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Portfolio Construction and Analytics

Author Dessislava A. Pachamanova , Frank J. Fabozzi
Release Date: 2016/04/01
ISBN: 9781118445594
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Book Description

A detailed, multi-disciplinary approach to investment analytics

Portfolio Construction and Analytics provides an up-to-date understanding of the analytic investment process for students and professionals alike. With complete and detailed coverage of portfolio analytics and modeling methods, this book is unique in its multi-disciplinary approach. Investment analytics involves the input of a variety of areas, and this guide provides the perspective of data management, modeling, software resources, and investment strategy to give you a truly comprehensive understanding of how today's firms approach the process.  Real-world examples provide insight into analytics performed with vendor software, and references to analytics performed with open source software will prove useful to both students and practitioners.

Portfolio analytics refers to all of the methods used to screen, model, track, and evaluate investments. Big data, regulatory change, and increasing risk is forcing a need for a more coherent approach to all aspects of investment analytics, and this book provides the strong foundation and critical skills you need.

  • Master the fundamental modeling concepts and widely used analytics
  • Learn the latest trends in risk metrics, modeling, and investment strategies
  • Get up to speed on the vendor and open-source software most commonly used
  • Gain a multi-angle perspective on portfolio analytics at today's firms

Identifying investment opportunities, keeping portfolios aligned with investment objectives, and monitoring risk and performance are all major functions of an investment firm that relies heavily on analytics output. This reliance will only increase in the face of market changes and increased regulatory pressure, and practitioners need a deep understanding of the latest methods and models used to build a robust investment strategy. Portfolio Construction and Analytics is an invaluable resource for portfolio management in any capacity.

Table of Contents

  1. The Frank J. Fabozzi Series
  2. Title Page
  3. Copyright
  4. Dedication
  5. Preface
    1. Central Themes
    2. Software
    3. Teaching
    4. Disclosure
  6. About the Authors
  7. Acknowledgments
    1. Chapter 1: Introduction to Portfolio Management and Analytics
      1. 1.1 Asset Classes and the Asset Allocation Decision
      2. 1.2 The Portfolio Management Process
      3. 1.3 Traditional versus Quantitative Asset Management
      4. 1.4 Overview of Portfolio Analytics
      5. 1.5 Outline of Topics Covered in the Book
  8. Part One: Statistical Models of Risk and Uncertainty
    1. Chapter 2: Random Variables, Probability Distributions, and Important Statistical Concepts
      1. 2.1 What Is a Probability Distribution?
      2. 2.2 The Bernoulli Probability Distribution and Probability Mass Functions
      3. 2.3 The Binomial Probability Distribution and Discrete Distributions
      4. 2.4 The Normal Distribution and Probability Density Functions
      5. 2.5 The Concept of Cumulative Probability
      6. 2.6 Describing Distributions
      7. 2.7 Dependence between Two Random Variables: Covariance and Correlation
      8. 2.8 Sums of Random Variables
      9. 2.9 Joint Probability Distributions and Conditional Probability
      10. 2.10 Copulas
      11. 2.11 From Probability Theory to Statistical Measurement: Probability Distributions and Sampling
    2. Chapter 3: Important Probability Distributions
      1. 3.1 Examples of Probability Distributions
      2. 3.2 Modeling Financial Return Distributions
      3. 3.3 Modeling Tails of Financial Return Distributions
    3. Chapter 4: Statistical Estimation Models
      1. 4.1 Commonly Used Return Estimation Models
      2. 4.2 Regression Analysis
      3. 4.3 Factor Analysis
      4. 4.4 Principal Components Analysis
      5. 4.5 Autoregressive Conditional Heteroscedastic Models
  9. Part Two: Simulation and Optimization Modeling
    1. Chapter 5: Simulation Modeling
      1. 5.1 Monte Carlo Simulation: A Simple Example
      2. 5.2 Why Use Simulation?
      3. 5.3 How Many Scenarios?
      4. 5.4 Random Number Generation
    2. Chapter 6: Optimization Modeling
      1. 6.1 Optimization Formulations
      2. 6.2 Important Types of Optimization Problems
      3. 6.3 A Simple Optimization Problem Formulation Example: Portfolio Allocation
      4. 6.4 Optimization Algorithms
      5. 6.5 Optimization Software
      6. 6.6 A Software Implementation Example
    3. Chapter 7: Optimization under Uncertainty
      1. 7.1 Dynamic Programming
      2. 7.2 Stochastic Programming
      3. 7.3 Robust Optimization
  10. Part Three: Three Portfolio Theory
    1. Chapter 8: Asset Diversification
      1. 8.1 The Case for Diversification
      2. 8.2 The Classical Mean-Variance Optimization Framework
      3. 8.3 Efficient Frontiers
      4. 8.4 Alternative Formulations of the Classical Mean-Variance Optimization Problem
      5. 8.5 The Capital Market Line
      6. 8.6 Expected Utility Theory
      7. 8.7 Diversification Redefined
    2. Chapter 9: Factor Models
      1. 9.1 Factor Models in the Financial Economics Literature
      2. 9.2 Mean-Variance Optimization with Factor Models
      3. 9.3 Factor Selection in Practice
      4. 9.4 Factor Models for Alpha Construction
      5. 9.5 Factor Models for Risk Estimation
      6. 9.6 Data Management and Quality Issues
      7. 9.7 Risk Decomposition, Risk Attribution, and Performance Attribution
      8. 9.8 Factor Investing
    3. Chapter 10: Benchmarks and the Use of Tracking Error in Portfolio Construction
      1. 10.1 Tracking Error versus Alpha: Calculation and Interpretation
      2. 10.2 Forward-Looking versus Backward-Looking Tracking Error
      3. 10.3 Tracking Error and Information Ratio
      4. 10.4 Predicted Tracking Error Calculation
      5. 10.5 Benchmarks and Indexes
      6. 10.6 Smart Beta Investing
  11. Part Four: Equity Portfolio Management
    1. Chapter 11: Advances in Quantitative Equity Portfolio Management
      1. 11.1 Portfolio Constraints Commonly Used in Practice
      2. 11.2 Portfolio Optimization with Tail Risk Measures
      3. 11.3 Incorporating Transaction Costs
      4. 11.4 Multiaccount Optimization
      5. 11.5 Incorporating Taxes
      6. 11.6 Robust Parameter Estimation
      7. 11.7 Portfolio Resampling
      8. 11.8 Robust Portfolio Optimization
    2. Chapter 12: Factor-Based Equity Portfolio Construction and Performance Evaluation
      1. 12.1 Equity Factors Used in Practice
      2. 12.2 Stock Screens
      3. 12.3 Portfolio Selection
      4. 12.4 Risk Decomposition
      5. 12.5 Stress Testing
      6. 12.6 Portfolio Performance Evaluation
      7. 12.7 Risk Forecasts and Simulation
  12. Part Five: Fixed Income Portfolio Management
    1. Chapter 13: Fundamentals of Fixed Income Portfolio Management
      1. 13.1 Fixed Income Instruments and Major Sectors of the Bond Market
      2. 13.2 Features of Fixed Income Securities
      3. 13.3 Major Risks Associated with Investing in Bonds
      4. 13.4 Fixed Income Analytics
      5. 13.5 The Spectrum of Fixed Income Portfolio Strategies
      6. 13.6 Value-Added Fixed Income Strategies
    2. Chapter 14: Factor-Based Fixed Income Portfolio Construction and Evaluation
      1. 14.1 Fixed Income Factors Used in Practice
      2. 14.2 Portfolio Selection
      3. 14.3 Risk Decomposition
    3. Chapter 15: Constructing Liability-Driven Portfolios
      1. 15.1 Risks Associated with Liabilities
      2. 15.2 Liability-Driven Strategies of Life Insurance Companies
      3. 15.3 Liability-Driven Strategies of Defined Benefit Pension Funds
  13. Part Six: Derivatives and Their Application to Portfolio Management
    1. Chapter 16: Basics of Financial Derivatives
      1. 16.1 Overview of the Use of Derivatives in Portfolio Management
      2. 16.2 Forward and Futures Contracts
      3. 16.3 Options
      4. 16.4 Swaps
    2. Chapter 17: Using Derivatives in Equity Portfolio Management
      1. 17.1 Stock Index Futures and Portfolio Management Applications
      2. 17.2 Equity Options and Portfolio Management Applications
      3. 17.3 Equity Swaps
    3. Chapter 18: Using Derivatives in Fixed Income Portfolio Management
      1. 18.1 Controlling Interest Rate Risk Using Treasury Futures
      2. 18.2 Controlling Interest Rate Risk Using Treasury Futures Options
      3. 18.3 Controlling Interest Rate Risk Using Interest Rate Swaps
      4. 18.4 Controlling Credit Risk with Credit Default Swaps
  14. Appendix: Basic Linear Algebra Concepts
    1. A.1 Systems of Equations
    2. A.2 Vectors and Matrices
    3. A.3 Matrix Algebra
    4. A.4 Important Definitions
  15. References
    1. Index
  16. End User License Agreement