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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.
Students and professors studying stock selection and portfolio management, as well as traders, practitioners, and portfolio managers working in the financial industry..
Chapter 1. Algorithmic Trading
Changing Trading Environment
Recent Growth in Algorithmic Trading
Classifications of Algorithms
Types of Algorithms
Algorithmic Trading Trends
Trading Venue Classification
Types of Orders
The Trading Floor
Algorithmic Trading Decisions
Algorithmic Analysis Tools
High Frequency Trading
Direct Market Access
Chapter 2. Market Microstructure
Market Microstructure Literature
The New Market Structure
New NYSE Trading Model
NASDAQ Select Market Maker Program
Chapter 3. Algorithmic Transaction Cost Analysis
Unbundled Transaction Cost Components
Transaction Cost Classification
Transaction Cost Categorization
Transaction Cost Analysis
Final Note on Post-Trade Analysis
Chapter 4. Market Impact Models
Graphical Illustrations of Market Impact
Developing a Market Impact Model
Derivation of Models
I-Star Market Impact Model
Parameter Estimation Techniques
Chapter 5. Estimating I-Star Model Parameters
Chapter 6. Price Volatility
Market Observations—Empirical Findings
Forecasting Stock Volatility
HMA-VIX Adjustment Model
Measuring Model Performance
Types of Factor Models
Chapter 7. Advanced Algorithmic Forecasting Techniques
Trading Cost Equations
Trading Risk Components
Trading Cost Models—Reformulated
Timing Risk Equation
Comparison of Market Impact Estimates
Volume Forecasting Techniques
Forecasting Monthly Volumes
Efficient Trading Frontier
Chapter 8. Algorithmic Decision Making Framework
Algorithmic Decision Making Framework
Chapter 9. Portfolio Algorithms
Transaction Cost Equations
Portfolio Optimization Techniques
Portfolio Adaptation Tactics
Managing Portfolio Risk
Chapter 10. Portfolio Construction
Portfolio Optimization and Constraints
Transaction Costs in Portfolio Optimization
Portfolio Management Process
Trading Decision Process
Unifying the Investment and Trading Theories
Determining the Appropriate Level of Risk Aversion
Best Execution Frontier
Portfolio Construction with Transaction Costs
Chapter 11. Quantitative Portfolio Management Techniques
Are the Existing Models Useful Enough for Portfolio Construction?
Pre-Trade of Pre-Trades
How Expensive is it to Trade?
MI Factor Scores
Alpha Capture Program
Chapter 12. Cost Index & Multi-Asset Trading Costs
Real-Time Cost Index
Multi-Asset Class Investing
Multi-Asset Trading Costs
Chapter 13. High Frequency Trading and Black Box Models
Data and Research
- No. of pages:
- © Academic Press 2014
- 1st July 2013
- Academic Press
- Hardcover ISBN:
- eBook ISBN:
Robert Kissell, Ph.D., 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 at 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.”
President, Kissell Research Group; Professor, Molloy College; Adjunct Professor, Fordham University
"Kissell... introduces the mathematical models for constructing, calibrating, and testing market impact models that calculate the change in stock price caused by a large trade or order, and presents an advanced portfolio optimization process that incorporates market impact and transaction costs directly into portfolio optimization."--ProtoView.com, March 2014
"This book provides excellent coverage of the challenges faced by portfolio managers and traders in implementing investment ideas and the advanced modeling techniques to address these challenges."--Kumar Venkataraman, Southern Methodist University
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