Quantitative Equity Investing Techniques and

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.

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.

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

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Quantitative Equity Investing Techniques and Strategies

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