Quantitative Equity Investing : Techniques and Strategies

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2010-03-01
  • Publisher: Wiley
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Supplemental Materials

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Quantitative equity portfolio management is a fundamental building block of investment management. This hands-on guide closes the gap between theory and practice by presenting state-of-the-art quantitative techniques and strategies for managing equity portfolios.

Author Biography

Authors Frank Fabozzi, Sergio Focardi, and Petter Kolm-all of whom have extensive experience in this area-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 provide numerous illustrations and thorough discussions of implementation issues facing those in the investment management business and include the necessary background material in financial econometrics to make the book self-contained. For many of the advanced topics, they also provide the reader with references to the most recent applicable research in this rapidly evolving field.

Table of Contents

Prefacep. xi
About the Authorsp. xv
Introductionp. 1
In Praise of Mathematical Financep. 3
Studies of the Use of Quantitative Equity Managementp. 9
Looking Ahead for Quantitative Equity Investingp. 45
Financial Econometrics I: Linear Regressionsp. 47
Historical Notesp. 47
Covariance and Correlationp. 49
Regressions, Linear Regressions, and Projectionsp. 61
Multivariate Regressionp. 76
Quantile Regressionsp. 78
Regression Diagnosticp. 80
Robust Estimation of Regressionsp. 83
Classification and Regression Treesp. 96
Summaryp. 99
Financial Econometrics II: Time Seriesp. 101
Stochastic Processesp. 101
Time Seriesp. 102
Stable Vector Autoregressive Processesp. 110
Integrated and Cointegrated Variablesp. 114
Estimation of Stable Vector Autoregressive (VAR) Modelsp. 120
Estimating the Number of Lagsp. 137
Autocorrelation and Distributional Properties of Residualsp. 139
Stationary Autoregressive Distributed Lag Modelsp. 140
Estimation of Nonstationary VAR Modelsp. 141
Estimation with Canonical Correlationsp. 151
Estimation with Principal Component Analysisp. 153
Estimation with the Eigenvalues of the Companion Matrixp. 154
Nonlinear Models in Financep. 155
Causalityp. 156
Summaryp. 157
Common Pitfalls in Financial Modelingp. 159
Theory and Engineeringp. 159
Engineering and Theoretical Sciencep. 161
Engineering and Product Design in Financep. 163
Learning, Theoretical, and Hybrid Approaches to Portfolio Managementp. 164
Sample Biasesp. 165
The Bias in Averagesp. 167
Pitfalls in Choosing from Large Data Setsp. 170
Time Aggregation of Models and Pitfalls in the Selection of Data Frequencyp. 173
Model Risk and its Mitigationp. 174
Summaryp. 193
Factor Models and their Estimationp. 195
The Notion of Factorsp. 195
Static Factor Modelsp. 196
Factor Analysis and Principal Components Analysisp. 205
Why Factor Models of Returnsp. 219
Approximate Factor Models of Returnsp. 221
Dynamic Factor Modelsp. 222
Summaryp. 239
Factor-Based Trading Strategies I: Factor Construction and Analysisp. 243
Factor-Based Tradingp. 245
Developing Factor-Based Trading Strategiesp. 247
Risk to Trading Strategiesp. 249
Desirable Properties of Factorsp. 251
Sources for Factorsp. 251
Building Factors from Company Characteristicsp. 253
Working with Datap. 253
Analysis of Factor Datap. 261
Summaryp. 266
Factor-Based Trading Strategies II: Cross-Sectional Models and Trading Strategiesp. 269
Cross-Sectional Methods for Evaluation of Factor Premiumsp. 270
Factor Modelsp. 278
Performance Evaluation of Factorsp. 288
Model Construction Methodologies for a Factor-Based Trading Strategyp. 295
Backtestingp. 306
Backtesting Our Factor Trading Strategyp. 308
Summaryp. 309
Portfolio Optimization: Basic Theory and Practicep. 313
Mean-Variance Analysis: Overviewp. 314
Classical Framework for Mean-Variance Optimizationp. 317
Mean-Variance Optimization with a Risk-Free Assetp. 321
Portfolio Constraints Commonly Used in Practicep. 327
Estimating the Inputs Used in Mean-Variance Optimization: Expected Return and Riskp. 333
Portfolio Optimization with Other Risk Measuresp. 342
Summaryp. 357
Portfolio Optimization: Bayesian Techniques and the Black-Litterman Modelp. 361
Practical Problems Encountered in Mean-Variance Optimizationp. 362
Shrinkage Estimationp. 369
The Black-Litterman Modelp. 373
Summaryp. 394
Robust Portfolio Optimizationp. 395
Robust Mean-Variance Formulationsp. 396
Using Robust Mean-Variance Portfolio Optimization in Practicep. 411
Some Practical Remarks on Robust Portfolio Optimization Modelsp. 416
Summaryp. 418
Transaction Costs and Trade Executionp. 419
A Taxonomy of Transaction Costsp. 420
Liquidity and Transaction Costsp. 427
Market Impact Measurements and Empirical Findingsp. 430
Forecasting and Modeling Market Impactp. 433
Incorporating Transaction Costs in Asset-Allocation Modelsp. 439
Integrated Portfolio Management: Beyond Expected Return and Portfolio Riskp. 444
Summaryp. 446
Investment Management and Algorithmic Tradingp. 449
Market Impact and the Order Bookp. 450
Optimal Executionp. 452
Impact Modelsp. 455
Popular Algorithmic Trading Strategiesp. 457
What Is Next?p. 465
Some Comments about the High-Frequency Arms Racep. 467
Summaryp. 470
Data Descriptions and Factor Definitionsp. 473
The MSCI World Indexp. 473
One-Month LIBORp. 482
The Compustat Point-in-Time, IBES Consensus Databases and Factor Definitionsp. 483
Summary of Well-Known Factors and Their Underlying Economic Rationalep. 487
Review of Eigenvalues and Eigenvectorsp. 493
The SWEEP Operatorp. 494
Indexp. 497
Table of Contents provided by Ingram. All Rights Reserved.

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