High Frequency Trading

Ingrid M. Werner Martin and Andrew Murrer Professor of Finance

Fisher College of Business The Ohio State University

February 2013

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Fantasecond

• On September 15, 2011, beginning at 12:48:54.600, there was a time warp in the trading of Yahoo! (YHOO) stock.

• HFT has reached speeds faster than the speed-of-light, allowing time travel into the future.

• It all happened in just over one second of trading, the evidence buried under an avalanche of about 19,000 quotes and 3,000 individual trade executions.

• Based on official time stamps, YHOO trades were executed on quotes that didn't exist until 190 milliseconds later!

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Outline • Definition • Colocation, Latency and Big Data • Trading Strategies • Empirical Evidence • The Dark Side of HFT • Adapting to HFT • Regulatory Issues

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HFT Definition

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HFT Definition

“Employing technology and algorithms to capitalize on very short-lived information gleaned from publicly available data using sophisticated statistical, econometric, machine learning, and other quantitative techniques” SEC Concept Release on Equity Market Structure (SEC 2010)

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HFT Definition • Electronic trading

– Ability to transmit orders electronically • Algorithmic trading

– Electronic trading whose parameters are determined by strict adherence to a predetermined set of rules aimed at delivering specific execution outcomes.

• High Frequency Trading – Algorithmic trading where a large number of orders are sent

into the market at high speed, with round-trip execution times measured in microseconds.

• Ultra High-Frequency Trading – High-Frequency Trading using co-location to minimize network

latencies.

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HFT Volume

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HFTs trade 37% of volume in Canada Source: IIROC (2012) study

Colocation, Latency, and Big Data

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The New World of Trading

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Monitoring Latency

11 The speed of light is 299,792,458 meters/second = 4.8 microseconds/kilometer

Big Data • About 25 billion messages pass through NASDAQ OMX’s

U.S. equities and options systems on an active day. • That is over 1 million messages per second in a 6.5 hour

day. • During volatile periods, peak message traffic can be twice

as much. • James Mangold, NasdaqOMX: • “We keep more than a compressed petabyte (1,000

terabytes) of raw trading data online at all times. Uncompressed it could be five or six petabytes.”

• “That’s a tremendous amount of data, and the volume is predicted to double in the foreseeable future.”

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Petabyte, Exabyte, Zettabyte, and Yottabyte

• Petabyte (1 000 000 000 000 000 bytes) – 1 Petabyte: 5 years of EOS data (at 46 mbps) – 2 Petabytes: All US academic research libraries – 20 Petabytes: Production of hard-disk drives in 1995 – 200 Petabytes: All printed material OR

Production of digital magnetic tape in 1995

• Exabyte (1 000 000 000 000 000 000 bytes) – 5 Exabytes: All words ever spoken by human beings.

• Zettabyte (1 000 000 000 000 000 000 000 bytes) • Yottabyte (1 000 000 000 000 000 000 000 000 bytes)

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HFT Strategies

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High Frequency Trading Strategies

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HFT Empirical Evidence

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Empirical Evidence • How do HFTs trade?

– Jovanovic and Menkveld (2010) – Baron, Brogaard, and Kirilenko (2012) – IIROC Study (2012) – Clark-Joseph (2013)

• Is HFT associated with a decrease or increase in market quality during regular market conditions?

– Brogaard (2011), Brogaard, Hendershott and Riordan (2012) – Hendershott and Menkveld (2011) – Hasbrouck and Saar (2011) – Menkveld (2011) – Malinkova, Park, and Riordan (2012) – Riordan and Storkenmaier (2012)

• Does HFT exacerbate liquidity problems when markets are under stress? – Kirilenko et al (2011) – Easley, Lopez de Prado, and O’Hara (2011)

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Contrarian Liquidity Supply

• Brogaard (2011) finds that HFTs participate in 77% of all trades.

• HFTs in his sample demand liquidity for 50.4% of all trades and supply liquidity for 51.4% of all trades.

• HFTs tend to be contrarian. • He finds no evidence that HFTs withdraw from

markets in bad times or engage in front-running of large non-HFT trades.

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Go Home Flat

• Menkveld (2012) studies the effect of entry of a new HFT into trading of Dutch stocks at Euronext and Chi-X.

• The HFT goes home flat • The HFT earnings arise

from passive orders.

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Rebate Trading • Exchanges pay traders who

post limit orders that execute a Liquidity Rebate: e.g., $0.003/share

• HFTs plan: – place a limit buy order – if his order is matched, he

turns around and places a limit sell order at the same price

– profit from trading will be $0.00, but he will collect rebates!

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IIROC Study (2012)

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Predict Prices Correlated with Public Information

• Brogaard, Hendershott, and Riordan (2012) study HFTs on Nasdaq.

• HFT predicts price changes in the overall market over short horizons measured in seconds.

• HFT is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances.

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Exploratory Trading

• Clark-Josephs (2013) finds that HFTs on the CME use small (loss-making) orders to estimate the elasticity of supply to predictable demand shocks.

• The HFT selectively then submits (profit-making) marketable orders that are timed to occur before predictable demand shocks.

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Excess HFT Performance

24 Source: Clark-Joseph (2013)

Strategic Runs

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• Hasbrouck and Saar (2011) construct a measure of HFT by identifying “strategic runs,” for Nasdaq stocks.

HFT Profitability

26 Source: Baron, Brogaard, Kirilenko (2012)

HFT and Market Quality

• Spreads, commissions, and fees have been trending downward, volume has exploded, and price efficiency has improved in the last decade as significant trading market structure changes have been implemented.

• HFT has increased dramatically during the same time period, as technology costs have declined and processing capacity and network speeds have increase exponentially.

• Careful empirical analysis is required to draw inferences about the effect of HFT on liquidity, trading costs, and price efficiency.

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US Markets

• Hendershott, Jones, and Menkveld (2011) find that increased algorithmic trading on the NYSE contributed to lower bid-ask spreads, particularly for large stocks.

• Hasbrouck and Saar (2011) find that increased HFT on Nadaq improves measures of market quality (spreads, depth, and volatility).

• Brogaard, Hendershott, and Riordan (2012) find that HFTs on Nasdaq facilitate price discovery.

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Canada

• Malinova, Park, and Riordan (2012) study the 4/1/2012 introduction of a message fee.

• Post event, HFTs generate fewer messages overall and particularly in smaller, less liquid stocks.

• The reduction of HFT message traffic causes and increase in spreads and an increase in the trading costs of retail and other traders.

• In other words, HFT activities generate a positive externality and lower other market participants’ trading costs.

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Canada

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Source: Malinova, Park, and Riordan (2012)

Xetra, Euronext, Chi-X • Hendershott and Riordan (2011) find that Xetra algorithmic

trading represents 52% of share volume and 50% of liquidity provision, and contributes to price efficiency for DAX 30. – Similar results have been found by Prix, Loistl,

and Huetl (2007) and Groth (2011) for Xetra and in the FX markets by Chaboud, Chiquoine, Hjalmarsson, and Vega (2009).

• Riordan and Storkenmaier (2009) find that reductions in system latency from 50ms to 10ms round trip on Xetra reduces trading costs, lowers adverse selection costs, increases liquidity and the informativeness of price.

• Menkveld (2012) finds that the bid-ask spreads were reduced by 30% compared to Belgian stocks that were not traded by the HFT.

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The Dark Side of HFT

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Quote Stuffing

33 Source: Credit Suisse (2012)

Quote Stuffing: Heineken, 2nd May, 2011 Quote Stuffing: Telefonica, 10th August, 2012

Quote stuffing is a strategy that floods the market with huge numbers of orders and cancellations in rapid succession. This creates a large number of new best bids and offers, each potentially lasting mere microseconds.

Layering

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Markets Under Stress

• The Flash Crash of May 6, 2010, lasted less than 30 minutes.

• The DJIA dropped 998 points, and for a brief moment more than $1 trillion in market capitalization was lost.

• In the aftermath of the Flash Crash, more than 20,000 trades were cancelled.

• More importantly, the Flash Crash contributed to retail traders’ loss of faith in the integrity of equity markets.

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Flash Crash

• The CFTC-SEC reports highlight the role of a large algorithmic sell trade in the S&P e-mini futures contract that coincided with the beginning of the Flash Crash.

• However, the reports find many other factors that contributed to the events of May 6, 2010, such as routing rules, quoting conventions, internalizers, HFTs, and trading halts.

• The reports highlight that episodic illiquidity can arise, and when it does, it is rapidly transmitted to correlated markets.

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Periodic Illiquidity

• Kirilenko et al. (2011) find that HFT in the futures market did not trigger the Flash Crash, but shifted from being liquidity providers to liquidity demanders as prices fell thus exacerbating market volatility.

• Easley, Lopez de Prado and O’Hara (2011) argue that historically high levels of order toxicity in the hours leading up to the Flash Crash forced market makers to withdraw liquidity from the market.

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Periodic Illiquidity

• On June 8, 2011, natural gas futures plummeted 8.1% and then bounced back seconds later.

• On 2 February 2011, an errant algorithm in oil futures sent 2000-3000 orders in a single second, causing an 8 fold increase in volatility and moving the oil price $1 before the algorithm was shut down.

• In March, trades in 10 new Morningstar ETFs were cancelled when prices fell by as much as 98% following what was determined to be a fat-finger problem.

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Adapting to HFT

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Implications for Asset Management

• Liquidity providing HFT help lower your slippage.

• But predatory HFTs may increase slippage. • Defense strategies (Easley et al (2012)):

– Develop trading strategies based on volume-clock (event-based time)

– Develop statistics to monitor HFT activity and take advantage of their weakness.

• Hide in dark pools. 40

Response to HFT

• Sophisticated market participants use a variety of techniques including – pattern recognition – burst detection, and – feature extraction

to detect various negative HFT behaviors and adapt their strategies accordingly.

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Quote Stuffing

42 Source: Credit Suisse (2012)

Quote Stuffing: Heineken, 2nd May, 2011

Regulatory Issues

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Regulatory Issues

• Should HFTs be required to stay active during volatile periods?

• How should the message traffic be managed and paid for?

• Should co-location and direct market access of unregulated and unsupervised entities be prohibited/limited?

• How can markets provide incentives to encourage more transparent liquidity?

• Should exchanges be able to kill errant algorithms?

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Regulatory Issues

• Bots programmed by linguists scanning news releases to generate signals – News Analytic Signals. – Correlated Buy/sell orders triggered by News Analytic

signals may cause periodic illiquidity. • Jarrow and Protter (2011) • Gross-Klussman and Hautsch (2011)

• Kill switches for errant algorithms. • Trading platforms may choose to exclude HFTs.

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News Analytic Signals

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Kill-switch

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Investor Confidence

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Conclusions

• HFT use a variety of trading strategies. • HFT is on average associated with better

market quality, improved market efficiency, and lower volatility.

• HFT is associated with periodic illiquidity. • HFT arms race is of questionable societal

value.

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Looking Ahead

• Consolidated audit trail across markets. • Harmonize circuit breakers and other frictions

across markets. • Fees for excessive message traffic as

implemented in Canada and proposed last year by NasdaqOMX and Direct Edge.

• Ensure fairness of access to market data.

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