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The Science of Algorithmic Trading and Portfolio Management

Author

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  • Kissell, Robert

    (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 currently an adjunct faculty member of the Gabelli School of Business at Fordham University, and has held several senior leadership positions with prominent bulge bracket Investment Banks.)

Abstract

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.

Suggested Citation

  • Kissell, Robert, 2013. "The Science of Algorithmic Trading and Portfolio Management," Elsevier Monographs, Elsevier, edition 1, number 9780124016897.
    • Handle: RePEc:eee:monogr:9780124016897
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    Citations

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    Cited by:

    1. Artur Sokolovsky & Luca Arnaboldi, 2020. "A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components," Papers 2009.09993, arXiv.org, revised Jun 2022.
    2. Ling Jia & Queena K. Qian & Frits Meijer & Henk Visscher, 2020. "Stakeholders’ Risk Perception: A Perspective for Proactive Risk Management in Residential Building Energy Retrofits in China," Sustainability, MDPI, vol. 12(7), pages 1-25, April.
    3. Eduard Hartwich & Alexander Rieger & Johannes Sedlmeir & Dominik Jurek & Gilbert Fridgen, 2023. "Machine economies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    4. Arumugam, Devika, 2023. "Algorithmic trading: Intraday profitability and trading behavior," Economic Modelling, Elsevier, vol. 128(C).
    5. Saerom Park & Jaewook Lee & Youngdoo Son, 2016. "Predicting Market Impact Costs Using Nonparametric Machine Learning Models," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    6. Thiago W. Alves & Ionut Florescu & George Calhoun & Dragos Bozdog, 2020. "SHIFT: A Highly Realistic Financial Market Simulation Platform," Papers 2002.11158, arXiv.org, revised Aug 2020.
    7. repec:zib:zbjtwe:v:2:y:2024:i:1:p:01-09 is not listed on IDEAS

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