The Science of Algorithmic Trading and Portfolio Management book cover

The Science of Algorithmic Trading and Portfolio Management

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.

Audience

Students and professors studying stock selection and portfolio management, as well as traders, practitioners, and portfolio managers working in the financial industry..

Hardbound, 496 Pages

Published: October 2013

Imprint: Academic Press

ISBN: 978-0-12-401689-7

Reviews

  • "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

Contents

  • Part I - Introduction

    1. Algorithmic Trading
      1. What is algorithmic trading?
      2. Types of algorithms
      3. Single Stock vs. Portfolio Algorithms, P&L Trading
      4. Algorithmic Trading Preferences
      5. Data requirements

    2. Market Microstructure
      1. New Trading Environment / Different Exchanges
      2. NYSE Listed vs. Nasdaq OTC Stocks
      3. Displayed Venues vs. Dark Pools
      4. Intraday Trading Patterns
      5. Maker Taker Pricing & Liquidity Rebates
      6. May 2010 Flash-Crash

    3. Transaction Cost Analysis (TCA)
      1. Unbundled Transaction Cost Components
      2. Pre-Trade Analysis
      3. Intra-Day Trading
      4. Post Trade Measurement
      5. Execution Consulting Services

      Part II - Mathematical Modeling

    4. Market Impact
      1. Definition
      2. Economic Description
      3. Random Walk with Market Impact
      4. I-Star Cost Allocation
      5. Underlying Dataset
      6. Estimating Parameters & Testing the Model
      7. Forecasting Market Impact
      8. Simulation of parameters - show how hard it is to fit a model
      9. Examples

    5. Multi-Asset Class Market Impact
      1. Equities
      2. Futures & ETFs
      3. Fixed Income
      4. Commodities
      5. FX Rates

    6. Price Volatility & Factor Models
      1. Volatility Models
      2. ARCH, GARCH, EWMA
      3. Factor Models
      4. Time Series
      5. Fundamental Factors
      6. Principal Component Analysis

    7. Algorithmic Trading Risk
      1. Price Risk
      2. Liquidity Risk
      3. Order Book & Limit Order Models
      4. Dark Pools
      5. Parameter Standard Errors
      6. Risk Simulation Example

    8. Algorithmic Decision Making Framework
      1. Macro, Micro, Order Placement Strategies
      2. Dark Pools, Limit Order Model, Smart Order Router
      3. Real-Time Adaptation Tactics
      4. Efficient Trading Frontier

    9. Portfolio Algorithms
      1. Formulating the Problems for a Basket
      2. Solution Speed & Techniques
      3. Constraints
      4. Real-Time Updatiing

    Part III - Portfolio Management

      10. .Portfolio Construction

        1. Mean Variance Optimization
        2. Unifying Investing and Trading
        3. Optimization with Market Impact
        4. Back-Testing approaches

      11. Quant Factors

        1. Stock Selection
        2. Maximum Capacity of a Strategy
        3. Liquidation of a Position
        4. Index Events and Corporate Actions

      12. Black Box Models

        1. Statistical Arbitrage
        2. Pairs Trading & Cointegration
        3. High Frequency Trading
          1. Liquidity Trading
          2. Market Making
          3. Rebates

        4. Advanced Topics

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