Technology and Models for Quantifying the Benefits and Costs of Trading in Dark Pools

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1 Technology and Models for Quantifying the Benefits and Costs of Trading in Dark Pools Richard Coggins May, 2012

2 Overview Analysing infrastructure and modelling requirements for trading in the dark Qualifying a return on investment is dark pool trading beneficial for all market participants? The future of dark pool trading in Australia

3 Types of Dark Pool in the Australian Landscape Client Gateway Algorithmic Platform Broker Dark Pool SOR Lit exchanges with dark order types Chi-X ASX Trade Match ASX Pure Match ASX-Centre Point Exchange dark pool Third party block pool Liquidnet ASX Volume Match Exchange block dark pool Third party blocks and electronic flow Dark Aggregators

4 Growth of Centre Point ASX Centre Point - Daily Trade Value and Market Share Millions (AUD) $4,500 $4,000 $3,500 $3,000 $2,500 $2,000 $1,500 Centre Point Crossing Centre Point Trade CP % of Overall Market 5.00% 4.50% 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% $1, % $ % $0 Source: Citi Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11 Jan-12 Feb-12 Mar-12 Apr % Centre point supports both crossings and mid-point trades Centre point has grown to 4.5% of ASX on market traded value

5 Centre Point Trading ex Crosses 50% Type of stocks traded most in CentrePoint - By spread Percentage of Total Traded Value in Centre Point 45% 40% 35% 30% 25% 20% 15% 10% 5% 4% 14% 42% 39% 0% Source: Citi Stock Spread Range (bps) Source: Cit i Citi Algorithmic Trading in Centre Point - Price Improvement 5.00 Price Improvement (bps) Source: Citi Stock Spread Range (bps) Source: Citi

6 Infrastructure for Dark Pool Trading Connectivity Support for venue order types Support for venue specific pricing and matching protocols Smart order router SOR enabled algorithms

7 Modelling dark pool trading: Transaction Cost Analysis Infrastructure Market Data Platform DMA Platform SOR/Algo/ Platform Critical capabilities: Break down by venue Capturing the complete relevant state history of orders Tick Data Base Execution Data Base State Data Base Synchronisation across data sources from various systems (market data versus execution venues) Consistency of information representation over time Transaction Cost Analysis System

8 Incorporating dark trading into transaction cost models Carry out a regression analysis, modelling a trading bench mark distribution as a function of factors capturing the trading conditions and constraints: Trading Bench Mark = F(parent order, stock characteristics, market conditions, venue activity) Typical Benchmarks: Interval VWAP, Implementation ShortFall, PWP20, Parent order Size, constraints, routing instructions Stock characteristics: Spread, intraday price volatility, intra-day volume profile, intra day volume volatility, average daily volume Market conditions: Buy/Sell imbalance, volume and price trends Venue activity: Level/type of trading activity in the (dark) venue compared to rest of the market

9 Potential savings and potential costs of Dark Pool trading Potential sources of savings Spread capture (price improvement) Queue jumping Market Impact reductions/liquidity discovery Reduced information leakage/signalling Venue trading fees Market supervision fees (reduced messaging count and or/trade count) Potential sources of costs Toxicity/ Gaming Information leakage Venue trading fees

10 Is dark pool trading beneficial for all market participants? Types of participants Retail, Insto long only, hedge fund, HFT Types of dark pools Block, manual intervention, electronic, public, third party or broker, order types Implications from modelling Spread capture and queue jumping advantageous to most except those who are latency sensitive and can t afford the opportunity cost of dark probing in their executions If trade size is greater than significant fraction of ADV, in which case market impact is the main consideration, not spread capture Relative trade size and access Some pools are buy side only Some pools may face cost hurdles for retail brokers to access if vendors don t support Some pools are exclusively broker internal pools Some pools no clear idea of what type of flow may be in there Some pools may lack protections/policing

11 The future of dark pool trading in Australia: Regulatory trends Global regulatory trends Consolidated tape (post trade transparency) Licensing of venues Price improvement Trade sizes (concerns around pre-trade transparency) Local regulatory trends ASIC CP145 and CP168 Requirement for price improvement Tiering of block thresholds Trade size thresholds not being considered for the time being Continued monitoring of any deterioration in pre-trade transparency Possible impact of revision to price tick regime Licensing discussion on-going

12 The future of dark pool trading in Australia: Innovation trends Growth in the number of dark pools, now 15. Continued growth in algorithmic product usage and SOR. Strong uptake of co-location facilities in ASX and Chi-X Increasing market share of ASX Centre Point Liquidnet, no take up of Volume match, Chi-East (Pan Asian dark pool) closing May 24 th. Dark order types in lit exchanges. E.g. Chi-X mid peg Likely outcomes: Price improvement rules will reduce some types of liquidity provision to dark pools Price improvement rules will encourage risk taking on executions to increase trade sizes to meet size thresholds Tiering rules will encourage increased smaller blocks How many dark pools can Aus market sustain? (Dark pool access agreements and aggregation)

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