make the rational unprofitable, 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 the Anti-spoofing regulations not only fail to safeguard the integrity of the market; they exacerbate the very market integrity of the market; they exacerbate the very market instability that lawmakers sought to remedy by enacting instability that lawmakers sought to remedy by enacting the prohibitions in the first place. the prohibitions in the first place. If front-running is If front-running is allowed to exist, spoofing is its best remedy.allowed to exist, spoofing is its best remedy. [5] [5]

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

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 sell S&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 had had 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-side 13:45:28: There were less than 1,050 contracts of buy-side resting orders in the E-Mini, representing less than 1% of resting 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

2010 Flash Crash – Timeline (cont.)2010 Flash Crash – Timeline (cont.)

・・ 13:45:28: E-Mini trading was paused for 5 sec when CME's 13:45:28: 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 preventa cascade of further price declines [2]a cascade of further price declines [2]

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 andbegan to recover shortly thereafterbegan to recover shortly thereafter

・・ 13:40 - 14:00: Over 20,000 trades across more than 300 13:40 - 14:00: Over 20,000 trades across more than 300 separate securities, including many ETFs, were executed separate 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-drop 14:08: The E-Mini prices were back to nearly their pre-drop level and most securities had reverted back to trading at level and most securities had reverted back to trading at prices reflecting true consensus valuesprices reflecting true consensus values

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 times Crash, trading volume per minute was nearly 8 times greater than trading volume per minute earlier in the day greater 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 times high 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, HFTs prices 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

2010 Flash Crash – HFTs2010 Flash Crash – HFTs

・・ As HFTs detected the sharp drop in price and sharp rise in As 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 trading trading 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 & Gamble causing some large-cap companies like Procter & Gamble and Accenture to trade down as low as a penny or as high and 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 price HFTs that remained in the markets exacerbated price declines during the crash. How?declines during the crash. How?

2010 Flash Crash – HFTs (cont.)2010 Flash Crash – HFTs (cont.)

・・ IIn the ordinary course of business, HFTs aggressively n the ordinary course of business, HFTs aggressively remove the last few contracts at the best bid or ask levels remove the last few contracts at the best bid or ask levels and then establish new best bids and asks at adjacent and 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 activity Under calm market conditions, this trading activity somewhat accelerates price changes and adds to trading somewhat 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 flow When prices are moving directionally due to an order flow imbalance, this activity can exacerbate a directional price imbalance, this activity can exacerbate a directional price move and contribute to volatility. Higher volatility further move and contribute to volatility. Higher volatility further increases the speed at which the best bid and offer queues increases the speed at which the best bid and offer queues get depleted, which makes HFTs act faster, leading to a get 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

Navinder Singh SaraoNavinder Singh Sarao

・・ On April 21, 2015, a 36-year-old UK resident Nav Sarao was On April 21, 2015, a 36-year-old UK resident Nav Sarao was arrested arrested after US Department of Justice charged him with after US Department of Justice charged him with market manipulation in S&P500 E-Mini futures (violationmarket manipulation in S&P500 E-Mini futures (violationof CME Rule 575), which CFTC accused of having contributedof CME Rule 575), which CFTC accused of having contributedto 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 usingbespoke software which allowed him to execute a bespoke 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 persistentdownward selling pressure on the E-Mini price [6]downward selling pressure on the E-Mini price [6]

LayeringLayering

・ ・ Layering is a type of spoofing which takes the form of a Layering is a type of spoofing which takes the form of a trader placing a number of bogus sell orders – often at trader placing a number of bogus sell orders – often at several price levels – to give the false impression of several 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 then By 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 outwhen 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 them Layering is more viable for HFTs – their speed allows them to mitigate the risk of someone trading against those false to mitigate the risk of someone trading against those false orders by canceling immediately in response to any orders by canceling immediately in response to any upward moves [4]upward moves [4]

Sarao's Layering Algorithm Sarao's Layering Algorithm [7][7]

Did Sarao Cause the Flash Crash?Did Sarao Cause the Flash Crash?

・ ・ The sell orders of 3,600 contracts his layering algorithm The sell orders of 3,600 contracts his layering algorithm spoofed betweenspoofed between 11:17 and 13:40 11:17 and 13:40 was much smaller thanwas much smaller thanthe 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 Flash His 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 spoofedaggressively, selling 32,046 contracts manually between aggressively, 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 contributedto the extreme order book imbalance in the E-Mini marketto the extreme order book imbalance in the E-Mini market

ConclusionsConclusions

・・ HFTs generally have poorer risk controls because of HFTs generally have poorer risk controls because of competitive time pressure, lacking in the more extensive competitive 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 itby reducing the liquidity and inducing directional price by 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 contributedto 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

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

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