Reveals the mechanics of high frequency trading markets while including the econometrics of the modeling process.

Table of Contents

• List of Contributors
• Contributors Biographies
• Editor Biography
• Acknowledgments
• Introduction
• Part 1 Trading Activity
• 1. High-Frequency Activity on NASDAQ
• 1.1 Introduction
• 1.2 Data
• 1.3 Results
• 1.3.1 Intraday Overall Message Activity
• 1.3.2 Algorithmic Activity
• 1.3.3 News Events
• 1.4 Conclusion
• Acknowledgments
• References
• 2. The Profitability of High-Frequency Trading: Is It for Real?
• 2.1 Introduction
• 2.2 Definition and Characteristics of HFT
• 2.3 What Constitutes HFT?
• 2.4 The Profitability of HFT
• 2.5 Profitability as a Function of the Holding Period
• 2.6 Methodology
• 2.7 Data and Empirical Results
• 2.8 Conclusion
• References
• 3. Data Characteristics for High-Frequency Trading Systems
• 3.1 Introduction
• 3.2 Literature Review
• 3.2.1 The FX Markets
• 3.2.2 Testing for Market Efficiency
• 3.2.3 Testing for Randomness
• 3.2.4 Testing for Serial Dependence
• 3.3 Methodology
• 3.4 Analysis of Data
• 3.4.1 Tests for Market Efficiency
• 3.4.2 Test for Randomness
• 3.4.3 Tests for Serial Dependence
• 3.5 Conclusion
• Acknowledgments
• References
• 4. The Relevance of Heteroskedasticity and Structural Breaks when Testing for a Random Walk with High-Frequency Financial Data: Evidence from ASEAN Stock Markets
• 4.1 Introduction
• 4.2 Method
• 4.3 Data
• 4.4 Results
• 4.5 Discussion
• 4.6 Conclusion
• References
• 5. Game Theoretical Aspects of Colocation in High-Speed Financial Markets
• 5.1 Introduction
• 5.2 Literature and Structure of the Chapter
• 5.3 Colocation and Latency Reduction
• 5.3.1 Information Asymmetries and Order Matching
• 5.3.2 Current Trends in Latency Reduction
• 5.3.3 Colocation as a Specific Form of Latency Reduction
• 5.4 Empirical Evidence: Technical Arbitrage through Latency Reduction
• 5.4.1 Price, Adjustments, Epps Effect, and Arbitrage
• 5.4.2 Technical Arbitrage Opportunities and HFT
• 5.5 Modeling Strategic Choices on Colocation
• 5.5.1 The Rationale for Colocation
• 5.5.2 Prisoner's Dilemma
• 5.5.3 Hawk–Dove Game
• 5.6 Discussion: Evolutionary Optimization and Spatial Dynamics
• 5.6.1 Optimal Behavior of Colocating Firms—an Analytic Approach
• 5.6.2 Spatial Consequences; von Thünen and beyond
• 5.7 Conclusion, Limitations, and Implications for Money Managers
• References
• 6. Describing and Regulating High-Frequency Trading: A European Perspective
• 6.1 Introduction
• 6.2 HFT Description and Drivers
• 6.3 High Frequency Trading versus Algorithmic Trading
• 6.4 Strategies of HFT
• 6.5 Characteristics of AT and HFT
• 6.6 About the Concept of Liquidity
• 6.7 HFT and Flash Crashes
• 6.8 MiFID II and HFT Regulation in the EU
• References
• Part 2 Evolution and the Future
• 7. High-Frequency Trading: Implications for Market Efficiency and Fairness
• 7.1 Introduction
• 7.2 Nature of HFT and Recent Trends
• 7.3 Some Salient Issues Related to HFT
• 7.3.1 Market Efficiency
• 7.3.2 Price Discovery Process and HFT
• 7.3.3 Price Volatility
• 7.3.4 HFT, “Flash Crash,” and Dark Pools
• 7.4 HFT and “Fairness”
• 7.4.1 What constitutes HFT?
• 7.4.2 What are the criteria to be used to judge fairness?
• 7.4.3 How fair are the HFT practices?
• 7.5 Concluding Remarks
• References
• 8. Revisioning Revisionism: A Glance at HFT's Critics
• 8.1 Introduction: High-Frequency Trading Under Siege
• 8.2 The Lewis Debate in Context
• 8.2.1 Dodd–Frank
• 8.2.2 Technical Problems and Public Confidence in Securities Markets
• 8.2.3 New York Attorney General Eric Schneiderman Goes on the Attack
• 8.2.4 Department of Justice, Federal Bureau of Investigation, and Commodity Futures Trading Commission Concerns
• 8.2.5 Senate Hearings
• 8.2.6 Flash Boys in the SEC: A Chilly Reception
• 8.2.7 Summary
• 8.3 An HFT Tableau: Perception versus Reality
• 8.3.1 HFT and the “Wall Street” Myth
• 8.3.2 Are HFTRs Modern-Day Robber Barons?
• 8.3.3 Is HFT Speed Purely Value Destructive?
• 8.3.4 HFT and Institutional Execution Costs
• 8.3.4.1 Do HFTRs Increase Price Slippage when Institutional Blocks are Traded?
• 8.3.4.2 Do HFTRs Increase Adverse Selection Costs?
• 8.3.4.3 Latency Arbitrage
• 8.3.4.4 Order Anticipation
• 8.3.4.5 The Growth of Dark Pools
• 8.3.4.6 Vanishing Liquidity
• 8.3.4.7 An Empirical Weighing
• 8.3.5 HFT and Retail Investors
• 8.3.6 HFT and Market Crashes
• 8.4 Conclusion
• References
• 9. High-Frequency Trading: Past, Present, and Future
• 9.1 Introduction
• 9.2 The Origins of HFT
• 9.3 HFT Today
• 9.3.1 The Regulators Perspective
• 9.3.2 Key Strategies
• 9.3.2.1 Market-Making/Electronic Liquid Provision
• 9.3.2.2 Relative Value Arbitrage
• 9.3.2.3 Predatory Strategies
• 9.4 HFT Going Forward
• 9.5 Hedge Funds
• 9.6 Conclusion
• References
• 10. High-Frequency Trading and Its Regulation in the Australian Equity Markets
• 10.1 Introduction
• 10.2 Regulatory Response
• 10.3 Conclusion
• References
• 11. Global Exchanges in the HFT Nexus
• 11.1 Introduction
• 11.2 The Nexus of an Exchange
• 11.3 Exchanges and Their Customers
• 11.3.1 The Views of Customers
• 11.3.2 Exchanges Compete for Customers
• 11.4 Regulators and Exchanges
• 11.4.1 Canada Proceeds Cautiously
• 11.4.2 India Aims to Increase Regulation of HFT
• 11.4.3 Australia's Regulator Aims for Balance
• 11.4.4 The United Kingdom and Europe Follow Different Regulatory Paths
• 11.4.5 Growing Threats to HFT in the United States
• 11.5 Conclusion
• 11.5.1 Implications
• Acknowledgments
• References
• Part 3 Liquidity and Execution
• 12. Liquidity: Systematic Liquidity, Commonality, and High-Frequency Trading
• 12.1 Introduction
• 12.2 High-Frequency Trading and Liquidity
• 12.3 An Empirical Study of Equity Market Liquidity
• 12.3.1 Measures of Liquidity for Individual Stocks
• 12.3.2 Estimators for Systematic Liquidity and Commonality
• 12.4 Data
• 12.5 Statistical Results
• 12.6 Concluding Thoughts
• References
• 13. We Missed It Again! Why Do So Many Market Orders in High-Frequency FX Trading Fail to be Executed?
• 13.1 Introduction
• 13.2 The Structure of the EBS FX Market
• 13.3 Aggressive IOC Orders
• 13.3.1 Hourly Decomposition of Market-Order Failures
• 13.3.2 Episodes
• 13.3.3 Runs Tests
• 13.4 Conclusion
• Acknowledgments
• References
• 14. Efficient Performance Evaluation for High-Frequency Traders
• 14.1 Introduction
• 14.2 The Model
• 14.2.1 The Call for an Efficient Sharpe Ratio
• 14.2.2 Construction of Subordinate Trading Time for HFT
• 14.2.3 Application of ESR to HFT Risk and Return Data
• 14.3 Conclusion
• Acknowledgments
• References
• Part 4 Impact of News Releases
• 15. Do High Frequency Traders Care about Earnings Announcements? An Analysis of Trading Activity before, during, and after Regular Trading Hours
• 15.1 Introduction
• 15.2 High Frequency Trading
• 15.3 Related Literature
• 15.4 Data
• 15.5 Results
• 15.6 Conclusion
• References
• 16. Why Accountants Should Care about High Frequency Trading
• 16.1 Introduction
• 16.1.1 Organizational Trust
• 16.1.2 System Complexity
• 16.1.3 Individual Responsibility
• 16.2 Internal controls and tone at the top
• 16.3 Conclusion
• Acknowledgment
• References
• 17. High-Frequency Trading under Information Regimes
• 17.1 Introduction
• 17.2 Data
• 17.3 Methodology and Results
• 17.3.1 Information Regimes
• 17.3.2 Regime Identification
• 17.3.2.1 Limit Order Book Alignment
• 17.3.2.2 Frequency Decomposition by Discrete Wavelet Transform
• 17.3.2.3 Two-Sample Kolmogorov–Smirnov Test
• 17.3.3 Markov Switching Model
• 17.4 High-Frequency Trading Strategies
• 17.4.1 Limit Order Position and Size
• 17.4.2 Strategy for Entry and Exit
• 17.5 Conclusion
• References
• 18. Effects of Firm-Specific Public Announcements on Market Dynamics: Implications for High-Frequency Traders
• 18.1 Introduction
• 18.2 Data and Methodology
• 18.3 Empirical Results
• 18.4 Implications for HFT
• 18.5 Conclusion
• Acknowledgments
• References
• 19. Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Real-Time High-Frequency Sentiment Series
• Acknowledgments
• 19.1 Introduction
• 19.2 Research Methods and Data
• 19.2.1 News Sentiment
• 19.2.2 Our Sample Characteristics and Preliminary Analysis
• 19.2.2.1 Volatility Models Utilized
• 19.3 The Significance of the Sentiment Scores in the GARCH Analysis of DJIA Return Series
• 19.4 Conclusion
• References
• Part 5 Impact of Volatility
• 20. High-Frequency Technical Trading: Insights for Practitioners
• 20.1 Introduction
• 20.2 The Trading Rule Methodology
• 20.2.1 Trading Rule Definitions
• 20.2.2 Trading Rule Profitability
• 20.3 Data
• 20.4 Results
• 20.5 Conclusion
• References
• 21. High-Frequency News Flow and States of Asset Volatility
• 21.1 Introduction
• 21.2 Data and Sample
• 21.2.1 Introduction to RavenPack
• 21.2.2 RavenPack Scores
• 21.3 Data and Sample
• 21.3.1 Return Series
• 21.3.2 News Variables
• 21.4 Methodology and Model Specification
• 21.4.1 The MRS-GARCH Model
• 21.4.2 Discrete Choice Model
• 21.5 Empirical Results and Implications
• 21.5.1 Descriptive Statistics of the Dataset
• 21.5.2 Estimates of the MRS-GARCH Model
• 21.5.3 News Effects on States of Stock Return Volatility
• 21.5.3.1 Dummy News Variables on Full Datasets
• 21.5.3.2 Number of News Variables on Constrained Dataset
• 21.6 Conclusion and Implications
• Appendix A: Dow Jones Composite Average 65 Stocks
• Appendix B: RavenPack Algorithms
• References
• 22. News Releases and Stock Market Volatility: Intraday Evidence from Borsa Istanbul
• 22.1 Introduction
• 22.2 Model Specification and Data
• 22.3 Results and Discussion
• 22.3 Conclusion
• References
• 23. The Low-Risk Anomaly Revisited on High-Frequency Data
• 23.1 Introduction
• 23.2 Literature Review
• 23.2.1 The Low-Risk Anomaly
• 23.2.2 The Use of High-Frequency Data in Risk-Return Analysis and Portfolio Allocation
• 23.3 Methodology
• 23.3.1 Portfolio Construction
• 23.3.2 Risk Measures Used for Portfolio Allocation
• 23.3.3 Out-of-Sample Performance Evaluation
• 23.4 Investment Universe and Data Collection
• 23.5 Findings
• 23.6 Conclusion
• Appendix 1
• Appendix 2
• Acknowledgments
• References
• 24. Measuring the Leverage Effect in a High-Frequency Trading Framework
• 24.1 Introduction
• 24.2 Model Setting
• 24.3 Computation of Leverage Using Fourier Methodology
• 24.4 Numerical Results
• 24.5 Conclusion
• Acknowledgments
• References