Handbook of High Frequency Trading

Editor: Gregoriou, Greg
Publication Year: 2015
Publisher: Elsevier Science & Technology

Single-User Purchase Price: $150.00
Unlimited-User Purchase Price: $225.00
ISBN: 978-0-12-802205-4
Category: Business, Finance & Economics - Economics
Image Count: 72
Book Status: Available
Table of Contents

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

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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