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The Science of Algorithmic Trading and Portfolio Management

Book Description

The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems.

This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects.

  • Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers.

    • Helps readers design systems to manage algorithmic risk and dark pool uncertainty.
    • Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. Chapter 1. Algorithmic Trading
    1. Introduction
    2. Changing Trading Environment
    3. Recent Growth in Algorithmic Trading
    4. Investment Cycle
    5. Classifications of Algorithms
    6. Types of Algorithms
    7. Algorithmic Trading Trends
    8. Trading Venue Classification
    9. Types of Orders
    10. Execution Options
    11. The Trading Floor
    12. Algorithmic Trading Decisions
    13. Algorithmic Analysis Tools
    14. High Frequency Trading
    15. Direct Market Access
  9. Chapter 2. Market Microstructure
    1. Introduction
    2. Market Microstructure Literature
    3. The New Market Structure
    4. Pricing Models
    5. Order Priority
    6. Equity Exchanges
    7. New NYSE Trading Model
    8. NASDAQ Select Market Maker Program
    9. Empirical Evidence
    10. Flash Crash
    11. Conclusion
  10. Chapter 3. Algorithmic Transaction Cost Analysis
    1. Introduction
    2. Unbundled Transaction Cost Components
    3. Transaction Cost Classification
    4. Transaction Cost Categorization
    5. Transaction Cost Analysis
    6. Implementation Shortfall
    7. Evaluating Performance
    8. Comparing Algorithms
    9. Experimental Design
    10. Final Note on Post-Trade Analysis
  11. Chapter 4. Market Impact Models
    1. Introduction
    2. Definition
    3. Graphical Illustrations of Market Impact
    4. Developing a Market Impact Model
    5. Derivation of Models
    6. I-Star Market Impact Model
    7. Model Formulation
    8. Parameter Estimation Techniques
  12. Chapter 5. Estimating I-Star Model Parameters
    1. Introduction
    2. Scientific Method
    3. Solution Technique
  13. Chapter 6. Price Volatility
    1. Introduction
    2. Definitions
    3. Market Observations—Empirical Findings
    4. Forecasting Stock Volatility
    5. HMA-VIX Adjustment Model
    6. Measuring Model Performance
    7. Factor Models
    8. Types of Factor Models
  14. Chapter 7. Advanced Algorithmic Forecasting Techniques
    1. Introduction
    2. Trading Cost Equations
    3. Trading Strategy
    4. Trading Time
    5. Trading Risk Components
    6. Trading Cost Models—Reformulated
    7. Timing Risk Equation
    8. Comparison of Market Impact Estimates
    9. Volume Forecasting Techniques
    10. Forecasting Monthly Volumes
    11. Forecasting Covariance
    12. Efficient Trading Frontier
  15. Chapter 8. Algorithmic Decision Making Framework
    1. Introduction
    2. Equations
    3. Algorithmic Decision Making Framework
  16. Chapter 9. Portfolio Algorithms
    1. Introduction
    2. Trader’s Dilemma
    3. Transaction Cost Equations
    4. Optimization Formulation
    5. Portfolio Optimization Techniques
    6. Portfolio Adaptation Tactics
    7. Managing Portfolio Risk
    8. Appendix
  17. Chapter 10. Portfolio Construction
    1. Introduction
    2. Portfolio Optimization and Constraints
    3. Transaction Costs in Portfolio Optimization
    4. Portfolio Management Process
    5. Trading Decision Process
    6. Unifying the Investment and Trading Theories
    7. Cost-Adjusted Frontier
    8. Determining the Appropriate Level of Risk Aversion
    9. Best Execution Frontier
    10. Portfolio Construction with Transaction Costs
    11. Conclusion
  18. Chapter 11. Quantitative Portfolio Management Techniques
    1. Introduction
    2. Are the Existing Models Useful Enough for Portfolio Construction?
    3. Pre-Trade of Pre-Trades
    4. How Expensive is it to Trade?
    5. MI Factor Scores
    6. Alpha Capture Program
  19. Chapter 12. Cost Index & Multi-Asset Trading Costs
    1. Introduction
    2. Cost Index
    3. Real-Time Cost Index
    4. Multi-Asset Class Investing
    5. Multi-Asset Trading Costs
  20. Chapter 13. High Frequency Trading and Black Box Models
    1. Introduction
    2. Data and Research
    3. Strategies
    4. Evaluation
    5. Summary
  21. References
  22. Index