The Science of Algorithmic Trading and Portfolio Management, Second 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
Dr. Robert Kissell is the president and founder of Kissell Research Group. He has over twenty years of experience specializing in economics, finance, math & statistics, risk, and sports modeling.
Dr. Kissell is author of the leading industry books, "The Science of Algorithmic Trading & Portfolio Management,? (Elsevier, 2013), "Multi-Asset Risk Modeling? (Elsevier, 2014), and "Optimal Trading Strategies,? (AMACOM, 2003). He has published numerous research papers on trading, electronic algorithms, risk management, and best execution. His paper, "Dynamic Pre-Trade Models: Beyond the Black Box,? (2011) won Institutional Investor's prestigious paper of the year award.
Dr. Kissell is an adjunct faculty member of the Gabelli School of Business at Fordham University and is an associate editor of the Journal of Trading and the Journal of Index Investing. He has previously been an instructor at Cornell University in their graduate Financial Engineering program.
Dr. Kissell has worked with numerous Investment Banks throughout his career including UBS Securities where he was Executive Director of Execution Strategies and Portfolio Analysis, and at JPMorgan 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.
During his college years, Dr. Kissell was a member of the Stony Brook Soccer Team and was Co-Captain in his Junior and Senior years. It was during this time as a student athlete where he began applying math and statistics to sports modeling problems. Many of the techniques discussed in "Optimal Sports Math, Statistics, and Fantasy? were developed during his time at Stony Brook, and advanced thereafter. Thus, making this book the byproduct of decades of successful research.
Dr. Kissell has a Ph.D. in Economics from Fordham University, an MS in Applied Mathematics from Hofstra University, an MS in Business Management from Stony Brook University, and a BS in Applied Mathematics & Statistics from Stony Brook University.