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Understanding Limit Order Books & Dark Pools in Financial Trading

Explore the dynamics of limit order books (LOBs) and dark pools in financial trading, analyzing market impact, hidden liquidity, statistical properties, and trading strategies.

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Understanding Limit Order Books & Dark Pools in Financial Trading

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  1. Outline • Limit Order Books • Iceberg Orders • Market Impact vs Time Priority • Statistical analysis of hidden liquidity • Dark Pools • Features, key properties, trading volume • Portfolio liquidation in dark pools

  2. Limit Order Books

  3. Limit Order Books • Almost all electronic exchanges are based on Limit Order Books (LOBs) • Market Orders: immediate execution • Limit Orders: stored in the LOB • Orders may be schielded from public view. • Orders are executed according to a set of Priority Rules: • Price Priority • Display Priority • Time Priority • Large orders (limit or market) move the market

  4. Limit Order Books

  5. Limit Order Books • Large Orders (both limit and market) move the market • Significant trading costs • Difficult to use limit orders („footprints“) • Possible ways to deal with this problem: • Split large order into several small orders • Use hidden/dark liquidity

  6. Hidden Liquidity • Increasing proportion of liquidity is hidden: reduced market impact • Dark pools: • Typically associated with a primary venue (e.g. LSE) • Trade settled only if matching liquidity is/becomes available • Iceberg orders: • Only a fraction of the order is openly displayed in the LOB • The hidden part loses its time priority • Uncertainty of execution

  7. Iceberg Orders

  8. Data Issues • No (Limited) real time data on hidden liquidity available • Reconciling trade and quote data, executed hidden liquidity may be detected (difficult!) • We used data provided by Deutsche Bank AG • One minute snapshots of the entire book (hidden and visible) • Stock Universe: S&P 500 • Period: November/December 2008.

  9. Sample Snapshot

  10. Statistical Properties of HL (based on work by Goekhan Cebiroglu and UH)

  11. Statistical Properties of Hidden Liquidity • Our empirical analysis was focussed on the following questions: • How much is hidden? • How much HL is in the spread/on top of the book? • Which macroscopic variables correlate with the HL volume in the spread/on top of the book? • Can we estimate the HL volume in the spread/on top of the book? • Hidden liquidity distribution in the spread

  12. How Much is Hidden? Hidden Liquidity Hidden Liquidity at Top of the Book Average Ratio of Posted HL Average Ratio of Posted HL at Top

  13. Probability of Finding an Empty Spread

  14. HL in the Spread • Averaged over the S&P 500 the spread is empty 70% of time • Small spreads (<1 tick, MSFT, EBAY, DELL): good chance of HL in spread • Medium spreads (1- 5 ticks, e.g. AMZN): empty > 70% of time • Larger spreads (5+ ticks, e.g. GOOG):good chance of HL in spread • What variables correlate with the HL volume in the spread?

  15. HL in the Spread: Explanatory Variables • Correlation of hidden liquidity volume with: • Average Spread: 0.474 • Average Price: 0.404 • 1/(Average Order Size): 0.374 • Average Daily Trading Volume (ADV): 0.223 • Average Trade Size: 0.084 • HL volume not well explained by either quantity (R^2 < 0.4)

  16. HL Ratio in the Spread: Explanatory Variables • Correlation of hidden liquidity ratio with: • Average Spread: 0.859 • Average Price: 0.755 • 1/(Average Order Size): NA • Average Daily Trading Volume (ADV): -0.212 • Average Trade Size: -0.322 • HL ratio well explained by average spread.

  17. HL Ratio in the Spread

  18. HL Concentration in the Spread

  19. HL in the Spread (AMZN)

  20. HL in the Spread (GOOG)

  21. HL in the Spread: Summary • Amount of HL in the spread not well explained by macroscopic variables • HL ratiowell estimated by average spread (plus ADV) • Hidden liquidity at the top of the book correlates strongly with • average trade size • average top-of-the book volume • inverse average price and spread

  22. Calibration Results for Apple: Parameter Setting • Calibration period: Jan 01, 2008 – Jan 15, 2008 • Submitted order size: 2000 Shares • Submission price level: best bid/ask • Trading horizon: 2 seconds • Top of the book size: 300 shares • We calibrated the order arrival rates as a function of market imbalance.

  23. Calibration Results for Apple: What to Expect • „Benchmark price“ for liquidity providers: spread to midpoint • Small spread: full display • Medium \Large spreads: • More liquidity on the opposite side: display • Much more liquidity on my side: hide • Similar effects, but amplified by spread

  24. Execution Profile

  25. Dark Pools

  26. „It‘s not dark yet, but it‘s getting there“ (Bob Dylan, 1997, „Not Dark Yet“ from the album „Love and Theft“) “I want to see it painted, painted black, Black as night, black as coal …” (Mick Jagger, 1966, from the Rolling Stones Song “Paint it Black”)

  27. Dark Pools • Opportunity to execute traded with reduced market impact • Trade is executed if/as soon as matching liquidity is/becomes available • Typically linked to a primary exchange • No price finding in dark pools • Dark pool trades are printed (with a delay) • DPs mostly operate in the US

  28. Classification of Dark Pools • Public Crossing Networks (Millenium, …) • No proprietary flow from the operator • Mostly continuous crossing at midpoint • Mostly no advertisement • Internalization Pools (CS: „Crossfinder“, GS: „Sigma X“, UBS: „PIN“) • Designed to internalize the operator‘s order flow („internal corssing“) • Ping Destinations (CITADEL, …) • Only IOC orders are accepted • Generally operated by big hedge funds

  29. Classification of Dark Pools • Exchange-Based Pools (NASDAQ Cross, NYSE Matchpoint…) • Registered by exchanges or „All Iceberg LOB“ • „All Iceberg LOB“ interacts with the regular LOB • Liquidity providers get paid • Consortium-Based Pools • „2nd Step Internalization“

  30. Volume Traded • Many DPs trade in sizes comparable with displayed markets • Broker-sponsored venues dominate, with 80% of all DP volume • 7%-8% of the total US volume trades in DPs (less exchange dark) • Some Venue Volumes: NASDAQ HIDDEN: 9.1% BATS BOLT: 8.7% NSYE ARCA HIDDEN: 3.4%

  31. Portfolio Liquidation using DPs (based on work by Peter Kratz and Thorsten Schoeneborn)

  32. Portfolio Liquidation Using DPs • Liquidation of a portfolio of 2 assets • Poorly diversified • Asset are not equally liquid • Place a large order in the DP • Execution in the DP leads to a more balanced position • Start liquidating position in the primary market

  33. Trading without DPs

  34. Trading with DPs

  35. Conclusion • Increasing interest in hidden liquidity and dark pools • The rise od DPs will change the nature of trading and the roles of prices • Regulators are aware of the downsides of DPs • Important field of future research in finance

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