The Science of Algorithmic Trading and Portfolio Management

The Science of Algorithmic Trading and Portfolio Management

Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques

1st Edition - July 1, 2013

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  • Author: Robert Kissell
  • eBook ISBN: 9780124016934
  • Hardcover ISBN: 9780124016897

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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.

Key Features

  • 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..

    Table of Contents

    • Dedication



      Chapter 1. Algorithmic Trading


      Changing Trading Environment

      Recent Growth in Algorithmic Trading

      Investment Cycle

      Classifications of Algorithms

      Types of Algorithms

      Algorithmic Trading Trends

      Trading Venue Classification

      Types of Orders

      Execution Options

      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

      Pricing Models

      Order Priority

      Equity Exchanges

      New NYSE Trading Model

      NASDAQ Select Market Maker Program

      Empirical Evidence

      Flash Crash


      Chapter 3. Algorithmic Transaction Cost Analysis


      Unbundled Transaction Cost Components

      Transaction Cost Classification

      Transaction Cost Categorization

      Transaction Cost Analysis

      Implementation Shortfall

      Evaluating Performance

      Comparing Algorithms

      Experimental Design

      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

      Model Formulation

      Parameter Estimation Techniques

      Chapter 5. Estimating I-Star Model Parameters


      Scientific Method

      Solution Technique

      Chapter 6. Price Volatility



      Market Observations—Empirical Findings

      Forecasting Stock Volatility

      HMA-VIX Adjustment Model

      Measuring Model Performance

      Factor Models

      Types of Factor Models

      Chapter 7. Advanced Algorithmic Forecasting Techniques


      Trading Cost Equations

      Trading Strategy

      Trading Time

      Trading Risk Components

      Trading Cost Models—Reformulated

      Timing Risk Equation

      Comparison of Market Impact Estimates

      Volume Forecasting Techniques

      Forecasting Monthly Volumes

      Forecasting Covariance

      Efficient Trading Frontier

      Chapter 8. Algorithmic Decision Making Framework



      Algorithmic Decision Making Framework

      Chapter 9. Portfolio Algorithms


      Trader’s Dilemma

      Transaction Cost Equations

      Optimization Formulation

      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

      Cost-Adjusted Frontier

      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


      Cost Index

      Real-Time Cost Index

      Multi-Asset Class Investing

      Multi-Asset Trading Costs

      Chapter 13. High Frequency Trading and Black Box Models


      Data and Research






    Product details

    • No. of pages: 496
    • Language: English
    • Copyright: © Academic Press 2013
    • Published: July 1, 2013
    • Imprint: Academic Press
    • eBook ISBN: 9780124016934
    • Hardcover ISBN: 9780124016897

    About the Author

    Robert Kissell

    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.”

    Affiliations and Expertise

    President, Kissell Research Group; Professor, Molloy College; Adjunct Professor, Fordham University

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