Portfolio
Construction
and Analytics
The Frank J. Fabozzi Series
Fixed Income Securities, Second Edition by Frank J. Fabozzi
Focus on Value: A Corporate and Investor Guide to Wealth Creation by
James 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 Frank
J. Fabozzi
Real Options and Option-Embedded Securities by William T. Moore
Capital Budgeting: Theory and Practice by Pamela P. Peterson and Frank
J. Fabozzi
The Exchange-Traded Funds Manual by Gary L. Gastineau
Professional Perspectives on Fixed Income Portfolio Management, Volume 3
edited by Frank J. Fabozzi
Investing in Emerging Fixed Income Markets edited by Frank J. Fabozzi and
Efstathia Pilarinu
Handbook of Alternative Assets by Mark J. P. Anson
The Global Money Markets by Frank J. Fabozzi, Steven V. Mann, and
Moorad Choudhry
The Handbook of Financial Instruments edited by Frank J. Fabozzi
Interest Rate, Term Structure, and Valuation Modeling edited by Frank
J. Fabozzi
Investment Performance Measurement by Bruce J. Feibel
The Handbook of Equity Style Management edited by T. Daniel Coggin and
Frank J. Fabozzi
The Theory and Practice of Investment Management edited by Frank
J. 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. Fabozzi
and Pamela P. Peterson
Measuring and Controlling Interest Rate and Credit Risk, Second Edition
by Frank J. Fabozzi, Steven V. Mann, and Moorad Choudhry
Professional Perspectives on Fixed Income Portfolio Management, Volume 4
edited by Frank J. Fabozzi
The Handbook of European Fixed Income Securities edited by Frank
J. Fabozzi and Moorad Choudhry
The Handbook of European Structured Financial Products edited by Frank
J. Fabozzi and Moorad Choudhry
The Mathematics of Financial Modeling and Investment Management by
Sergio 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 Daniel
Singer
Market Neutral Strategies edited by Bruce I. Jacobs and Kenneth N. Levy
Securities Finance: Securities Lending and Repurchase Agreements edited by
Frank 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 by
Frank J. Fabozzi, Sergio M. Focardi, and Petter N. Kolm
Advanced Bond Portfolio Management: Best Practices in Modeling and
Strategies edited by Frank J. Fabozzi, Lionel Martellini, and Philippe
Priaulet
Analysis of Financial Statements, Second Edition by Pamela P. Peterson and
Frank J. Fabozzi
Collateralized Debt Obligations: Structures and Analysis, Second Edition by
Douglas 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, Frank
J. Fabozzi, Sergio M. Focardi, and Teo Jasic
Developments in Collateralized Debt Obligations: New Products and
Insights by Douglas J. Lucas, Laurie S. Goodman, Frank J. Fabozzi, and
Rebecca J. Manning
Robust Portfolio Optimization and Management by Frank J. Fabozzi, Peter
N. Kolm, Dessislava A. Pachamanova, and Sergio M. Focardi
Advanced Stochastic Models, Risk Assessment, and Portfolio Optimizations
by Svetlozar T. Rachev, Stogan V. Stoyanov, and Frank J. Fabozzi
How to Select Investment Managers and Evaluate Performance by
G. Timothy Haight, Stephen O. Morrell, and Glenn E. Ross
Bayesian Methods in Finance by Svetlozar T. Rachev, John S. J. Hsu, Biliana
S. 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 Frank
J. 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 Brian
P. Lancaster, Glenn M. Schultz, and Frank J. Fabozzi
Handbook of Finance: Volume I: Financial Markets and Instruments edited
by Frank J. Fabozzi
Handbook of Finance: Volume II: Financial Management and Asset Management edited by Frank J. Fabozzi
Handbook of Finance: Volume III: Valuation, Financial Modeling, and
Quantitative Tools edited by Frank J. Fabozzi
Finance: Capital Markets, Financial Management, and Investment Management 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 Pamela
Peterson-Drake and Frank J. Fabozzi
Leveraged Finance: Concepts, Methods, and Trading of High-Yield Bonds,
Loans, and Derivatives by Stephen Antczak, Douglas Lucas, and Frank
J. Fabozzi
Modern Financial Systems: Theory and Applications by Edwin Neave
Institutional Investment Management: Equity and Bond Portfolio Strategies
and Applications by Frank J. Fabozzi
Robust Equity Portfolio Management + Website by Woo Chang Kim, Jang
Ho Kim, and Frank J. Fabozzi
Portfolio
Construction
and Analytics
DESSISLAVA A. PACHAMANOVA
FRANK 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 in
any form or by any means, electronic, mechanical, photocopying, recording, scanning, or
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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—dc23
LC record available at http://lccn.loc.gov/2015040278
Cover Design: Wiley
Cover Image: © kentoh/Shutterstock
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
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 1
Introduction to Portfolio Management and Analytics
1.1
1.2
1.3
1.4
1.5
Asset Classes and the Asset Allocation Decision
The Portfolio Management Process
1.2.1 Setting the Investment Objectives
1.2.2 Developing and Implementing a Portfolio Strategy
1.2.3 Monitoring the Portfolio
1.2.4 Adjusting the Portfolio
Traditional versus Quantitative Asset Management
Overview of Portfolio Analytics
1.4.1 Market Analytics
1.4.2 Financial Screening
1.4.3 Asset Allocation Models
1.4.4 Strategy Testing and Evaluating Portfolio
Performance
1.4.5 Systems for Portfolio Analytics
Outline of Topics Covered in the Book
1
1
4
4
6
8
9
9
10
12
15
16
17
20
22
PART ONE
Statistical Models of Risk and Uncertainty
CHAPTER 2
Random Variables, Probability Distributions, and Important
Statistical Concepts
2.1
2.2
What Is a Probability Distribution?
The Bernoulli Probability Distribution and Probability
Mass Functions
31
31
32
ix
x
CONTENTS
2.3
The Binomial Probability Distribution and Discrete
Distributions
2.4 The Normal Distribution and Probability Density
Functions
2.5 The Concept of Cumulative Probability
2.6 Describing Distributions
2.6.1 Measures of Central Tendency
2.6.2 Measures of Risk
2.6.3 Skew
2.6.4 Kurtosis
2.7 Dependence between Two Random Variables: Covariance
and Correlation
2.8 Sums of Random Variables
2.9 Joint Probability Distributions and Conditional
Probability
2.10 Copulas
2.11 From Probability Theory to Statistical Measurement:
Probability Distributions and Sampling
2.11.1 Central Limit Theorem
2.11.2 Confidence Intervals
2.11.3 Bootstrapping
2.11.4 Hypothesis Testing
CHAPTER 3
Important Probability Distributions
3.1
3.2
Examples of Probability Distributions
3.1.1 Notation Used in Describing Continuous
Probability Distributions
3.1.2 Discrete and Continuous Uniform Distributions
3.1.3 Student’s t Distribution
3.1.4 Lognormal Distribution
3.1.5 Poisson Distribution
3.1.6 Exponential Distribution
3.1.7 Chi-Square Distribution
3.1.8 Gamma Distribution
3.1.9 Beta Distribution
Modeling Financial Return Distributions
3.2.1 Elliptical Distributions
3.2.2 Stable Paretian Distributions
3.2.3 Generalized Lambda Distribution
34
38
41
44
44
47
54
55
55
57
61
64
66
70
71
72
73
77
79
79
80
82
83
85
87
88
90
90
91
92
94
96
xi
Contents
3.3
Modeling Tails of Financial Return Distributions
3.3.1 Generalized Extreme Value Distribution
3.3.2 Generalized Pareto Distribution
3.3.3 Extreme Value Models
CHAPTER 4
Statistical Estimation Models
4.1
4.2
4.3
4.4
4.5
Commonly Used Return Estimation Models
Regression Analysis
4.2.1 A Simple Regression Example
4.2.2 Regression Applications in the Investment
Management Process
Factor Analysis
Principal Components Analysis
Autoregressive Conditional Heteroscedastic Models
98
98
99
101
106
106
108
109
114
116
118
125
PART TWO
Simulation and Optimization Modeling
CHAPTER 5
Simulation Modeling
5.1
5.2
5.3
5.4
Monte Carlo Simulation: A Simple Example
5.1.1 Selecting Probability Distributions for the Inputs
5.1.2 Interpreting Monte Carlo Simulation Output
Why Use Simulation?
5.2.1 Multiple Input Variables and Compounding
Distributions
5.2.2 Incorporating Correlations
5.2.3 Evaluating Decisions
How Many Scenarios?
Random Number Generation
CHAPTER 6
Optimization Modeling
6.1
Optimization Formulations
6.1.1 Minimization versus Maximization
6.1.2 Local versus Global Optima
6.1.3 Multiple Objectives
133
133
135
137
140
141
142
144
147
149
151
152
154
155
156
xii
CONTENTS
6.2
6.3
6.4
6.5
6.6
Important Types of Optimization Problems
6.2.1 Convex Programming
6.2.2 Linear Programming
6.2.3 Quadratic Programming
6.2.4 Second-Order Cone Programming
6.2.5 Integer and Mixed Integer Programming
A Simple Optimization Problem Formulation Example:
Portfolio Allocation
Optimization Algorithms
Optimization Software
A Software Implementation Example
6.6.1 Optimization with Excel Solver
6.6.2 Solution to the Portfolio Allocation Example
CHAPTER 7
Optimization under Uncertainty
7.1
7.2
7.3
Dynamic Programming
Stochastic Programming
7.2.1 Multistage Models
7.2.2 Mean-Risk Stochastic Models
7.2.3 Chance-Constrained Models
Robust Optimization
157
157
158
159
160
161
161
166
168
170
171
175
180
181
183
184
189
191
194
PART THREE
Portfolio Theory
CHAPTER 8
Asset Diversification
8.1
8.2
8.3
8.4
8.5
8.6
The Case for Diversification
The Classical Mean-Variance Optimization Framework
Efficient Frontiers
Alternative Formulations of the Classical Mean-Variance
Optimization Problem
8.4.1 Expected Return Formulation
8.4.2 Risk Aversion Formulation
The Capital Market Line
Expected Utility Theory
8.6.1 Quadratic Utility Function
8.6.2 Linear Utility Function
8.6.3 Exponential Utility Function
203
204
208
212
215
215
215
216
220
221
223
224
xiii
Contents
8.7
8.6.4 Power Utility Function
8.6.5 Logarithmic Utility Function
Diversification Redefined
CHAPTER 9
Factor Models
9.1
9.2
9.3
9.4
9.5
9.6
9.7
9.8
Factor Models in the Financial Economics Literature
Mean-Variance Optimization with Factor Models
Factor Selection in Practice
Factor Models for Alpha Construction
Factor Models for Risk Estimation
9.5.1 Macroeconomic Factor Models
9.5.2 Fundamental Factor Models
9.5.3 Statistical Factor Models
9.5.4 Hybrid Factor Models
9.5.5 Selecting the "Right" Factor Model
Data Management and Quality Issues
9.6.1 Data Alignment
9.6.2 Survival Bias
9.6.3 Look-Ahead Bias
9.6.4 Data Snooping
Risk Decomposition, Risk Attribution, and Performance
Attribution
Factor Investing
CHAPTER 10
Benchmarks and the Use of Tracking Error in Portfolio Construction
10.1 Tracking Error versus Alpha: Calculation and
Interpretation
10.2 Forward-Looking versus Backward-Looking Tracking
Error
10.3 Tracking Error and Information Ratio
10.4 Predicted Tracking Error Calculation
10.4.1 Variance-Covariance Method for Tracking Error
Calculation
10.4.2 Tracking Error Calculation Based on a
Multifactor Model
10.5 Benchmarks and Indexes
10.5.1 Market Indexes
10.5.2 Noncapitalization Weighted Indexes
10.6 Smart Beta Investing
224
224
226
232
233
236
239
243
245
245
246
248
250
250
251
252
253
253
254
254
256
260
261
264
265
265
266
266
268
268
270
272
xiv
CONTENTS
PART FOUR
Equity Portfolio Management
CHAPTER 11
Advances in Quantitative Equity Portfolio Management
11.1 Portfolio Constraints Commonly Used in Practice
11.1.1 Long-Only (No-Short-Selling) Constraints
11.1.2 Holding Constraints
11.1.3 Turnover Constraints
11.1.4 Factor Constraints
11.1.5 Cardinality Constraints
11.1.6 Minimum Holding and Transaction Size
Constraints
11.1.7 Round Lot Constraints
11.1.8 Tracking Error Constraints
11.1.9 Soft Constraints
11.1.10 Misalignment Caused by Constraints
11.2 Portfolio Optimization with Tail Risk Measures
11.2.1 Portfolio Value-at-Risk Optimization
11.2.2 Portfolio Conditional Value-at-Risk Optimization
11.3 Incorporating Transaction Costs
11.3.1 Linear Transaction Costs
11.3.2 Piecewise-Linear Transaction Costs
11.3.3 Quadratic Transaction Costs
11.3.4 Fixed Transaction Costs
11.3.5 Market Impact Costs
11.4 Multiaccount Optimization
11.5 Incorporating Taxes
11.6 Robust Parameter Estimation
11.7 Portfolio Resampling
11.8 Robust Portfolio Optimization
281
282
283
283
284
284
286
287
288
290
291
291
291
292
294
297
299
300
302
302
303
304
308
312
314
317
CHAPTER 12
Factor-Based Equity Portfolio Construction and
Performance Evaluation
325
12.1 Equity Factors Used in Practice
12.1.1 Fundamental Factors
12.1.2 Macroeconomic Factors
12.1.3 Technical Factors
12.1.4 Additional Factors
325
326
327
327
327
Contents
12.2 Stock Screens
12.3 Portfolio Selection
12.3.1 Ad-Hoc Portfolio Selection
12.3.2 Stratification
12.3.3 Factor Exposure Targeting
12.4 Risk Decomposition
12.5 Stress Testing
12.6 Portfolio Performance Evaluation
12.7 Risk Forecasts and Simulation
xv
328
331
331
332
333
334
343
346
350
PART FIVE
Fixed Income Portfolio Management
CHAPTER 13
Fundamentals of Fixed Income Portfolio Management
13.1 Fixed Income Instruments and Major Sectors
of the Bond Market
13.1.1 Treasury Securities
13.1.2 Federal Agency Securities
13.1.3 Corporate Bonds
13.1.4 Municipal Bonds
13.1.5 Structured Products
13.2 Features of Fixed Income Securities
13.2.1 Term to Maturity and Maturity
13.2.2 Par Value
13.2.3 Coupon Rate
13.2.4 Bond Valuation and Yield
13.2.5 Provisions for Paying Off Bonds
13.2.6 Bondholder Option Provisions
13.3 Major Risks Associated with Investing in Bonds
13.3.1 Interest Rate Risk
13.3.2 Call and Prepayment Risk
13.3.3 Credit Risk
13.3.4 Liquidity Risk
13.4 Fixed Income Analytics
13.4.1 Measuring Interest Rate Risk
13.4.2 Measuring Spread Risk
13.4.3 Measuring Credit Risk
13.4.4 Estimating Fixed Income Portfolio Risk
Using Simulation
361
361
362
363
363
364
364
365
365
366
366
367
368
370
371
371
372
373
374
375
375
383
384
384
xvi
CONTENTS
13.5 The Spectrum of Fixed Income Portfolio Strategies
13.5.1 Pure Bond Indexing Strategy
13.5.2 Enhanced Indexing/Primary Factor Matching
13.5.3 Enhanced Indexing/Minor Factor Mismatches
13.5.4 Active Management/Larger Factor Mismatches
13.5.5 Active Management/Full-Blown Active
13.5.6 Smart Beta Strategies for Fixed Income Portfolios
13.6 Value-Added Fixed Income Strategies
13.6.1 Interest Rate Expectations Strategies
13.6.2 Yield Curve Strategies
13.6.3 Inter- and Intra-sector Allocation Strategies
13.6.4 Individual Security Selection Strategies
CHAPTER 14
Factor-Based Fixed Income Portfolio Construction and Evaluation
14.1 Fixed Income Factors Used in Practice
14.1.1 Term Structure Factors
14.1.2 Credit Spread Factors
14.1.3 Currency Factors
14.1.4 Emerging Market Factors
14.1.5 Volatility Factors
14.1.6 Prepayment Factors
14.2 Portfolio Selection
14.2.1 Stratification Approach
14.2.2 Optimization Approach
14.2.3 Portfolio Rebalancing
14.3 Risk Decomposition
CHAPTER 15
Constructing Liability-Driven Portfolios
15.1 Risks Associated with Liabilities
15.1.1 Interest Rate Risk
15.1.2 Inflation Risk
15.1.3 Longevity Risk
15.2 Liability-Driven Strategies of Life Insurance Companies
15.2.1 Immunization
15.2.2 Advanced Optimization Approaches
15.2.3 Constructing Replicating Portfolios
386
387
388
389
389
390
390
391
391
392
393
394
398
398
399
400
401
401
402
402
402
403
405
408
410
420
421
421
422
423
423
424
435
437
Contents
15.3 Liability-Driven Strategies of Defined Benefit
Pension Funds
15.3.1 High-Grade Bond Portfolio Solution
15.3.2 Including Other Assets
15.3.3 Advanced Modeling Strategies
xvii
438
439
442
443
PART SIX
Derivatives and Their Application to Portfolio Management
CHAPTER 16
Basics of Financial Derivatives
16.1 Overview of the Use of Derivatives in Portfolio
Management
16.2 Forward and Futures Contracts
16.2.1 Risk and Return of Forward/Futures Position
16.2.2 Leveraging Aspect of Futures
16.2.3 Pricing of Futures and Forward Contracts
16.3 Options
16.3.1 Risk and Return Characteristics of Options
16.3.2 Option Pricing Models
16.4 Swaps
16.4.1 Interest Rate Swaps
16.4.2 Equity Swaps
16.4.3 Credit Default Swaps
CHAPTER 17
Using Derivatives in Equity Portfolio Management
17.1 Stock Index Futures and Portfolio Management
Applications
17.1.1 Basic Features of Stock Index Futures
17.1.2 Theoretical Price of a Stock Index Futures
Contract
17.1.3 Portfolio Management Strategies with Stock
Index Futures
17.2 Equity Options and Portfolio Management
Applications
17.2.1 Types of Equity Options
17.2.2 Equity Portfolio Management Strategies
with Options
17.3 Equity Swaps
449
449
451
453
453
454
459
460
470
485
485
486
487
490
490
490
491
494
504
504
506
511
xviii
CONTENTS
CHAPTER 18
Using Derivatives in Fixed Income Portfolio Management
18.1 Controlling Interest Rate Risk Using Treasury Futures
18.1.1 Strategies for Controlling Interest Rate Risk with
Treasury Futures
18.1.2 Pricing of Treasury Futures
18.2 Controlling Interest Rate Risk Using Treasury
Futures Options
18.2.1 Strategies for Controlling Interest Rate Risk Using
Treasury Futures Options
18.2.2 Pricing Models for Treasury Futures Options
18.3 Controlling Interest Rate Risk Using Interest Rate Swaps
18.3.1 Strategies for Controlling Interest Rate Risk Using
Interest Rate Swaps
18.3.2 Pricing of Interest Rate Swaps
18.4 Controlling Credit Risk with Credit Default Swaps
18.4.1 Strategies for Controlling Credit Risk with Credit
Default Swaps
18.4.2 General Principles for Valuing a Single-Name
Credit Default Swap
515
515
518
520
521
524
526
527
528
530
532
534
535
Appendix: Basic Linear Algebra Concepts
541
References
549
Index
563
Preface
nalytics” and “Big Data” have become buzzwords in many industries,
and have dominated the news over the past few years. In finance, analytics and big data have been around for a long time, even if they were described
with different terms. As J.R. Lowry, chief operating officer of State Street
Global Exchange, stated in a 2014 interview published in the MIT Sloan
Management Review, “In general, data and analytics have pervaded our
business for many, many years, but it wasn’t something that we were focused
on in any kind of coherent way.”
The need to focus on investment analytics in a coherent way has never
been greater. In the aftermath of the 2007–2009 financial crisis, there has
been a tremendous amount of regulatory change. Like most industries, the
financial industry is trying to cope with the challenges of managing big data
and the risks associated with using models. Many asset management firms
face increasing pressure to address important questions such as
“
A
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 a
new business, State Street Global Exchange (SSGX), which applies “a wrapper of information, insights and analytics around the investment process,”
and provides a “more purposeful approach to data and analytics across
the company.”1 SSGX is a center that has pulled in software capabilities
and analytics groups focused on risk, as well as electronic trading platforms
focused on foreign exchange, fixed income, and derivatives trading.
Portfolio and risk analytics platforms are offered by investment product
providers such as Barclays (the POINT Advanced Analytics Platform)2 and
BlackRock (the Aladdin Platform)3 with a similar goal of combining sophisticated risk analytics with comprehensive portfolio management, trading
1
Ferguson (2014).
See https://ecommerce.barcap.com/point/point.dxml.
3
See https://www.blackrock.com/aladdin/offerings/aladdin-overview.
2
xix
xx
PREFACE
and operations tools. Longtime portfolio software vendors (Axioma, IBM
Algorithmics, MSCI Barra, and Northfield Information Services) and data
providers (Bloomberg, FactSet, Thomson Reuters) are adding both advanced
analytics tools and the ability to link to various data sources. New partnerships are being formed—for example, financial data provider Thomson
Reuters joined forces with Palantir Technologies, a leading Silicon Valley
big data technology company, to create QA Studio, a solution for quantitative research that combines powerful analytics and intuitive visualizations
to help with the generation of investment ideas.4 The development of free
open source software such as the statistical modeling environment R5 and
the open source programming environment Python6 with libraries for financial applications has greatly improved accessibility to analytical tools and
has reduced the costs of implementing portfolio analytics solutions.
In this book, we often refer to the traditional asset management
company model, in which the focus is on the selection of star portfolio managers in charge of different portions of a firm’s funds under management.
However, new technologies have been disrupting the investment industry
as a whole. The bundling of asset management practice and software
platform offerings is a recent phenomenon, as is the democratization of
access to financial data7 and trading opportunities.8 The popularity of
automated investment services companies, also called robo advisors,9 has
been increasing. New-generation asset management companies include
Quantopian,10 which provides an analytics and trading platform and
crowdsources investment ideas from contributors from all over the world,
with the goal of rewarding top performers and applying tested strategies
to asset management instead of hiring and managing individual portfolio
managers. The core of Quantopian’s strategy involves providing useful
market and stock fundamentals data, as well as a tool for backtesting,
zipline, which has been made open source (free) to help create and support
a community of contributors.
Nobody can tell what the future of the portfolio management industry
will look like but it certainly seems inevitable that data and analytics will
play a major role in it.
4
See http://alphanow.thomsonreuters.com/solutions/qa-studio/.
See https://www.r-project.org/.
6
See https://www.python.org/.
7
See https://www.quandl.com/. Quandl offers free financial data.
8
See https://www.interactivebrokers.com/.
9
Examples of robo advisors include Betterment, WealthFront, WiseBanyan, Personal
Capital, Motif Investing, FutureAdvisor, and Bloom.
10
See https://www.quantopian.com/.
5
Preface
xxi
CENTRAL THEMES
Portfolio Construction and Analytics attempts to look at the analytics process at investment firms from multiple perspectives: the data management
side, the modeling side, and the software resources side. It reviews many
widely used approaches to portfolio analytics and discusses new trends in
metrics, modeling approaches, and portfolio analytics system design. The
theoretical underpinnings of some of the modeling approaches are provided
for context; however, our goal is to emphasize how such models are used in
practice.
The book contains 18 chapters in six parts. Part One, Statistical Models
of Risk and Uncertainty, contains the fundamental statistical modeling concepts necessary to understand the modeling and measurement of portfolio
risk. Part Two, Simulation and Optimization Modeling, explains two important modeling techniques for constructing portfolios with desired characteristics and evaluating their risk and performance—simulation and optimization. Part Three, Portfolio Theory, introduces the classical quantitative portfolio risk optimization approach and new tools for optimizing portfolios,
both in terms of total risk and in terms of risk relative to a selected benchmark. Parts Four and Five, Equity Portfolio Management and Fixed Income
Portfolio Management, focus on specific factors and strategies used in equity
and fixed income portfolio management, respectively. Part Six describes the
basics of financial derivative instruments and how financial derivatives can
be used for portfolio construction and risk management.
The material is presented at a high level but with practical real-world
examples created with R and Microsoft Excel or provided by established
portfolio software vendors, and should be accessible to a broad audience. We
believe that practitioners and analysts who would like to get an overview of
tools for portfolio analytics will find these themes—along with the examples
of applications and instructions for implementation—useful. At the same
time, we address the topics in this book in a rigorous way, and provide references to the original works, so the book should be of interest to academics,
students, and researchers who need an updated and integrated view of portfolio construction and analytics.
SOFTWARE
We were wary of using a specific software package and turning this book into
a software tutorial because the popularity of different tools changes quickly.
The examples in this book were created with Microsoft Excel and R, as well
as portfolio risk management software by Barclays Capital and FactSet. We
xxii
PREFACE
assume basic familiarity with spreadsheets and Microsoft Excel. Because of
the wide variability of online resources and tutorials for Microsoft Excel
and the open source software package R, we do not provide tutorials with
the book;11 however, we try to provide hints for the implementation of the
examples with R and point to the libraries that have the analytics capabilities
needed to implement the examples.12
TEACHING
Portfolio Construction and Analytics covers finance and applied analytical
techniques topics. It can be used as a textbook for upper-level undergraduate
or lower-level graduate (such as MBA or master’s) courses with emphasis on
modeling, such as applied investments, financial analytics, or the decision
sciences. The book can be used also as a supplement in a special topics course
in quantitative methods or finance, as a reference for student projects, or as
a self-study aid by students.
The book assumes that the reader has only very basic background in
finance or quantitative methods, such as understanding of the time value of
money, knowledge of basic calculus, and comfort with numbers and metrics.
Most analytical concepts necessary for understanding the notation or applications 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 in
the main body of the chapter, and illustrations are provided. Instructions
for implementation of the examples are provided in footnotes. There is a
summary that contains the most important discussion points at the end of
each 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 fixed
income portfolio construction strategies. Chapters 7 and 15 contain special
topics that would be of interest in more quantitatively oriented courses and
more advanced finance courses, respectively, or can be assigned for student
projects. Depending on the amount of time an instructor has, Chapters 16
11
A simple online search of “primer in R” will bring up a number of websites with
helpful introductions to the software.
12
When it comes to equity portfolio management, a free learning resource is provided
by the Quantopian trading platform (https://www.quantopian.com), where readers
can create an account, view examples of the software implementation of popular
investment strategies and risk metrics calculation (with Python), and modify them to
test new strategies with real data.
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 financial
derivative instruments.
DISCLOSURE
Frank J. Fabozzi is a member of two board fund complexes where BlackRock Inc. is the manager of the funds. Mention of BlackRock’s analytics or
products in this book should not be construed as any form of endorsement.
About the Authors
Dessislava A. Pachamanova is professor of analytics and computational
finance 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 operations research, finance, engineering, marketing and management journals,
numerous book chapters, as well as two Wiley titles: Robust Portfolio
Optimization and Management (2007) and Simulation and Optimization
in Finance: Modeling with MATLAB, @RISK, or VBA (2010), both part
of the Frank J. Fabozzi Series in Finance. Dessislava’s academic research is
supplemented by consulting and previous work in the financial industry,
including projects with quantitative strategy groups at WestLB and Goldman Sachs. She holds an AB in mathematics from Princeton University and
a PhD from the Sloan School of Management at MIT.
Frank J. Fabozzi is professor of finance at EDHEC Business School and a
senior scientific adviser at EDHEC-Risk Institute. Since 1984 he has served
as editor of the Journal of Portfolio Management. A CFA and CPA holder,
Fabozzi is a trustee for both the BlackRock closed-end fund complex and
the equity-liquidity fund complex. He is the CFA Institute’s 2007 recipient
of the C. Stewart Sheppard Award and the CFA Institute’s 2015 recipient
of the James R. Vertin Award. Fabozzi was inducted into the Fixed Income
Analysts Society Hall of Fame in November 2002. He has served on the
faculty of Yale, MIT, and Princeton. The author and editor of numerous
books in asset management, he earned a BA and MA in economics from The
City College of New York and a doctorate in economics from the Graduate
Center of the City University of New York.
xxv
Acknowledgments
n writing a book that covers a wide range of topics in finance and draws
on tools in statistics, simulation, and optimization, we were fortunate to
have received valuable help from a number of individuals.
We are very grateful to Andrew Geer, Ed Reis, Rick Barrett, and Bill
McCoy of FactSet for creating the equity portfolio risk management example
in Chapter 12. In addition, we thank Ed Reis for generating the exhibits for
the example and for his careful proofreading of Chapter 12.
Special thanks are due also to Anthony Lazanas and Cenk Ural of Barclays for preparing the fixed income portfolio risk management example in
Chapter 14. The real-world examples are a true asset to the book.
We are indebted to Andrew Aziz of IBM Algorithmics and Robert
Bry of IBM for sharing materials about the IBM Algorithmics enterprise
risk management software and for spending time discussing with us the
specifics of systems for quantitative portfolio risk management and the
role of cloud-based computing in making such systems more efficient and
affordable.
We thank Professor Alper Atamturk of the University of California
at Berkeley and Bloomberg, Matt Nuffort (formerly of Amazon), Jack
Cahill, manager of the Cutler Center for Investments and Finance at
Babson College, Hugh Crowther of Crowther Investment, and Delaney
Granizo-Mackenzie, Jess Stauth, David Edwards, Seong Lee, Scott
Sanderson, and John Fawcett of Quantopian for helpful discussions.
We also thank the R and Python developer communities, bloggers and
contributors to online forums, who have made such tremendous resources
for analytics available to the world free of charge, and whose advice and
willingness to share code helped with the creation of some of the examples
and illustrations in the book.
We appreciate the patience and understanding of Evan Burton and Meg
Freeborn of Wiley as we worked through changes in the timeline for the
book submission and several iterations of the table of contents.
This book would not have been possible without the support of our
families—Christian, Anna, and Coleman (D.A.P.) and Donna, Karly,
I
xxvii
xxviii
ACKNOWLEDGMENTS
Patricia, and Francesco (F.J.F.). We thank them for allowing us to spend
precious time away from them so that we could complete this book, and
for serving as a reminder that there is so much more to life.
Dessislava A. Pachamanova
Frank J. Fabozzi