The Science of Algorithmic Trading and Portfolio Management: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques

Kissell, Robert

ISBN 10: 0124016898 / ISBN 13: 9780124016897

Published by Academic Press, 2013

About this Item

Seller Inventory # 11994

Bibliographic Details

• Title: The Science of Algorithmic Trading and Portfolio Management: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques
• Publisher: Academic Press
• Publication Date: 2013
• Language: English
• ISBN 10: 0124016898
• ISBN 13: 9780124016897
• Binding: Hardcover
• Edition: 1st Edition
• Condition: Good
• Dimensions: 19.81W x 23.62H x 3.05D centimeters 19.81 centimeters width by 23.62 centimeters height by 3.05 centimeters depth
• Weight: 1179 grams

About this title

Synopsis

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.

About the Author

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.”