Contents -- About the Editors -- About the Contributors -- Foreword by -- Robert Almgren -- Bertrand Patillet -- Philippe Guillot -- Albert J. Menkveld -- Preface by -- Charles-Albert Lehalle -- Sophie Laruelle -- Introduction -- Liquidity in Question -- Microstructure from a Regulatory Standpoint -- A Recent Appetite of Regulators and Policy-Makers for Electronic Markets -- Worldwide Consolidation: An Electronic Trading Global Timeline -- Changes in the Microstructure -- Defining Best Execution -- Redefining the Roles: An Agent is Now Both Liquidity Taker and Liquidity Provider Simultaneously -- Towards a Paradigm Shift: A Blurring of Roles -- Impact on Liquidity -- 1. Monitoring the Fragmentation at Any Scale -- 1.1 Fluctuations of Market Shares: A First Graph on Liquidity -- 1.1.1 The market share: A not so obvious liquidity metric -- 1.1.2 Phase 1: First attempts of fragmentation -- 1.1.3 Phase 2: Convergence towards a European offer -- Entropy of the market microstructure -- Fragmentation of what? -- 1.1.4 Phase 3: Apparition of broker crossing networks and Dark Pools -- How HFT activity promoted trading outside of visible pools -- Small and mid-caps: Catching up main indexes -- From division to union -- 1.2 Smart Order Routing (SOR), A Structural Component of European Price Formation Process -- 1.2.1 How to route orders in a fragmented market? -- Focus on atomic orders -- Using a Smart Order Router (SOR) -- Aggregate liquidity to minimize the impact of each order -- Smart Order Routers or Smart Fee Savers? -- Beware of duplicate liquidity -- 1.2.2 Fragmentation is a consequence of primary markets' variance -- 1.3 Still Looking for the Optimal Tick Size -- 1.3.1 Why does tick size matter? -- 1.3.2 How tick size affects market quality -- Decreasing the tick size lowers spreads when tick size is a constraint.

Smaller and faster liquidity, does this means more unstable? -- Cumulative depth and order exposure incentive -- Queue jumping and the profitability of limit orders relative to marketable ones -- 1.3.3 How can tick size be used by trading venue to earn market share? -- Tick size war in the US -- Tick size war in Europe -- 1.3.4 How does tick size change the profitability of the various participants in the market? -- 1.3.5 The value of a quote -- 1.4 Can We See in the Dark? -- 1.4.1 Mechanism of dark liquidity pools -- 1.4.2 In-depth analysis of dark liquidity -- European Dark Pool market share -- Dark Pools and price discovery -- Main characteristics of dark liquidity -- Dark Pools risks and toxic liquidity -- 2. Understanding the Stakes and the Roots of Fragmentation -- 2.1 From Intraday Market Share to Volume Curves: Some Stationarity Issues -- 2.1.1 Inventory-driven investors need fixing auctions -- Fixing auctions: What's at stake -- Basic matching rules during call auctions -- Pre-fixing dynamics demystified -- 2.1.2 Timing is money: Investors need to trade accordingly -- Market design and information flow timing imply liquidity patterns -- Examples of mixed effects -- 1) Opening of the US market -- 2) US macroeconomic news -- 3) Equity derivatives expiries -- 2.1.3 Fragmentation and the evolution of intraday volume patterns -- 2.2 Does More Liquidity Guarantee a Better Market Share? A Little Story About the European Bid-Ask Spread -- 2.2.1 The bid-ask spread and volatility move accordingly -- 2.2.2 Bid-ask spread and market share are deeply linked -- 2.2.3 Exchanges need to show volatility-resistance -- 2.3 The Agenda of High Frequency Traders: How Do They Extend their Universe? -- 2.3.1 Metrics for the balance in liquidity among indexes -- 2.3.2 A history of coverage -- 2.3.3 High-frequency traders do not impact all investors equally.

How could HFT lower the spread without changing execution costs? -- Bid-ask spread: Cost and uncertainty for investors -- 2.4 The Link Between Fragmentation and Systemic Risk -- 2.4.1 The Spanish experiment -- 2.4.2 Volatility, cross-stock correlation, intraday, extraday -- The volatility of an index depends on the correlations among its components -- The cross-stock correlation and the volatility at short time scales -- Stylized fact: High correlation, high volatility -- High correlations during summer 2010 -- Increasing intraday correlation -- Overnight versus overday correlation -- 2.4.3 The Flash Crash (May 6th, 2010) in NY: How far are we from systemic risk? -- Example of the Flash Crash impact: Procter & Gamble -- A large order initiated by a fundamental trader lighted the Flash Crash -- The package of measures under examination or adopted by the SEC in the Flash Crash aftermath -- Outages in Europe -- 3. Optimal Organisations for Optimal Trading -- 3.1 Organising a Trading Structure to Answer to a Fragmented Landscape -- 3.1.1 Main inputs of trading tools -- The market data -- The connection to venues -- Historical data -- Models -- 3.1.2 Components of trading algorithms -- 3.1.3 Main outputs of an automated trading system -- Pre-trade analytics -- Monitoring indicators -- Performance indicators -- 'What if' scenarios? -- Where to produce real time indicators? -- Post-trade analysis -- Transaction Cost Analysis (TCA) -- 3.2 Market Impact Measurements: Understanding the Price Formation Process from the Viewpoint of One Investor -- 3.2.1 Better understanding on what impacts the price -- 3.2.2 Market impact over the trading period -- 3.2.3 Market impact on a longer horizon: Different patterns for different investment styles -- 3.2.4 Dependence between investment style and market impact on a monthly horizon -- 3.3 Optimal Trading Methods.

3.3.1 Algorithmic trading: Adapting trading style to investors' needs -- Each trading feature has its own benchmark -- Customization offers multi-feature trading styles -- 3.3.2 Liquidity seeking algorithms are no longer nice to have -- From Smart Order Routing to liquidity seeking -- A typical example of smart routing -- Seeking an optimal liquidity capturing scheme -- Example of a passive split -- Building a liquidity seeker -- Appendix A: Quantitative Appendix -- A.1 From Entropy to FEI (Fragmentation Efficiency Index) -- A.2 Information Seeking and Price Discovery -- A.3 A Simple Model Explaining the Natural Fragmentation of Market Microstructure -- A.3.1 A toy model of SOR dynamics -- A.3.2 A toy model of the impact of SOR activity on the market shares -- A.3.3 A coupled model of SOR-market shares dynamics -- A.3.4 Simulations -- A.3.5 Qualitative analysis -- A.4 A Toy Model of the Flash Crash -- A.4.1 A market depth-oriented model -- A.4.2 Impact of the Flash Crash on our model -- A.5 Harris Model: Underlying Continuous Spread Discretized by Tick -- Motivation and notations -- Numerical application -- A.6 Optimal Trade Scheduling -- A.6.1 The trading model -- A.6.2 Towards a mean-variance optimal trade scheduling -- A pure risk adverse trade schedule -- Combining market impact and risk: The mean-variance criterion -- A.7 Estimation of Proportion and its Confidence Intervals -- A.7.1 Application to the estimation of the market share of venues on an asset -- A.7.2 Aggregation or application to the market share on an index -- A.7.3 Comparison of the estimators -- A.8 Gini Coefficient and Kolmogorov-Smirnov Test -- A.8.1 Gini coefficient -- A.8.2 Kolmogorov-Smirnov test -- A.8.3 Practical implementation -- A.9 Simple Linear Regression Model -- A.9.1 Model presentation -- A.9.2 Application to relation between spread and volatility.

A.10 Time Series and Seasonalities -- A.10.1 Introduction to time series -- Application to deseasonality -- A.10.2 Example of volume model -- A.11 Clusters of Liquidity -- A.11.1 Introduction to point processes -- A.11.2 One-dimensional Hawkes processes -- A.12 Signature Plot and Epps Effect -- A.12.1 Volatility and signature plot -- A.12.2 Correlation and Epps effect -- A.13 Averaging Effect -- A.13.1 Mean versus path -- A.13.2 Regression of average quantities versus mean of the regressions -- Appendix B: Glossary -- Bibliography -- Index.