A 2014 CFTC report has concluded that the 2010 Flash Crash was not caused by high-frequency traders but was exacerbated by them with their market-making strategy known as the electronic liquidity provision (ELP). This paper presents a computational analysis of the impact of ELP-HFTs on core market quality during a flash crash. Specifically examined is how ELP-HFTs affect the attributes of core market quality such as liquidity, bid-ask spreads, and short-term price volatility. To investigate the question, we build a zero-intelligence limit order book (LOB) simulator from scratch, implement the ELP strategy in it, and execute simulations in which a flash crash is artificially created. Our results show that ELP-HFTs reduce bid-ask spreads, mitigate short-term volatility, and increase total trade volume. The increase in total trade volume is attributed to what is known as the “hot-potato” effect, which was also observed during the 2010 Flash Crash. However, we conclude that the ELP strategy by itself does not amplify directional price moves despite hot-potato effects.

1. Flash Crash SimulatorFlash Crash Simulator
Analyzing HFT's Impact on Market QualityAnalyzing HFT's Impact on Market Quality
Yoshiharu SatoYoshiharu Sato
University of Warsaw, 2015University of Warsaw, 2015
https://sites.google.com/site/yoshi2233/https://sites.google.com/site/yoshi2233/

2. About MeAbout Me
BackgroundBackground
・・ 3D Graphics Programming (C/C++)3D Graphics Programming (C/C++)
・・ Derivatives Trading (CFDs, Vanilla Options)Derivatives Trading (CFDs, Vanilla Options)
・・ BSc in Quantitative Analysis & InformaticsBSc in Quantitative Analysis & Informatics
ExpertiseExpertise
・・ Real-Time, High-Performance Systems in C++Real-Time, High-Performance Systems in C++

3. My Previous Work:My Previous Work: 3D Engine in C/C++3D Engine in C/C++
(Geometry, Quaternion, Optics, SIMD)(Geometry, Quaternion, Optics, SIMD)

4. My Previous Work:My Previous Work: Algorithm Backtester in C++Algorithm Backtester in C++
(Statistics, Econometrics, Time-Series Analysis, Multithreading)(Statistics, Econometrics, Time-Series Analysis, Multithreading)

5. Current Research (MSc in Quantitative Finance):Current Research (MSc in Quantitative Finance):
High Frequency TradingHigh Frequency Trading

6. HFT has been criticized for:HFT has been criticized for:
・・ unfair speed advantageunfair speed advantage (direct feed, colocation)(direct feed, colocation)
・・ predatory trading practicespredatory trading practices (front-running)(front-running)
・・ allegedly causing flash crashesallegedly causing flash crashes

7. Empirical studies show that HFT has:Empirical studies show that HFT has:
・・ narrowed bid-ask spreadsnarrowed bid-ask spreads
・・ reduced transaction costsreduced transaction costs
・・ made price formations more efficientmade price formations more efficient

8. Two Types of HFTTwo Types of HFT
・・ OpportunisticOpportunistic
- statistical arbitrage- statistical arbitrage
- news feed arbitrage- news feed arbitrage
- latency arbitrage- latency arbitrage
- momentum detection- momentum detection
- order flow prediction, etc- order flow prediction, etc
・・ Market MakingMarket Making
- rebate arbitrage (ELP)- rebate arbitrage (ELP)
- ETF market making- ETF market making
- option market making, etc- option market making, etc

9. Rebate Arbitrage (ELP)Rebate Arbitrage (ELP)
・・ A market-making HFT strategy that seeks to earn bothA market-making HFT strategy that seeks to earn both
the bid-ask spreads and the rebates paid by exchangesthe bid-ask spreads and the rebates paid by exchanges
as incentives for providing liquidityas incentives for providing liquidity
・・ TheThe Maker-TakerMaker-Taker pricing model gives rebates topricing model gives rebates to
liquidity providers (passive flowliquidity providers (passive flow with limit orderswith limit orders) while) while
charging liquidity takers (active flow with marketcharging liquidity takers (active flow with market
orders)orders)
・・ These ELP-HFTs can afford to breakeven or even loseThese ELP-HFTs can afford to breakeven or even lose
money on each trade as long as the rebates they receivemoney on each trade as long as the rebates they receive
covers their costscovers their costs

10. Market-Making HFT:Market-Making HFT: Virtu FinancialVirtu Financial
Virtu made money on 1,484 out of 1,485 trading days (only one losingVirtu made money on 1,484 out of 1,485 trading days (only one losing
trading day =trading day = 99.93% winning rate99.93% winning rate) from Jan 2009 to Dec 2014, and) from Jan 2009 to Dec 2014, and
made money EVERY trading day in 2014, generating profit of $190made money EVERY trading day in 2014, generating profit of $190
million from revenue of $723 millionmillion from revenue of $723 million

11. Virtu Financial (cont.)Virtu Financial (cont.)

12. Case Study:Case Study: Tokyo Stock ExchangeTokyo Stock Exchange

13. HFT @ TSEHFT @ TSE
・・ TSE introduced a new exchange systemTSE introduced a new exchange system 'Arrowhead''Arrowhead'
in 2010, offeringin 2010, offering ULLDMAULLDMA (Ultra-Low Latency Direct(Ultra-Low Latency Direct
Market Access)Market Access) to HFTsto HFTs
・・ System development started in 2007 by 500 staffs fromSystem development started in 2007 by 500 staffs from
Fujitsu, based on “V-model with feedback”Fujitsu, based on “V-model with feedback”
((4,000+4,000+ pages of system requirement)pages of system requirement)
・・ 200+ exchange servers are connected via high-speed200+ exchange servers are connected via high-speed
networks, each usingnetworks, each using IMDB (In-Memory Database)IMDB (In-Memory Database)

14. HFT @ TSE (cont.)HFT @ TSE (cont.)
・・ Trade volume shares of HFTs increased from 10% inTrade volume shares of HFTs increased from 10% in
2010 to2010 to 72%72% (270 trillion yen or $2.3 trillion) in 2014(270 trillion yen or $2.3 trillion) in 2014
・・ 8,000+8,000+ human dealers lost their job over the past 5 yearshuman dealers lost their job over the past 5 years
due to the rise of HFTdue to the rise of HFT ((Japan Securities Dealers Association)Japan Securities Dealers Association)
・・ Arrowhead v2.0 started operation in September 2015,Arrowhead v2.0 started operation in September 2015,
reducing transaction latency toreducing transaction latency to 0.5 millisecond0.5 millisecond andand
increasing transaction capacity toincreasing transaction capacity to 270 million orders/day270 million orders/day
・・ New safety mechanisms: Cancel On Disconnect, KillNew safety mechanisms: Cancel On Disconnect, Kill
Switch, User-Set Hard Limits, Dummy SymbolSwitch, User-Set Hard Limits, Dummy Symbol

15. Did HFT really cause the Flash CrashDid HFT really cause the Flash Crash
on May 6, 2010?on May 6, 2010?

16. The Answer Is No,The Answer Is No, ButBut
・・ CFTC official report concludes that HFTs didCFTC official report concludes that HFTs did
not cause the Flash Crash but they amplified itnot cause the Flash Crash but they amplified it
by reducing liquidity andby reducing liquidity and inducing downwardinducing downward
price spiralprice spiral
→→ How?How?

17. Order Book ImbalanceOrder Book Imbalance
・・ Large sell orders (totalLarge sell orders (total $4.1 billion$4.1 billion)) byby WaddellWaddell
& Reed& Reed andand large 'spoofing' sell orderslarge 'spoofing' sell orders byby NavNav
SaraoSarao caused an order book imbalance in S&Pcaused an order book imbalance in S&P
500 E-Mini futures, starting the price drop500 E-Mini futures, starting the price drop
・・ As HFTs detected the drop in E-Mini, many ofAs HFTs detected the drop in E-Mini, many of
them paused trading in equities market due tothem paused trading in equities market due to
high volatility and high risk of adverse selection,high volatility and high risk of adverse selection,
making liquidity evaporated from the marketmaking liquidity evaporated from the market

18. CFTC Official ExplanationCFTC Official Explanation
・・ When prices are moving directionally due toWhen prices are moving directionally due to
order book imbalance, ELP-HFT can exacerbateorder book imbalance, ELP-HFT can exacerbate
a price move and contribute to high volatilitya price move and contribute to high volatility
・・ Higher volatility further increases the speed atHigher volatility further increases the speed at
which the best bid and offer queues get depletedwhich the best bid and offer queues get depleted
by ELP-HFT, leading to a spike in trade volumeby ELP-HFT, leading to a spike in trade volume
(“(“hot-potato effect”hot-potato effect”)) and induce a flash crashand induce a flash crash

19. Research GoalResearch Goal
・・ If the CFTC's explanation is true, then I shouldIf the CFTC's explanation is true, then I should
be able to induce a flash crash by implementingbe able to induce a flash crash by implementing
the ELP strategy in an order book simulatorthe ELP strategy in an order book simulator
→→ Analyze the impact of ELP-HFT on core marketAnalyze the impact of ELP-HFT on core market
quality in order book simulationsquality in order book simulations

20. IssuesIssues

21. Car Crash SimulationCar Crash Simulation
・・ Car/engine structuresCar/engine structures
→→ KnownKnown
・・ Material propertiesMaterial properties
→→ KnownKnown
・・ Deformation modelDeformation model
→→ KnownKnown
・・ Simulation Real≒Simulation Real≒

22. Flash Crash SimulationFlash Crash Simulation
・・ Market structureMarket structure
→→ UnknownUnknown (lack of comprehensive audit trail)(lack of comprehensive audit trail)
・・ Strategies mechanicsStrategies mechanics
→→ UnknownUnknown (undisclosed intellectual properties)(undisclosed intellectual properties)
・・ Interaction modelInteraction model
→→ UnknownUnknown (too much interactive complexity)(too much interactive complexity)
・・ Simulation ≠ Real (simulation has to be simplified)Simulation ≠ Real (simulation has to be simplified)

23. Limit Order Book (LOB) SimulationLimit Order Book (LOB) Simulation
・・ ELP market-making strategy can NOT beELP market-making strategy can NOT be
simulated using static historical data due to itssimulated using static historical data due to its
interactions with the LOB (interactions with the LOB (== no backtestingno backtesting))
・・ Requires a full LOB matching engine insteadRequires a full LOB matching engine instead
→→ Implement it in multithreaded C++Implement it in multithreaded C++

24. Zero-Intelligence LOB SimulationZero-Intelligence LOB Simulation
・・ Numerical models (developed mainly byNumerical models (developed mainly by
econophysicists) in which random orders areeconophysicists) in which random orders are
submitted at random prices to the LOB withoutsubmitted at random prices to the LOB without
any AI / trading strategiesany AI / trading strategies
・・ Particles of 2 types (bids and asks) in 1-D gridParticles of 2 types (bids and asks) in 1-D grid
are subject to 3 types of eventsare subject to 3 types of events (Challet et al., 2001)(Challet et al., 2001)
(1)(1) depositiondeposition (limit orders)(limit orders)
(2)(2) annihilationannihilation (market orders)(market orders)
(3)(3) evaporationevaporation (cancellation)(cancellation)

25. Limit & Market OrdersLimit & Market Orders
・・ New order arrival follows aNew order arrival follows a Poisson distributionPoisson distribution
・・ Trade volume at the best bid or ask (marketTrade volume at the best bid or ask (market
orders & marketable limit orders) follows aorders & marketable limit orders) follows a
Gamma distributionGamma distribution (Bouchaud et al., 2002)(Bouchaud et al., 2002)

26. Cancellation OrdersCancellation Orders
・・ Probability of cancellation depends:Probability of cancellation depends:
- exponentially on the position in the LOB (- exponentially on the position in the LOB (yy))
- linearly on the order book imbalance (- linearly on the order book imbalance (NNimbimb))
- decreases by the total number of orders (- decreases by the total number of orders (NN))
(Mike et al., 2008)(Mike et al., 2008)

27. Einstein-Smoluchowski Relation (FDR)Einstein-Smoluchowski Relation (FDR)
・・ LOB seen as a colloidal Brownian particleLOB seen as a colloidal Brownian particle
embedded in smaller fluid moleculesembedded in smaller fluid molecules (Yura et al., 2014)(Yura et al., 2014)

28. My LOB Simulator (C++) ArchitectureMy LOB Simulator (C++) Architecture
・・ Asynchronous Function Parallel ModelAsynchronous Function Parallel Model

29. Demo Available for Download:Demo Available for Download:
https://sites.google.com/site/yoshi2233/hft-flashcrashhttps://sites.google.com/site/yoshi2233/hft-flashcrash

30. Simulation ResultsSimulation Results
・・ ConsistentConsistent with empirical studieswith empirical studies
・・ HFT reduces bid-ask spreadsHFT reduces bid-ask spreads
(Hendershott et al., 2011 / Hasbrouck et al., 2013 /(Hendershott et al., 2011 / Hasbrouck et al., 2013 /
Moriyasu et al., 2013 / Nanex Research, 2014 / Hruska et al., 2015)Moriyasu et al., 2013 / Nanex Research, 2014 / Hruska et al., 2015)
・・ HFT mitigates short-term price volatilityHFT mitigates short-term price volatility
(Hagstromer et al., 2013 / ASIC, 2015)(Hagstromer et al., 2013 / ASIC, 2015)

31. Simulation Results (cont.)Simulation Results (cont.)
・・ InconsistentInconsistent with the CFTC official explanationwith the CFTC official explanation
・・ ELP-HFT causes hot-potato effects,ELP-HFT causes hot-potato effects, but does notbut does not
amplify directional price movesamplify directional price moves
→→ Some other HFT strategies amplified the drop?Some other HFT strategies amplified the drop?
(e.g. Momentum Ignition, Order Flow Detection)(e.g. Momentum Ignition, Order Flow Detection)

32. ConclusionConclusion
・・ It's important to distinguish betweenIt's important to distinguish between
opportunistic HFT and market-making HFTopportunistic HFT and market-making HFT
・・ To test HFT strategies, you need a limit orderTo test HFT strategies, you need a limit order
book (LOB) matching engine and simulatorbook (LOB) matching engine and simulator
・・ ELP market-making strategy by itself does notELP market-making strategy by itself does not
induce flash crashes, yet it can be used to initiateinduce flash crashes, yet it can be used to initiate
other opportunistic, volatility-inducing strategiesother opportunistic, volatility-inducing strategies
(e.g. Order Flow Detection)(e.g. Order Flow Detection)

33. ThanksThanks