Frank J. Fabozzi, Sergio M. Focardi, Petter N. Kolm

Quantitative Equity Investing

Techniques and Strategies

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• The Frank J. Fabozzi Series
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 528
• Erscheinungstermin: 1. März 2010
• Englisch
• Abmessung: 235mm x 157mm x 33mm
• Gewicht: 766g
• ISBN-13: 9780470262474
• ISBN-10: 0470262478
• Artikelnr.: 27135655

Autorenporträt

FRANK J. FABOZZI is Professor in the Practice of Finance and Becton Fellow at the Yale School of Management and Editor of the Journal of Portfolio Management. He is a Chartered Financial Analyst and earned a doctorate in economics from the City University of New York. 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.

Inhaltsangabe

Preface xi
About the Authors xv
Chapter 1 Introduction 1
In Praise of Mathematical Finance 3
Studies of the Use of Quantitative Equity Management 9
Looking Ahead for Quantitative Equity Investing 45
Chapter 2 Financial Econometrics I: Linear Regressions 47
Historical Notes 47
Covariance and Correlation 49
Regressions, Linear Regressions, and Projections 61
Multivariate Regression 76
Quantile Regressions 78
Regression Diagnostic 80
Robust Estimation of Regressions 83
Classification and Regression Trees 96
Summary 99
Chapter 3 Financial Econometrics II: Time Series 101
Stochastic Processes 101
Time Series 102
Stable Vector Autoregressive Processes 110
Integrated and Cointegrated Variables 114
Estimation of Stable Vector Autoregressive (VAR) Models 120
Estimating the Number of Lags 137
Autocorrelation and Distributional Properties of Residuals 139
Stationary Autoregressive Distributed Lag Models 140
Estimation of Nonstationary VAR Models 141
Estimation with Canonical Correlations 151
Estimation with Principal Component Analysis 153
Estimation with the Eigenvalues of the Companion Matrix 154
Nonlinear Models in Finance 155
Causality 156
Summary 157
Chapter 4 Common Pitfalls in Financial Modeling 159
Theory and Engineering 159
Engineering and Theoretical Science 161
Engineering and Product Design in Finance 163
Learning, Theoretical, and Hybrid Approaches to Portfolio Management 164
Sample Biases 165
The Bias in Averages 167
Pitfalls in Choosing from Large Data Sets 170
Time Aggregation of Models and Pitfalls in the Selection of Data Frequency
173
Model Risk and its Mitigation 174
Summary 193
Chapter 5 Factor Models and Their Estimation 195
The Notion of Factors 195
Static Factor Models 196
Factor Analysis and Principal Components Analysis 205
Why Factor Models of Returns 219
Approximate Factor Models of Returns 221
Dynamic Factor Models 222
Summary 239
Chapter 6 Factor-Based Trading Strategies I: Factor Construction and
Analysis 243
Factor-Based Trading 245
Developing Factor-Based Trading Strategies 247
Risk to Trading Strategies 249
Desirable Properties of Factors 251
Sources for Factors 251
Building Factors from Company Characteristics 253
Working with Data 253
Analysis of Factor Data 261
Summary 266
Chapter 7 Factor-Based Trading Strategies II: Cross-Sectional Models and
Trading Strategies 269
Cross-Sectional Methods for Evaluation of Factor Premiums 270
Factor Models 278
Performance Evaluation of Factors 288
Model Construction Methodologies for a Factor-Based Trading Strategy 295
Backtesting 306
Backtesting Our Factor Trading Strategy 308
Summary 309
Chapter 8 Portfolio Optimization: Basic Theory and Practice 313
Mean-Variance Analysis: Overview 314
Classical Framework for Mean-Variance Optimization 317
Mean-Variance Optimization with a Risk-Free Asset 321
Portfolio Constraints Commonly Used in Practice 327
Estimating the Inputs Used in Mean-Variance Optimization: Expected Return
and Risk 333
Portfolio Optimization with Other Risk Measures 342
Summary 357
Chapter 9 Portfolio Optimization: Bayesian Techniques and the
Black-Litterman Model 361
Practical Problems Encountered in Mean-Variance Optimization 362
Shrinkage Estimation 369
The Black-Litterman Model 373
Summary 394
Chapter 10 Robust Portfolio Optimization 395
Robust Mean-Variance Formulations 396
Using Robust Mean-Variance Portfolio Optimization in Practice 411
Some Practical Remarks on Robust Portfolio Optimization Models 416
Summary 418
Chapter 11 Transaction Costs and Trade Execution 419
A Taxonomy of Transaction Costs 420
Liquidity and Transaction Costs 427
Market Impact Measurements and Empirical Findings 430
Forecasting and Modeling Market Impact 433
Incorporating Transaction Costs in Asset-Allocation Models 439
Integrated Portfolio Management: Beyond Expected Return and Portfolio Risk
444
Summary 446
Chapter 12 Investment Management and Algorithmic Trading 449
Market Impact and the Order Book 450
Optimal Execution 452
Impact Models 455
Popular Algorithmic Trading Strategies 457
What Is Next? 465
Some Comments about the High-Frequency Arms Race 467
Summary 470
Appendix A Data Descriptions and Factor Definitions 473
The MSCI World Index 473
One-Month LIBOR 482
The Compustat Point-in-Time, IBES Consensus Databases and Factor
Definitions 483
Appendix B Summary of Well-Known Factors and Their Underlying Economic
Rationale 487
Appendix C Review of Eigenvalues and Eigenvectors 493
The SWEEP Operator 494
Index 497