- Inbunden (Hardback)
- Antal sidor
- Academic Press
- L.Kissell, Robert
- black & white line drawings, black & white tables, figures
- 234 x 190 x 25 mm
- Antal komponenter
- 3:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Gloss Lam
- 1179 g
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The Science of Algorithmic Trading and Portfolio Management
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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.
Fler böcker av Robert L Kissell
Recensioner i media
"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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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.
I - Introduction 1. Algorithmic Trading 2. Market Microstructure 3. Transaction Cost Analysis (TCA) II - Mathematical Modeling 4.. Market Impact 5. Multi-Asset Class Market Impact 6 Price 7. Algorithmic Trading Risk 8. Algorithmic Decision Making Framework 9. Portfolio Algorithms III - Portfolio Management 10. Portfolio Construction 11. Quant Factors 12. Black Box Models