Quantitative Equity Investing: Techniques and Strategies
A comprehensive look at the tools and techniques used in quantitative equity management
Some books attempt to extend portfolio theory, but the real issue today relates to the practical implementation of the theory introduced by Harry Markowitz and others who followed. The purpose of this book is to close the implementation gap by presenting state-of-the art quantitative techniques and strategies for managing equity portfolios.
Throughout these pages, Frank Fabozzi, Sergio Focardi, and Petter Kolm address the essential elements of this discipline, including financial model building, financial engineering, static and dynamic factor models, asset allocation, portfolio models, transaction costs, trading strategies, and much more. They also provide ample illustrations and thorough discussions of implementation issues facing those in the investment management business and include the necessary background material in probability, statistics, and econometrics to make the book self-contained.
- Written by a solid author team who has extensive financial experience in this area
- Presents state-of-the art quantitative strategies for managing equity portfolios
- Focuses on the implementation of quantitative equity asset management
- Outlines effective analysis, optimization methods, and risk models
In today's financial environment, you have to have the skills to analyze, optimize and manage the risk of your quantitative equity investments. This guide offers you the best information available to achieve this goal.
About the Authors.
Chapter 1 Introduction.
In Praise of Mathematical Finance.
Studies of the Use of Quantitative Equity Management.
Looking Ahead for Quantitative Equity Investing.
Chapter 2 Financial Econometrics I: Linear Regressions.
Covariance and Correlation.
Regressions, Linear Regressions, and Projections.
Robust Estimation of Regressions.
Classification and Regression Trees.
Chapter 3 Financial Econometrics II: Time Series.
Stable Vector Autoregressive Processes.
Integrated and Cointegrated Variables.
Estimation of Stable Vector Autoregressive (Var) Models.
Estimating the Number of Lags.
Autocorrelation and Distributional Properties of Residuals.
Stationary Autoregressive Distributed Lag Models.
Estimation of Nonstationary VAR models.
Estimation with Canonical Correlations.
Estimation with Principal Component Analysis.
Estimation with the Eigenvalues of the Companion Matrix.
Nonlinear Models in Finance.
Chapter 4 Common Pitfalls in Financial Modeling.
Theory and Engineering.
Engineering and Theoretical Science.
Engineering and Product Design in Finance.
Learning, Theoretical, and Hybrid Approaches to Portfolio Management.
The Bias in Averages.
Pitfalls in Choosing from Large Data Sets.
Time Aggregation of Models and Pitfalls in the Selection of Data Frequency.
Model Risk and its Mitigation.
Chapter 5 Factor Models and Their Estimation.
The Notion of Factors.
Static Factor Models.
Factor Analysis and Principal Components Analysis.
Why Factor Models of Returns.
Approximate Factor Models of Returns.
Dynamic Factor Models.
Chapter 6 Factor-Based Trading Strategies I: Factor Construction and Analysis.
Developing Factor-Based Trading Strategies.
Risk to Trading Strategies.
Desirable Properties of Factors.
Sources for Factors.
Building Factors from Company Characteristics.
Working with Data.
Analysis of Factor Data.
Chapter 7 Factor-Based Trading Strategies II: Cross-Sectional Models and Trading Strategies.
Cross-Sectional Methods for Evaluation of Factor Premiums.
Performance Evaluation of Factors.
Model Construction Methodologies for a Factor-Based Trading Strategy.
Backtesting Our Factor Trading Strategy.
Chapter 8 Portfolio Optimization: Basic Theory and Practice.
Mean-Variance Analysis: Overview.
Classical Framework for Mean-Variance Optimization.
Mean-variance Optimization with a Risk-Free Asset.
Portfolio Constraints Commonly Used in Practice.
Estimating the Inputs Used in Mean-Variance Optimization: Expected Return and Risk.
Portfolio Optimization with Other Risk Measures.
Chapter 9 Portfolio Optimization: Bayesian Techniques and the Black-Litterman Model.
Practical Problems Encountered in Mean-Variance Optimization.
The Black-Litterman Model.
Chapter 10 Robust Portfolio Optimization.
Robust Mean-Variance Formulations.
Using Robust Mean-Variance Portfolio Optimization in Practice.
Some Practical Remarks on Robust Portfolio Optimization Models.
Chapter 11 Transaction Costs and Trade Execution.
A Taxonomy of Transaction Costs.
Liquidity and Transaction Costs.
Market Impact Measurements and Empirical Findings.
Forecasting and Modeling Market Impact.
Incorporating Transaction Costs in Asset-Allocation Models.
Integrated Portfolio Management: Beyond Expected Return and Portfolio Risk.
Chapter 12 Investment Management and Algorithmic Trading.
Market Impact and the Order Book.
Popular Algorithmic Trading Strategies.
What Is Next?
Some Comments about the High-Frequency Arms Race.
Appendix A Data Descriptions and Factor Definitions.
The MSCI World Index.
The Compustat Point-in-Time, IBES Consensus Databases and Factor Definitions.
Appendix B Summary of Well-Known Factors and Their Underlying Economic Rationale.
Appendix C Review of Eigenvalues and Eigenvectors.
The SWEEP Operator.
Sergio M. Focardi is Professor of Finance at EDHEC Business School in Nice and a founding partner of the Paris-based consulting firm The Intertek Group. He is also a member of the Editorial Board of the Journal of Portfolio Management. Sergio holds a degree in electronic engineering from the University of Genoa and a PhD in mathematical finance from the University of Karlsruhe as well as a postgraduate degree in communications from the Galileo Ferraris Electrotechnical Institute (Turin).
Petter N. Kolm is the Deputy Director of the Mathematics in Finance Master's Program and Clinical Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University; and a founding Partner of the New York–based financial consulting firm the Heimdall Group, LLC. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management. He received an MS in mathematics from ETH in Zurich; an MPhil in applied mathematics from the Royal Institute of Technology in Stockholm; and a PhD in applied mathematics from Yale University.