The Evolution of Stock Markets

Anthony Clake

September 23, 2013

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Contents

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1. Background

2. History of Exchanges

3. Ecosystem: trading costs

4. Algorithms

5. Issues of concern to long-term investors

6. Possible regulatory responses

7. Measuring market quality

8. Fragmenting liquidity

9. Dark Pools

10.Regulation / Evolution

11.MW Trading

12.Questions?

1. Background

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Investment strategy pyramid - Schematic

1. Background

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HFT activity by strategy type

2. History of Exchanges

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Specialist Model

• Example: NYSE

• People meet at a central point and trade blocks through a vocal negotiation process.

• The specialist holds orders for brokers on his/her book and will step in to provide liquidity to

dampen liquidity and trade for his/her own account.

• Conflicts of interest?

Quote driven Model

• Examples: NASDAQ (pre-SOES), LSE AIM

• Brokers post electronic indicative prices and sizes in a central place.

• Customers contact the brokers to “firm-up” their size and price.

• Customers often complain about “quote-fade”

Order driven Model

• Examples: LSE SETS, NYSE DOT, Chi-X Europe

• Firm orders are sent electronically to a book in which people can trade against, provided the

order has not been cancelled.

• Most exchanges now trade this way

2. History of Exchanges

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Timeline of milestones leading up to the rise of CBT

3. Ecosystem: trading costs

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Life cycle of a trade – A simplified view

3. Ecosystem: trading costs

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Commission / exchange fees / taxes

• We estimate about 10-20% of total trading cost for an institutional manager

Impact costs

• We estimate about 80-90% of total trading cost for an institutional manager

• Traders are trying to control impact costs:

Perceived impact: f(ADV%, volatility, aggression / participation rate)

Perceived alpha / toxicity

Perceived risk / time to completion

o Need to forecast volume

Perceived information leakage

Time of day

Trade sizes are generally made to reflect perceived alpha – perceived cost = net alpha

3. Ecosystem: trading costs

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Equity trading ecosystem (schematic)

4. Algorithms

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People use algorithms to save money:

• Break orders into smaller pieces, executed electronically

Anonymity (less information leakage)

Aggression control / more spread capture (don’t need instant risk price on entire piece)

• React automatically to changing market dynamics better than hiring humans

Types:

• Benchmark: Percentage of Volume, Vwap, Implementation Shortfall

• Liquidity Seeking: Dark Only, Aggressive inside a price (“I would”)

The voice trade The algorithmic trade

4. Algorithms

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Similarities and differences between HFT and AT

4. Algorithms

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Examples of predatory HFT strategies

5. Issues of concern to long-term investors

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Challenges in trading “HFT stocks”

“Phantom” vs. “real” liquidity

Order anticipation

“Mandatory fee” paid to market making HFTs

New order types

Less volume and heterogeneous trading

6. Possible regulatory responses

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Manage trade-off between cost and level of liquidity provisioning: Tick size policy

Increase order book execution predictability: Minimum execution ratios and

minimum resting times

Reduce systemic and endogenous risk: Circuit breakers and notification of

algorithms

Promote liquidity: Market-making obligations and regulation of internalisation

Reduce latency advantage of HFTs: Periodic call auctions

Reduce HFT role: Limit maker-taker prici9ng and introduction of financial

transaction tax

7. Measuring market quality

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8. Fragmenting Liquidity

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Why use other exchanges?

• Speed, ability to control orders (new and cancels) relative to aggression objectives and fair

value models

• Cost: rebates, fees, tick sizes / spreads

• “Jump the queue” – compete for better queue position at price level on different exchange

• “Hide in the weeds” – break up slices to anonymise repeated buying

Primary exchange

• Opening auction, Continuous trading, Closing auction

MTF / ECN / PTS

• Maker / taker pricing (inverted in

some cases!)

Dark pools

OTC

Primary -

Auction

4%

Primary -

Continuous

32%

Other Venues*

18%

Dark Pools

41%

High Touch

5%

Primary - Auction

1%

Primary -

Continuous

13%

Other Venues*

7%

Dark Pools

76%

High Touch

3%

Primary - Auction

9%

Primary -

Continuous

59%

Other Venues*

9%

Dark Pools

14%

High Touch

9%

– Direct Market Access connection to major exchanges

• Lower cost of execution, ca. <1bp per trade

• Provides access to alternative liquidity sources

• Approx. 15% reduction in slippage across EU

from 2009-2012

– Reduction in high touch trading has reduced execution costs

– Development of proprietary algorithms to leverage extensive

venue connectivity and quantitative research

8. Fragmenting liquidity

EU

Asia US

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Note: Data relates to notional value traded on a typical day in January 2013, for illustrative purposes only. *Multilateral Trading Facility (EU),

Electronic Communication Network (US), Proprietary Trading System (Asia).

Source: Marshall Wace LLP. Confidential to recipient; not for reproduction or redistribution. Please refer to final pages for Important

Disclosures.

9. Dark Pools

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Orders are conditional to trade on the other side being present in the pool – no one sees your order

otherwise

• Anonymity / less information leakage if no trade than a lit venue

Reference lit prices to determine crossing points

• Most commonly cross at mid-point, or near / far peg

• Can also cross at even tick increments if not mid-point

• Market data speed important for reference calculation

Broker Pools (BCS)

• Can segregate which flow to cross with (toxicity).

• Brokers can save from paying exchange fees

• Crossing clients can save costs by preventing

competition with intermediaries

• Examples: MS Pool, CS Crossfinder

MTFs / ATS

• Everyone can access, no segregation

• Examples: Chi-X Dark, Bats Dark, UBS MTF

10. Regulation / Evolution

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Flash Crash

• All regulators around the world focused on preventing again

Canada, Australia

• Must trade at a better price than the lit exchanges in dark pools (mid-point or better full tick

size)

Dodd Frank Impact

• Less trading activity from investment banking proprietary trading

Less active fund management by investors

• More use of futures and ETFs for benchmark tracking

Resistance of fixed income markets to exchanges

Italian transaction tax

• Less statistical arbitrage flow, market making still effectively exempt through net settlement tax

Germany on HFT

11. MW Trading

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Typical stages of investment process

• Portfolio manager: Fundamental: “stock is very cheap: we should buy and hold for a while”

• Trader: Statistical arbitrage / scheduling: “the stock is underperforming its pairs over the past few

hours, we should be more aggressive buying now before it reverts”

• Algorithm / broker: High Frequency / market making: “we can collect a little more edge by buying

a bit more than usual right now on the bid”

Integrating statistical arbitrage / high frequency stages into portfolio construction

• Constraints hamper the ability to trade more aggressively or even generate a trade at all

• Effectively accessing liquidity and feeding back to portfolio construction (aggression vs. collecting

edge).

• Portfolio construction dynamically drives aggression through its utility desires (alpha, risk).

More creative liquidity consumption process

o Sit in dark pools to help collect additional edge

o Delay trading for spread collection

o Delay trading for cheaper time of day (target aggression for certain times of day)

o Trading security synthetically

Aggression is constantly changing via urgency desires

12. Questions?

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Appendix

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Appendix

Appendix

References

• Norges Bank Investment Management Discussion Note #1-2013: “High Frequency Trading – An Asset

Manager’s Perspective” August, 2013

• Will Psomadelis, Schroders: “Know Your Counterparty: The New Paradigm of Equity Market

Microstructure and The Impact to Institutional Investors.” August, 2013.

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