Quantitative Equity Investing : Techniques and Strategies

by Frank J. Fabozzi (School of Management, Yale Univ.); Sergio M. Focardi; Petter N. Kolm

• ISBN13: 9780470617519
• ISBN10: 0470617519
• Format: eBook
• Copyright: 2010-02-01
• Publisher: Wiley

Summary

A comprehensive look at the tools and techniques used in quantitative equity managementSome 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 modelsIn 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.

Table of Contents

Preface

About the Authors

Introduction

In Praise of Mathematical Finance

Studies of the Use of Quantitative Equity Management

Looking Ahead for Quantitative Equity Investing

Financial Econometrics I: Linear Regressions

Historical Notes

Covariance and Correlation

Regressions, Linear Regressions, and Projections

Multivariate Regression

Quantile Regressions

Regression Diagnostic

Robust Estimation of Regressions

Classification and Regression Trees

Summary

Financial Econometrics II: Time Series

Stochastic Processes

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

Causality

Summary

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

Sample Biases

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

Summary

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

Summary

Factor-Based Trading Strategies I: Factor Construction and Analysis

Factor-Based Trading

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

Summary

Factor-Based Trading Strategies II: Cross-Sectional Models and Trading Strategies

Cross-Sectional Methods for Evaluation of Factor Premiums

Factor Models

Performance Evaluation of Factors

Model Construction Methodologies for a Factor-Based Trading Strategy

Backtesting

Backtesting Our Factor Trading Strategy

Summary

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

Summary

Portfolio Optimization: Bayesian Techniques and the Black-Litterman Model

Practical Problems Encountered in Mean-Variance Optimization

Shrinkage Estimation

The Black-Litterman Model

Summary

Robust Portfolio Optimization

Robust Mean-Variance Formulations

Using Robust Mean-Variance Portfolio Optimization in Practice

Some Practical Remarks on Robust Portfolio Optimization Models

Summary

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

Summary

Investment Management and Algorithmic Trading

Market Impact and the Order Book

Optimal Execution

Impact Models

Popular Algorithmic Trading Strategies

What Is Next?

Some Comments about the High-Frequency Arms Race

Summary

Data Descriptions and Factor Definitions

The MSCI World Index

One-Month LIBOR

The Compustat Point-in-Time, IBES Consensus Databases and Factor Definitions

Summary of Well-Known Factors and Their Underlying Economic Rationale

Review of Eigenvalues and Eigenvectors

The SWEEP Operator

Index

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