PortfolioConstructionand Analytics

The Frank J. Fabozzi SeriesFixed Income Securities, Second Edition by Frank J. Fabozzi

Focus on Value: A Corporate and Investor Guide to Wealth Creation byJames L. Grant and James A. Abate

Handbook of Global Fixed Income Calculations by Dragomir Krgin

Managing a Corporate Bond Portfolio by Leland E. Crabbe and FrankJ. Fabozzi

Real Options and Option-Embedded Securities by William T. Moore

Capital Budgeting: Theory and Practice by Pamela P. Peterson and FrankJ. Fabozzi

The Exchange-Traded Funds Manual by Gary L. Gastineau

Professional Perspectives on Fixed Income Portfolio Management, Volume 3edited by Frank J. Fabozzi

Investing in Emerging Fixed Income Markets edited by Frank J. Fabozzi andEfstathia Pilarinu

Handbook of Alternative Assets by Mark J. P. Anson

The Global Money Markets by Frank J. Fabozzi, Steven V. Mann, andMoorad Choudhry

The Handbook of Financial Instruments edited by Frank J. Fabozzi

Interest Rate, Term Structure, and Valuation Modeling edited by FrankJ. Fabozzi

Investment Performance Measurement by Bruce J. Feibel

The Handbook of Equity Style Management edited by T. Daniel Coggin andFrank J. Fabozzi

The Theory and Practice of Investment Management edited by FrankJ. Fabozzi and Harry M. Markowitz

Foundations of Economic Value Added, Second Edition by James L. Grant

Financial Management and Analysis, Second Edition by Frank J. Fabozziand Pamela P. Peterson

Measuring and Controlling Interest Rate and Credit Risk, Second Editionby Frank J. Fabozzi, Steven V. Mann, and Moorad Choudhry

Professional Perspectives on Fixed Income Portfolio Management, Volume 4edited by Frank J. Fabozzi

The Handbook of European Fixed Income Securities edited by FrankJ. Fabozzi and Moorad Choudhry

The Handbook of European Structured Financial Products edited by FrankJ. Fabozzi and Moorad Choudhry

The Mathematics of Financial Modeling and Investment Management bySergio M. Focardi and Frank J. Fabozzi

Short Selling: Strategies, Risks, and Rewards edited by Frank J. Fabozzi

The Real Estate Investment Handbook by G. Timothy Haight and DanielSinger

Market Neutral Strategies edited by Bruce I. Jacobs and Kenneth N. Levy

Securities Finance: Securities Lending and Repurchase Agreements edited byFrank J. Fabozzi and Steven V. Mann

Fat-Tailed and Skewed Asset Return Distributions by Svetlozar T. Rachev,Christian Menn, and Frank J. Fabozzi

Financial Modeling of the Equity Market: From CAPM to Cointegration byFrank J. Fabozzi, Sergio M. Focardi, and Petter N. Kolm

Advanced Bond Portfolio Management: Best Practices in Modeling andStrategies edited by Frank J. Fabozzi, Lionel Martellini, and PhilippePriaulet

Analysis of Financial Statements, Second Edition by Pamela P. Peterson andFrank J. Fabozzi

Collateralized Debt Obligations: Structures and Analysis, Second Edition byDouglas J. Lucas, Laurie S. Goodman, and Frank J. Fabozzi

Handbook of Alternative Assets, Second Edition by Mark J. P. Anson

Introduction to Structured Finance by Frank J. Fabozzi, Henry A. Davis,and Moorad Choudhry

Financial Econometrics by Svetlozar T. Rachev, Stefan Mittnik, FrankJ. Fabozzi, Sergio M. Focardi, and Teo Jasic

Developments in Collateralized Debt Obligations: New Products andInsights by Douglas J. Lucas, Laurie S. Goodman, Frank J. Fabozzi, andRebecca J. Manning

Robust Portfolio Optimization and Management by Frank J. Fabozzi, PeterN. Kolm, Dessislava A. Pachamanova, and Sergio M. Focardi

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimizationsby Svetlozar T. Rachev, Stogan V. Stoyanov, and Frank J. Fabozzi

How to Select Investment Managers and Evaluate Performance byG. Timothy Haight, Stephen O. Morrell, and Glenn E. Ross

Bayesian Methods in Finance by Svetlozar T. Rachev, John S. J. Hsu, BilianaS. Bagasheva, and Frank J. Fabozzi

Simulation and Optimization in Finance: Modeling with MATLAB, @RISK,or VBA + Website by Dessislava A. Pachamanova and Frank J. Fabozzi

The Handbook of Municipal Bonds edited by Sylvan G. Feldstein and FrankJ. Fabozzi

Subprime Mortgage Credit Derivatives by Laurie S. Goodman, Shumin Li,Douglas J. Lucas, Thomas A Zimmerman, and Frank J. Fabozzi

Introduction to Securitization by Frank J. Fabozzi and Vinod Kothari

Structured Products and Related Credit Derivatives edited by BrianP. Lancaster, Glenn M. Schultz, and Frank J. Fabozzi

Handbook of Finance: Volume I: Financial Markets and Instruments editedby Frank J. Fabozzi

Handbook of Finance: Volume II: Financial Management and Asset Man-agement edited by Frank J. Fabozzi

Handbook of Finance: Volume III: Valuation, Financial Modeling, andQuantitative Tools edited by Frank J. Fabozzi

Finance: Capital Markets, Financial Management, and Investment Manage-ment by Frank J. Fabozzi and Pamela Peterson-Drake

Active Private Equity Real Estate Strategy edited by David J. Lynn

Foundations and Applications of the Time Value of Money by PamelaPeterson-Drake and Frank J. Fabozzi

Leveraged Finance: Concepts, Methods, and Trading of High-Yield Bonds,Loans, and Derivatives by Stephen Antczak, Douglas Lucas, and FrankJ. Fabozzi

Modern Financial Systems: Theory and Applications by Edwin Neave

Institutional Investment Management: Equity and Bond Portfolio Strategiesand Applications by Frank J. Fabozzi

Robust Equity Portfolio Management + Website by Woo Chang Kim, JangHo Kim, and Frank J. Fabozzi

PortfolioConstructionand Analytics

DESSISLAVA A. PACHAMANOVAFRANK J. FABOZZI

Copyright © 2016 by Dessislava A. Pachamanova and Frank J. Fabozzi. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted inany form or by any means, electronic, mechanical, photocopying, recording, scanning, orotherwise, except as permitted under Section 107 or 108 of the 1976 United States CopyrightAct, without either the prior written permission of the Publisher, or authorization throughpayment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Webat www.copyright.com. Requests to the Publisher for permission should be addressed to thePermissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030,(201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their bestefforts in preparing this book, they make no representations or warranties with respect to theaccuracy or completeness of the contents of this book and specifically disclaim any impliedwarranties of merchantability or fitness for a particular purpose. No warranty may be createdor extended by sales representatives or written sales materials. The advice and strategiescontained herein may not be suitable for your situation. You should consult with aprofessional where appropriate. Neither the publisher nor author shall be liable for any lossof profit or any other commercial damages, including but not limited to special, incidental,consequential, or other damages.

For general information on our other products and services or for technical support, pleasecontact our Customer Care Department within the United States at (800) 762-2974, outsidethe United States at (317) 572-3993 or fax (317) 572-4002.

Wiley publishes in a variety of print and electronic formats and by print-on-demand. Somematerial included with standard print versions of this book may not be included in e-books orin print-on-demand. If this book refers to media such as a CD or DVD that is not included inthe version you purchased, you may download this material at http://booksupport.wiley.com.For more information about Wiley products, visit www.wiley.com.

Library of Congress Cataloging-in-Publication Data:

Names: Fabozzi, Frank J., author. | Pachamanova, Dessislava A., author.Title: Portfolio construction and analytics / Frank J. Fabozzi, Dessislava Pachamanova.Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2016] | Series:

Frank J. Fabozzi series | Includes bibliographical references and index.Identifiers: LCCN 2015040278 (print) | LCCN 2016003023 (ebook) | ISBN 9781118445594

(hardback) | ISBN 9781119238140 (ePub) | ISBN 9781119238164 (Adobe PDF)Subjects: LCSH: Portfolio management. | BISAC: BUSINESS & ECONOMICS / Finance.Classification: LCC HG4529.5 .F33456 2016 (print) | LCC HG4529.5 (ebook) |

DDC 332.6—dc23LC record available at http://lccn.loc.gov/2015040278

Cover Design: WileyCover Image: © kentoh/Shutterstock

Printed in the United States of America

10 9 8 7 6 5 4 3 2 1

http://www.copyright.comhttp://www.wiley.com/go/permissionshttp://booksupport.wiley.comhttp://www.wiley.comhttp://lccn.loc.gov/2015040278

Dessislava A. Pachamanova

To my parents, Rositsa and Angel

Frank J. Fabozzi

To my wife, Donna, and my children, Karly, Patricia,and Francesco

Contents

Preface xix

About the Authors xxv

Acknowledgments xxvii

CHAPTER 1Introduction to Portfolio Management and Analytics 11.1 Asset Classes and the Asset Allocation Decision 11.2 The Portfolio Management Process 4

1.2.1 Setting the Investment Objectives 41.2.2 Developing and Implementing a Portfolio Strategy 61.2.3 Monitoring the Portfolio 81.2.4 Adjusting the Portfolio 9

1.3 Traditional versus Quantitative Asset Management 91.4 Overview of Portfolio Analytics 10

1.4.1 Market Analytics 121.4.2 Financial Screening 151.4.3 Asset Allocation Models 161.4.4 Strategy Testing and Evaluating Portfolio

Performance 171.4.5 Systems for Portfolio Analytics 20

1.5 Outline of Topics Covered in the Book 22

PART ONEStatistical Models of Risk and Uncertainty

CHAPTER 2Random Variables, Probability Distributions, and ImportantStatistical Concepts 312.1 What Is a Probability Distribution? 312.2 The Bernoulli Probability Distribution and Probability

Mass Functions 32

ix

x CONTENTS

2.3 The Binomial Probability Distribution and DiscreteDistributions 34

2.4 The Normal Distribution and Probability DensityFunctions 38

2.5 The Concept of Cumulative Probability 412.6 Describing Distributions 44

2.6.1 Measures of Central Tendency 442.6.2 Measures of Risk 472.6.3 Skew 542.6.4 Kurtosis 55

2.7 Dependence between Two Random Variables: Covarianceand Correlation 55

2.8 Sums of Random Variables 572.9 Joint Probability Distributions and Conditional

Probability 612.10 Copulas 642.11 From Probability Theory to Statistical Measurement:

Probability Distributions and Sampling 662.11.1 Central Limit Theorem 702.11.2 Confidence Intervals 712.11.3 Bootstrapping 722.11.4 Hypothesis Testing 73

CHAPTER 3Important Probability Distributions 773.1 Examples of Probability Distributions 79

3.1.1 Notation Used in Describing ContinuousProbability Distributions 79

3.1.2 Discrete and Continuous Uniform Distributions 803.1.3 Student’s t Distribution 823.1.4 Lognormal Distribution 833.1.5 Poisson Distribution 853.1.6 Exponential Distribution 873.1.7 Chi-Square Distribution 883.1.8 Gamma Distribution 903.1.9 Beta Distribution 90

3.2 Modeling Financial Return Distributions 913.2.1 Elliptical Distributions 923.2.2 Stable Paretian Distributions 943.2.3 Generalized Lambda Distribution 96

Contents xi

3.3 Modeling Tails of Financial Return Distributions 983.3.1 Generalized Extreme Value Distribution 983.3.2 Generalized Pareto Distribution 993.3.3 Extreme Value Models 101

CHAPTER 4Statistical Estimation Models 1064.1 Commonly Used Return Estimation Models 1064.2 Regression Analysis 108

4.2.1 A Simple Regression Example 1094.2.2 Regression Applications in the Investment

Management Process 1144.3 Factor Analysis 1164.4 Principal Components Analysis 1184.5 Autoregressive Conditional Heteroscedastic Models 125

PART TWOSimulation and Optimization Modeling

CHAPTER 5Simulation Modeling 1335.1 Monte Carlo Simulation: A Simple Example 133

5.1.1 Selecting Probability Distributions for the Inputs 1355.1.2 Interpreting Monte Carlo Simulation Output 137

5.2 Why Use Simulation? 1405.2.1 Multiple Input Variables and Compounding

Distributions 1415.2.2 Incorporating Correlations 1425.2.3 Evaluating Decisions 144

5.3 How Many Scenarios? 1475.4 Random Number Generation 149

CHAPTER 6Optimization Modeling 1516.1 Optimization Formulations 152

6.1.1 Minimization versus Maximization 1546.1.2 Local versus Global Optima 1556.1.3 Multiple Objectives 156

xii CONTENTS

6.2 Important Types of Optimization Problems 1576.2.1 Convex Programming 1576.2.2 Linear Programming 1586.2.3 Quadratic Programming 1596.2.4 Second-Order Cone Programming 1606.2.5 Integer and Mixed Integer Programming 161

6.3 A Simple Optimization Problem Formulation Example:Portfolio Allocation 161

6.4 Optimization Algorithms 1666.5 Optimization Software 1686.6 A Software Implementation Example 170

6.6.1 Optimization with Excel Solver 1716.6.2 Solution to the Portfolio Allocation Example 175

CHAPTER 7Optimization under Uncertainty 1807.1 Dynamic Programming 1817.2 Stochastic Programming 183

7.2.1 Multistage Models 1847.2.2 Mean-Risk Stochastic Models 1897.2.3 Chance-Constrained Models 191

7.3 Robust Optimization 194

PART THREEPortfolio Theory

CHAPTER 8Asset Diversification 2038.1 The Case for Diversification 2048.2 The Classical Mean-Variance Optimization Framework 2088.3 Efficient Frontiers 2128.4 Alternative Formulations of the Classical Mean-Variance

Optimization Problem 2158.4.1 Expected Return Formulation 2158.4.2 Risk Aversion Formulation 215

8.5 The Capital Market Line 2168.6 Expected Utility Theory 220

8.6.1 Quadratic Utility Function 2218.6.2 Linear Utility Function 2238.6.3 Exponential Utility Function 224

Contents xiii

8.6.4 Power Utility Function 2248.6.5 Logarithmic Utility Function 224

8.7 Diversification Redefined 226

CHAPTER 9Factor Models 2329.1 Factor Models in the Financial Economics Literature 2339.2 Mean-Variance Optimization with Factor Models 2369.3 Factor Selection in Practice 2399.4 Factor Models for Alpha Construction 2439.5 Factor Models for Risk Estimation 245

9.5.1 Macroeconomic Factor Models 2459.5.2 Fundamental Factor Models 2469.5.3 Statistical Factor Models 2489.5.4 Hybrid Factor Models 2509.5.5 Selecting the "Right" Factor Model 250

9.6 Data Management and Quality Issues 2519.6.1 Data Alignment 2529.6.2 Survival Bias 2539.6.3 Look-Ahead Bias 2539.6.4 Data Snooping 254

9.7 Risk Decomposition, Risk Attribution, and PerformanceAttribution 254

9.8 Factor Investing 256

CHAPTER 10Benchmarks and the Use of Tracking Error in Portfolio Construction 26010.1 Tracking Error versus Alpha: Calculation and

Interpretation 26110.2 Forward-Looking versus Backward-Looking Tracking

Error 26410.3 Tracking Error and Information Ratio 26510.4 Predicted Tracking Error Calculation 265

10.4.1 Variance-Covariance Method for Tracking ErrorCalculation 266

10.4.2 Tracking Error Calculation Based on aMultifactor Model 266

10.5 Benchmarks and Indexes 26810.5.1 Market Indexes 26810.5.2 Noncapitalization Weighted Indexes 270

10.6 Smart Beta Investing 272

xiv CONTENTS

PART FOUREquity Portfolio Management

CHAPTER 11Advances in Quantitative Equity Portfolio Management 28111.1 Portfolio Constraints Commonly Used in Practice 282

11.1.1 Long-Only (No-Short-Selling) Constraints 28311.1.2 Holding Constraints 28311.1.3 Turnover Constraints 28411.1.4 Factor Constraints 28411.1.5 Cardinality Constraints 28611.1.6 Minimum Holding and Transaction Size

Constraints 28711.1.7 Round Lot Constraints 28811.1.8 Tracking Error Constraints 29011.1.9 Soft Constraints 291

11.1.10 Misalignment Caused by Constraints 29111.2 Portfolio Optimization with Tail Risk Measures 291

11.2.1 Portfolio Value-at-Risk Optimization 29211.2.2 Portfolio Conditional Value-at-Risk Optimization 294

11.3 Incorporating Transaction Costs 29711.3.1 Linear Transaction Costs 29911.3.2 Piecewise-Linear Transaction Costs 30011.3.3 Quadratic Transaction Costs 30211.3.4 Fixed Transaction Costs 30211.3.5 Market Impact Costs 303

11.4 Multiaccount Optimization 30411.5 Incorporating Taxes 30811.6 Robust Parameter Estimation 31211.7 Portfolio Resampling 31411.8 Robust Portfolio Optimization 317

CHAPTER 12Factor-Based Equity Portfolio Construction andPerformance Evaluation 32512.1 Equity Factors Used in Practice 325

12.1.1 Fundamental Factors 32612.1.2 Macroeconomic Factors 32712.1.3 Technical Factors 32712.1.4 Additional Factors 327

Contents xv

12.2 Stock Screens 32812.3 Portfolio Selection 331

12.3.1 Ad-Hoc Portfolio Selection 33112.3.2 Stratification 33212.3.3 Factor Exposure Targeting 333

12.4 Risk Decomposition 33412.5 Stress Testing 34312.6 Portfolio Performance Evaluation 34612.7 Risk Forecasts and Simulation 350

PART FIVEFixed Income Portfolio Management

CHAPTER 13Fundamentals of Fixed Income Portfolio Management 36113.1 Fixed Income Instruments and Major Sectors

of the Bond Market 36113.1.1 Treasury Securities 36213.1.2 Federal Agency Securities 36313.1.3 Corporate Bonds 36313.1.4 Municipal Bonds 36413.1.5 Structured Products 364

13.2 Features of Fixed Income Securities 36513.2.1 Term to Maturity and Maturity 36513.2.2 Par Value 36613.2.3 Coupon Rate 36613.2.4 Bond Valuation and Yield 36713.2.5 Provisions for Paying Off Bonds 36813.2.6 Bondholder Option Provisions 370

13.3 Major Risks Associated with Investing in Bonds 37113.3.1 Interest Rate Risk 37113.3.2 Call and Prepayment Risk 37213.3.3 Credit Risk 37313.3.4 Liquidity Risk 374

13.4 Fixed Income Analytics 37513.4.1 Measuring Interest Rate Risk 37513.4.2 Measuring Spread Risk 38313.4.3 Measuring Credit Risk 38413.4.4 Estimating Fixed Income Portfolio Risk

Using Simulation 384

xvi CONTENTS

13.5 The Spectrum of Fixed Income Portfolio Strategies 38613.5.1 Pure Bond Indexing Strategy 38713.5.2 Enhanced Indexing/Primary Factor Matching 38813.5.3 Enhanced Indexing/Minor Factor Mismatches 38913.5.4 Active Management/Larger Factor Mismatches 38913.5.5 Active Management/Full-Blown Active 39013.5.6 Smart Beta Strategies for Fixed Income Portfolios 390

13.6 Value-Added Fixed Income Strategies 39113.6.1 Interest Rate Expectations Strategies 39113.6.2 Yield Curve Strategies 39213.6.3 Inter- and Intra-sector Allocation Strategies 39313.6.4 Individual Security Selection Strategies 394

CHAPTER 14Factor-Based Fixed Income Portfolio Construction and Evaluation 39814.1 Fixed Income Factors Used in Practice 398

14.1.1 Term Structure Factors 39914.1.2 Credit Spread Factors 40014.1.3 Currency Factors 40114.1.4 Emerging Market Factors 40114.1.5 Volatility Factors 40214.1.6 Prepayment Factors 402

14.2 Portfolio Selection 40214.2.1 Stratification Approach 40314.2.2 Optimization Approach 40514.2.3 Portfolio Rebalancing 408

14.3 Risk Decomposition 410

CHAPTER 15Constructing Liability-Driven Portfolios 42015.1 Risks Associated with Liabilities 421

15.1.1 Interest Rate Risk 42115.1.2 Inflation Risk 42215.1.3 Longevity Risk 423

15.2 Liability-Driven Strategies of Life Insurance Companies 42315.2.1 Immunization 42415.2.2 Advanced Optimization Approaches 43515.2.3 Constructing Replicating Portfolios 437

Contents xvii

15.3 Liability-Driven Strategies of Defined BenefitPension Funds 43815.3.1 High-Grade Bond Portfolio Solution 43915.3.2 Including Other Assets 44215.3.3 Advanced Modeling Strategies 443

PART SIXDerivatives and Their Application to Portfolio Management

CHAPTER 16Basics of Financial Derivatives 44916.1 Overview of the Use of Derivatives in Portfolio

Management 44916.2 Forward and Futures Contracts 451

16.2.1 Risk and Return of Forward/Futures Position 45316.2.2 Leveraging Aspect of Futures 45316.2.3 Pricing of Futures and Forward Contracts 454

16.3 Options 45916.3.1 Risk and Return Characteristics of Options 46016.3.2 Option Pricing Models 470

16.4 Swaps 48516.4.1 Interest Rate Swaps 48516.4.2 Equity Swaps 48616.4.3 Credit Default Swaps 487

CHAPTER 17Using Derivatives in Equity Portfolio Management 49017.1 Stock Index Futures and Portfolio Management

Applications 49017.1.1 Basic Features of Stock Index Futures 49017.1.2 Theoretical Price of a Stock Index Futures

Contract 49117.1.3 Portfolio Management Strategies with Stock

Index Futures 49417.2 Equity Options and Portfolio Management

Applications 50417.2.1 Types of Equity Options 50417.2.2 Equity Portfolio Management Strategies

with Options 50617.3 Equity Swaps 511

xviii CONTENTS

CHAPTER 18Using Derivatives in Fixed Income Portfolio Management 51518.1 Controlling Interest Rate Risk Using Treasury Futures 515

18.1.1 Strategies for Controlling Interest Rate Risk withTreasury Futures 518

18.1.2 Pricing of Treasury Futures 52018.2 Controlling Interest Rate Risk Using Treasury

Futures Options 52118.2.1 Strategies for Controlling Interest Rate Risk Using

Treasury Futures Options 52418.2.2 Pricing Models for Treasury Futures Options 526

18.3 Controlling Interest Rate Risk Using Interest Rate Swaps 52718.3.1 Strategies for Controlling Interest Rate Risk Using

Interest Rate Swaps 52818.3.2 Pricing of Interest Rate Swaps 530

18.4 Controlling Credit Risk with Credit Default Swaps 53218.4.1 Strategies for Controlling Credit Risk with Credit

Default Swaps 53418.4.2 General Principles for Valuing a Single-Name

Credit Default Swap 535

Appendix: Basic Linear Algebra Concepts 541

References 549

Index 563

Preface

“Analytics” and “Big Data” have become buzzwords in many industries,and have dominated the news over the past few years. In finance, analyt-ics and big data have been around for a long time, even if they were describedwith different terms. As J.R. Lowry, chief operating officer of State StreetGlobal Exchange, stated in a 2014 interview published in the MIT SloanManagement Review, “In general, data and analytics have pervaded ourbusiness for many, many years, but it wasn’t something that we were focusedon in any kind of coherent way.”

The need to focus on investment analytics in a coherent way has neverbeen greater. In the aftermath of the 2007–2009 financial crisis, there hasbeen a tremendous amount of regulatory change. Like most industries, thefinancial industry is trying to cope with the challenges of managing big dataand the risks associated with using models. Many asset management firmsface increasing pressure to address important questions such as

■ How to measure, visualize, and manage risks better?■ How to find new sources of return?■ How to manage trading activity effectively?■ How to keep costs down?

The solution of banking giant State Street Corporation was to launch anew business, State Street Global Exchange (SSGX), which applies “a wrap-per of information, insights and analytics around the investment process,”and provides a “more purposeful approach to data and analytics acrossthe company.”1 SSGX is a center that has pulled in software capabilitiesand analytics groups focused on risk, as well as electronic trading platformsfocused on foreign exchange, fixed income, and derivatives trading.

Portfolio and risk analytics platforms are offered by investment productproviders such as Barclays (the POINT Advanced Analytics Platform)2 andBlackRock (the Aladdin Platform)3 with a similar goal of combining sophis-ticated risk analytics with comprehensive portfolio management, trading

1Ferguson (2014).2See https://ecommerce.barcap.com/point/point.dxml.3See https://www.blackrock.com/aladdin/offerings/aladdin-overview.

xix

https://ecommerce.barcap.com/point/point.dxmlhttps://www.blackrock.com/aladdin/offerings/aladdin-overview

xx PREFACE

and operations tools. Longtime portfolio software vendors (Axioma, IBMAlgorithmics, MSCI Barra, and Northfield Information Services) and dataproviders (Bloomberg, FactSet, Thomson Reuters) are adding both advancedanalytics tools and the ability to link to various data sources. New part-nerships are being formed—for example, financial data provider ThomsonReuters joined forces with Palantir Technologies, a leading Silicon Valleybig data technology company, to create QA Studio, a solution for quanti-tative research that combines powerful analytics and intuitive visualizationsto help with the generation of investment ideas.4 The development of freeopen source software such as the statistical modeling environment R5 andthe open source programming environment Python6 with libraries for finan-cial applications has greatly improved accessibility to analytical tools andhas reduced the costs of implementing portfolio analytics solutions.

In this book, we often refer to the traditional asset managementcompany model, in which the focus is on the selection of star portfolio man-agers in charge of different portions of a firm’s funds under management.However, new technologies have been disrupting the investment industryas a whole. The bundling of asset management practice and softwareplatform offerings is a recent phenomenon, as is the democratization ofaccess to financial data7 and trading opportunities.8 The popularity ofautomated investment services companies, also called robo advisors,9 hasbeen increasing. New-generation asset management companies includeQuantopian,10 which provides an analytics and trading platform andcrowdsources investment ideas from contributors from all over the world,with the goal of rewarding top performers and applying tested strategiesto asset management instead of hiring and managing individual portfoliomanagers. The core of Quantopian’s strategy involves providing usefulmarket and stock fundamentals data, as well as a tool for backtesting,zipline, which has been made open source (free) to help create and supporta community of contributors.

Nobody can tell what the future of the portfolio management industrywill look like but it certainly seems inevitable that data and analytics willplay a major role in it.

4See http://alphanow.thomsonreuters.com/solutions/qa-studio/.5See https://www.r-project.org/.6See https://www.python.org/.7See https://www.quandl.com/. Quandl offers free financial data.8See https://www.interactivebrokers.com/.9Examples of robo advisors include Betterment, WealthFront, WiseBanyan, PersonalCapital, Motif Investing, FutureAdvisor, and Bloom.10See https://www.quantopian.com/.

http://alphanow.thomsonreuters.com/solutions/qa-studio/https://www.r-project.org/https://www.python.org/https://www.quandl.comhttps://www.interactivebrokers.com/https://www.quantopian.com/

Preface xxi

CENTRAL THEMES

Portfolio Construction and Analytics attempts to look at the analytics pro-cess at investment firms from multiple perspectives: the data managementside, the modeling side, and the software resources side. It reviews manywidely used approaches to portfolio analytics and discusses new trends inmetrics, modeling approaches, and portfolio analytics system design. Thetheoretical underpinnings of some of the modeling approaches are providedfor context; however, our goal is to emphasize how such models are used inpractice.

The book contains 18 chapters in six parts. Part One, Statistical Modelsof Risk and Uncertainty, contains the fundamental statistical modeling con-cepts necessary to understand the modeling and measurement of portfoliorisk. Part Two, Simulation and Optimization Modeling, explains two impor-tant modeling techniques for constructing portfolios with desired character-istics and evaluating their risk and performance—simulation and optimiza-tion. Part Three, Portfolio Theory, introduces the classical quantitative port-folio risk optimization approach and new tools for optimizing portfolios,both in terms of total risk and in terms of risk relative to a selected bench-mark. Parts Four and Five, Equity Portfolio Management and Fixed IncomePortfolio Management, focus on specific factors and strategies used in equityand fixed income portfolio management, respectively. Part Six describes thebasics of financial derivative instruments and how financial derivatives canbe used for portfolio construction and risk management.

The material is presented at a high level but with practical real-worldexamples created with R and Microsoft Excel or provided by establishedportfolio software vendors, and should be accessible to a broad audience. Webelieve that practitioners and analysts who would like to get an overview oftools for portfolio analytics will find these themes—along with the examplesof applications and instructions for implementation—useful. At the sametime, we address the topics in this book in a rigorous way, and provide ref-erences to the original works, so the book should be of interest to academics,students, and researchers who need an updated and integrated view of port-folio construction and analytics.

SOFTWARE

We were wary of using a specific software package and turning this book intoa software tutorial because the popularity of different tools changes quickly.The examples in this book were created with Microsoft Excel and R, as wellas portfolio risk management software by Barclays Capital and FactSet. We

xxii PREFACE

assume basic familiarity with spreadsheets and Microsoft Excel. Because ofthe wide variability of online resources and tutorials for Microsoft Exceland the open source software package R, we do not provide tutorials withthe book;11 however, we try to provide hints for the implementation of theexamples with R and point to the libraries that have the analytics capabilitiesneeded to implement the examples.12

TEACHING

Portfolio Construction and Analytics covers finance and applied analyticaltechniques topics. It can be used as a textbook for upper-level undergraduateor lower-level graduate (such as MBA or master’s) courses with emphasis onmodeling, such as applied investments, financial analytics, or the decisionsciences. The book can be used also as a supplement in a special topics coursein quantitative methods or finance, as a reference for student projects, or asa self-study aid by students.

The book assumes that the reader has only very basic background infinance or quantitative methods, such as understanding of the time value ofmoney, knowledge of basic calculus, and comfort with numbers and metrics.Most analytical concepts necessary for understanding the notation or appli-cations are introduced and explained in footnotes or in specified references.This makes the book suitable for readers with a wide range of backgrounds.

Every chapter follows the same outline. The concepts are introduced inthe main body of the chapter, and illustrations are provided. Instructionsfor implementation of the examples are provided in footnotes. There is asummary that contains the most important discussion points at the end ofeach chapter.

A typical course may start with the material in Chapters 1 through 6.It can then cover Chapters 8 through 14, which discuss equity and fixedincome portfolio construction strategies. Chapters 7 and 15 contain specialtopics that would be of interest in more quantitatively oriented courses andmore advanced finance courses, respectively, or can be assigned for studentprojects. Depending on the amount of time an instructor has, Chapters 16

11A simple online search of “primer in R” will bring up a number of websites withhelpful introductions to the software.12When it comes to equity portfolio management, a free learning resource is providedby the Quantopian trading platform (https://www.quantopian.com), where readerscan create an account, view examples of the software implementation of popularinvestment strategies and risk metrics calculation (with Python), and modify them totest new strategies with real data.

https://www.quantopian.com

Preface xxiii

through 18 would be good to include in a course on investment management,as they discuss the fundamentals of portfolio risk management with financialderivative instruments.

DISCLOSURE

Frank J. Fabozzi is a member of two board fund complexes where Black-Rock Inc. is the manager of the funds. Mention of BlackRock’s analytics orproducts in this book should not be construed as any form of endorsement.

About the Authors

Dessislava A. Pachamanova is professor of analytics and computationalfinance and Zwerling Family Endowed Research Scholar at Babson College.Her research spans multiple areas, including portfolio risk management,simulation, high-performance and robust optimization, predictive analytics,and financial engineering. She has published dozens of articles in opera-tions research, finance, engineering, marketing and management journals,numerous book chapters, as well as two Wiley titles: Robust PortfolioOptimization and Management (2007) and Simulation and Optimizationin Finance: Modeling with MATLAB, @RISK, or VBA (2010), both partof the Frank J. Fabozzi Series in Finance. Dessislava’s academic research issupplemented by consulting and previous work in the financial industry,including projects with quantitative strategy groups at WestLB and Gold-man Sachs. She holds an AB in mathematics from Princeton University anda PhD from the Sloan School of Management at MIT.

Frank J. Fabozzi is professor of finance at EDHEC Business School and asenior scientific adviser at EDHEC-Risk Institute. Since 1984 he has servedas editor of the Journal of Portfolio Management. A CFA and CPA holder,Fabozzi is a trustee for both the BlackRock closed-end fund complex andthe equity-liquidity fund complex. He is the CFA Institute’s 2007 recipientof the C. Stewart Sheppard Award and the CFA Institute’s 2015 recipientof the James R. Vertin Award. Fabozzi was inducted into the Fixed IncomeAnalysts Society Hall of Fame in November 2002. He has served on thefaculty of Yale, MIT, and Princeton. The author and editor of numerousbooks in asset management, he earned a BA and MA in economics from TheCity College of New York and a doctorate in economics from the GraduateCenter of the City University of New York.

xxv

Acknowledgments

In writing a book that covers a wide range of topics in finance and drawson tools in statistics, simulation, and optimization, we were fortunate tohave received valuable help from a number of individuals.

We are very grateful to Andrew Geer, Ed Reis, Rick Barrett, and BillMcCoy of FactSet for creating the equity portfolio risk management examplein Chapter 12. In addition, we thank Ed Reis for generating the exhibits forthe example and for his careful proofreading of Chapter 12.

Special thanks are due also to Anthony Lazanas and Cenk Ural of Bar-clays for preparing the fixed income portfolio risk management example inChapter 14. The real-world examples are a true asset to the book.

We are indebted to Andrew Aziz of IBM Algorithmics and RobertBry of IBM for sharing materials about the IBM Algorithmics enterpriserisk management software and for spending time discussing with us thespecifics of systems for quantitative portfolio risk management and therole of cloud-based computing in making such systems more efficient andaffordable.

We thank Professor Alper Atamturk of the University of Californiaat Berkeley and Bloomberg, Matt Nuffort (formerly of Amazon), JackCahill, manager of the Cutler Center for Investments and Finance atBabson College, Hugh Crowther of Crowther Investment, and DelaneyGranizo-Mackenzie, Jess Stauth, David Edwards, Seong Lee, ScottSanderson, and John Fawcett of Quantopian for helpful discussions.

We also thank the R and Python developer communities, bloggers andcontributors to online forums, who have made such tremendous resourcesfor analytics available to the world free of charge, and whose advice andwillingness to share code helped with the creation of some of the examplesand illustrations in the book.

We appreciate the patience and understanding of Evan Burton and MegFreeborn of Wiley as we worked through changes in the timeline for thebook submission and several iterations of the table of contents.

This book would not have been possible without the support of ourfamilies—Christian, Anna, and Coleman (D.A.P.) and Donna, Karly,

xxvii

xxviii ACKNOWLEDGMENTS

Patricia, and Francesco (F.J.F.). We thank them for allowing us to spendprecious time away from them so that we could complete this book, andfor serving as a reminder that there is so much more to life.

Dessislava A. PachamanovaFrank J. Fabozzi