Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques, 2nd Edition focuses on trading strategies and methods, including new insights on the evolution of financial markets, pre-trade models and post-trade analysis, liquidation cost and risk analysis required for regulatory reporting, and compliance and regulatory reporting requirements. Highlighting new investment styles, it adds new material on best execution processes for investors and brokers, including model validation, quality and assurance, limit order model testing, and smart order model testing. Using basic programming tools, such as Excel, MATLAB, and Python, this book provides a process to create TCA low cost exchange traded funds.
- Provides insights into all necessary components of algorithmic trading, including transaction costs analysis, market impact, risk and optimization, and a thorough and detailed discussion of trading algorithms
- Includes increased coverage of mathematics, statistics and machine learning
- Presents broad coverage of Alpha Model construction
1. New Financial Markets 2. Algorithmic Trading 3. Market Microstructure 4. Transaction Cost Analysis 5. Market Impact Models 6. Estimating I-Star Model Parameters 7. Volatility and Risk Models 8. Advanced Forecasting Techniques - "Volume Forecasting Models" 9. Algorithmic Decision-Making Framework 10. Portfolio Algorithms & Trade Schedule Optimization 11. Pre-Trade and Post-Trade Models 12. Liquidation Cost Analysis 13. Compliance and Regulatory Reporting 14. Portfolio Construction 15. Quantitative Portfolio Management Techniques 16. Multi-Asset Trading Costs, ETFs, Fixed Income, etc. 17. High Frequency Trading and Black Box Models 18. Cost Index
Historical TCA Patterns, Costs by Market Cap, and Investment Style 19. TCA with Excel, MATLAB, & Python 20. Advanced Topics
TCA ETFs, Stat Arb, Liquidity Trading 21. Best Execution Process
Model Validation, and Best Execution Process for Brokers and for Investors
Robert Kissell, PhD, is President of Kissell Research Group, a global financial and economic consulting firm specializing in quantitative modeling, statistical analysis, and algorithmic trading. He is also a professor at Molloy College in the School of Business and an adjunct professor at the Gabelli School of Business at Fordham University. He has held several senior leadership positions with prominent bulge bracket investment banks, including UBS Securities where he was Executive Director of Execution Strategies and Portfolio Analysis, and JP Morgan where he was Executive Director and Head of Quantitative Trading Strategies. He was previously at Citigroup/Smith Barney where he was Vice President of Quantitative Research, and at Instinet where he was Director of Trading Research. He began his career as an economic consultant at R.J. Rudden Associates specializing in energy, pricing, risk, and optimization. Dr. Kissell has written several books and published dozens of journal articles on algorithmic trading, risk, and finance. He is a coauthor of the CFA Level III reading titled "Trade Strategy and Execution, CFA Institute 2019.