QUANTITATIVE

INVESTMENT

ANALYSIS

Second Edition

Richard A. DeFusco, CFA

Dennis W. McLeavey, CFA

Jerald E. Pinto, CFA

David E. Runkle, CFA

John Wiley & Sons, Inc.

QUANTITATIVE

INVESTMENT

ANALYSIS

CFA Institute is the premier association for investment professionals around the world,

with over 85,000 members in 129 countries. Since 1963 the organization has developed

and administered the renowned Chartered Financial Analyst Program. With a rich history

of leading the investment profession, CFA Institute has set the highest standards in ethics,

education, and professional excellence within the global investment community, and is the

foremost authority on investment profession conduct and practice.

Each book in the CFA Institute Investment Series is geared toward industry practitioners

along with graduate-level finance students and covers the most important topics in the

industry. The authors of these cutting-edge books are themselves industry professionals and

academics and bring their wealth of knowledge and expertise to this series.

QUANTITATIVE

INVESTMENT

ANALYSIS

Second Edition

Richard A. DeFusco, CFA

Dennis W. McLeavey, CFA

Jerald E. Pinto, CFA

David E. Runkle, CFA

John Wiley & Sons, Inc.

Copyright c 2004, 2007 by CFA Institute. All rights reserved.

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

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Quantitative investment analysis / Richard A. DeFusco . . . [et al.].—

2nd ed.

p. cm.—(The CFA Institute investment series)

Includes bibliographical references.

ISBN-13 978-0-470-05220-4 (cloth)

ISBN-10 0-470-05220-1 (cloth)

1. Investment analysis—Mathematical models. I. DeFusco, Richard

Armand.

HG4529.Q35 2006

332.601’5195—dc22

2006052578

Printed in the United States of America.

10

9 8

7 6 5 4

3 2 1

To Margo, Rachel, and Rebekah

R.A.D.

To Jan, Christine, and Andy

D.W.M.

In memory of Irwin T. Vanderhoof, CFA

J.E.P.

To Patricia, Anne, and Sarah

D.E.R.

CONTENTS

Foreword

xiii

Acknowledgments

xvii

Introduction

CHAPTER 1

The Time Value of Money

1 Introduction

2 Interest Rates: Interpretation

3 The Future Value of a Single Cash Flow

3.1 The Frequency of Compounding

3.2 Continuous Compounding

3.3 Stated and Effective Rates

4 The Future Value of a Series of Cash Flows

4.1 Equal Cash Flows—Ordinary Annuity

4.2 Unequal Cash Flows

5 The Present Value of a Single Cash Flow

5.1 Finding the Present Value of a Single Cash Flow

5.2 The Frequency of Compounding

6 The Present Value of a Series of Cash Flows

6.1 The Present Value of a Series of Equal Cash Flows

6.2 The Present Value of an Infinite Series of Equal Cash Flows—Perpetuity

6.3 Present Values Indexed at Times Other Than t = 0

6.4 The Present Value of a Series of Unequal Cash Flows

7 Solving for Rates, Number of Periods, or Size of Annuity Payments

7.1 Solving for Interest Rates and Growth Rates

7.2 Solving for the Number of Periods

7.3 Solving for the Size of Annuity Payments

7.4 Review of Present and Future Value Equivalence

7.5 The Cash Flow Additivity Principle

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CHAPTER 2

Discounted Cash Flow Applications

1 Introduction

2 Net Present Value and Internal Rate of Return

2.1 Net Present Value and the Net Present Value Rule

2.2 The Internal Rate of Return and the Internal Rate of

Return Rule

2.3 Problems with the IRR Rule

3 Portfolio Return Measurement

3.1 Money-Weighted Rate of Return

3.2 Time-Weighted Rate of Return

4 Money Market Yields

CHAPTER 3

Statistical Concepts and Market Returns

1 Introduction

2 Some Fundamental Concepts

2.1 The Nature of Statistics

2.2 Populations and Samples

2.3 Measurement Scales

3 Summarizing Data Using Frequency Distributions

4 The Graphic Presentation of Data

4.1 The Histogram

4.2 The Frequency Polygon and the Cumulative Frequency

Distribution

5 Measures of Central Tendency

5.1 The Arithmetic Mean

5.2 The Median

5.3 The Mode

5.4 Other Concepts of Mean

6 Other Measures of Location: Quantiles

6.1 Quartiles, Quintiles, Deciles, and Percentiles

6.2 Quantiles in Investment Practice

7 Measures of Dispersion

7.1 The Range

7.2 The Mean Absolute Deviation

7.3 Population Variance and Population Standard

Deviation

7.4 Sample Variance and Sample Standard Deviation

7.5 Semivariance, Semideviation, and Related Concepts

7.6 Chebyshev’s Inequality

7.7 Coefficient of Variation

7.8 The Sharpe Ratio

8 Symmetry and Skewness in Return Distributions

9 Kurtosis in Return Distributions

10 Using Geometric and Arithmetic Means

Contents

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CHAPTER 4

Probability Concepts

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Introduction

Probability, Expected Value, and Variance

Portfolio Expected Return and Variance of Return

Topics in Probability

4.1 Bayes’ Formula

4.2 Principles of Counting

CHAPTER 5

Common Probability Distributions

1 Introduction

2 Discrete Random Variables

2.1 The Discrete Uniform Distribution

2.2 The Binomial Distribution

3 Continuous Random Variables

3.1 Continuous Uniform Distribution

3.2 The Normal Distribution

3.3 Applications of the Normal Distribution

3.4 The Lognormal Distribution

4 Monte Carlo Simulation

CHAPTER 6

Sampling and Estimation

1 Introduction

2 Sampling

2.1 Simple Random Sampling

2.2 Stratified Random Sampling

2.3 Time-Series and Cross-Sectional Data

3 Distribution of the Sample Mean

3.1 The Central Limit Theorem

4 Point and Interval Estimates of the Population Mean

4.1 Point Estimators

4.2 Confidence Intervals for the Population Mean

4.3 Selection of Sample Size

5 More on Sampling

5.1 Data-Mining Bias

5.2 Sample Selection Bias

5.3 Look-Ahead Bias

5.4 Time-Period Bias

CHAPTER 7

Hypothesis Testing

1 Introduction

2 Hypothesis Testing

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3 Hypothesis Tests Concerning the Mean

3.1 Tests Concerning a Single Mean

3.2 Tests Concerning Differences between Means

3.3 Tests Concerning Mean Differences

4 Hypothesis Tests Concerning Variance

4.1 Tests Concerning a Single Variance

4.2 Tests Concerning the Equality (Inequality) of Two Variances

5 Other Issues: Nonparametric Inference

5.1 Tests Concerning Correlation: The Spearman Rank

Correlation Coefficient

5.2 Nonparametric Inference: Summary

CHAPTER 8

Correlation and Regression

1 Introduction

2 Correlation Analysis

2.1 Scatter Plots

2.2 Correlation Analysis

2.3 Calculating and Interpreting the Correlation Coefficient

2.4 Limitations of Correlation Analysis

2.5 Uses of Correlation Analysis

2.6 Testing the Significance of the Correlation Coefficient

3 Linear Regression

3.1 Linear Regression with One Independent Variable

3.2 Assumptions of the Linear Regression Model

3.3 The Standard Error of Estimate

3.4 The Coefficient of Determination

3.5 Hypothesis Testing

3.6 Analysis of Variance in a Regression with One Independent Variable

3.7 Prediction Intervals

3.8 Limitations of Regression Analysis

CHAPTER 9

Multiple Regression and Issues in Regression Analysis

1 Introduction

2 Multiple Linear Regression

2.1 Assumptions of the Multiple Linear Regression Model

2.2 Predicting the Dependent Variable in a Multiple Regression Model

2.3 Testing Whether All Population Regression Coefficients Equal Zero

2.4 Adjusted R 2

3 Using Dummy Variables in Regressions

4 Violations of Regression Assumptions

4.1 Heteroskedasticity

4.2 Serial Correlation

4.3 Multicollinearity

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Contents

4.4 Heteroskedasticity, Serial Correlation, Multicollinearity:

Summarizing the Issues

5 Model Specification and Errors in Specification

5.1 Principles of Model Specification

5.2 Misspecified Functional Form

5.3 Time-Series Misspecification (Independent Variables Correlated

with Errors)

5.4 Other Types of Time-Series Misspecification

6 Models with Qualitative Dependent Variables

CHAPTER 10

Time-Series Analysis

1 Introduction

2 Challenges of Working with Time Series

3 Trend Models

3.1 Linear Trend Models

3.2 Log-Linear Trend Models

3.3 Trend Models and Testing for Correlated Errors

4 Autoregressive (AR) Time-Series Models

4.1 Covariance-Stationary Series

4.2 Detecting Serially Correlated Errors in an Autoregressive Model

4.3 Mean Reversion

4.4 Multiperiod Forecasts and the Chain Rule of Forecasting

4.5 Comparing Forecast Model Performance

4.6 Instability of Regression Coefficients

5 Random Walks and Unit Roots

5.1 Random Walks

5.2 The Unit Root Test of Nonstationarity

6 Moving-Average Time-Series Models

6.1 Smoothing Past Values with an n-Period Moving Average

6.2 Moving-Average Time-Series Models for Forecasting

7 Seasonality in Time-Series Models

8 Autoregressive Moving-Average Models

9 Autoregressive Conditional Heteroskedasticity Models

10 Regressions with More than One Time Series

11 Other Issues in Time Series

12 Suggested Steps in Time-Series Forecasting

CHAPTER 11

Portfolio Concepts

1 Introduction

2 Mean–Variance Analysis

2.1 The Minimum-Variance Frontier and Related Concepts

2.2 Extension to the Three-Asset Case

2.3 Determining the Minimum-Variance Frontier for Many Assets

2.4 Diversification and Portfolio Size

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Contents

2.5 Portfolio Choice with a Risk-Free Asset

2.6 The Capital Asset Pricing Model

2.7 Mean–Variance Portfolio Choice Rules: An Introduction

3 Practical Issues in Mean–Variance Analysis

3.1 Estimating Inputs for Mean–Variance Optimization

3.2 Instability in the Minimum-Variance Frontier

4 Multifactor Models

4.1 Factors and Types of Multifactor Models

4.2 The Structure of Macroeconomic Factor Models

4.3 Arbitrage Pricing Theory and the Factor Model

4.4 The Structure of Fundamental Factor Models

4.5 Multifactor Models in Current Practice

4.6 Applications

4.7 Concluding Remarks

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Appendices

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References

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Glossary

527

About the CFA Program

541

About the Authors

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Index

545

FOREWORD

HOW QUANTITATIVE INVESTMENT ANALYSIS

CAN IMPROVE PORTFOLIO DECISION MAKING

I am a Quant. By my own self-admission, I use quantitative investment techniques in the

management of investment portfolios. However, when I tell people that I am a Quant, they

often respond: ‘‘But Mark, aren’t you a lawyer?’’ Well, yes, but . . .

The fact is that Quants come from all walks of life. Whether we are called Quants,

Quant Jocks, Gear Heads, Computer Monkeys, or any of the other monikers that are attached

to investors who like to scribble equations on a piece of paper, we all share a common

denominator—the use of quantitative analysis to make better investment decisions. You don’t

have to be a rocket scientist with a Ph.D. in an esoteric mathematical field to be a Quant

(although there are, I suspect, several former rocket scientists who have found working in the

financial markets to be both fun and profitable). Anyone can become a Quant—even a lawyer.

But let’s take a step back. Why should any investor want to use quantitative tools in the

management of investment portfolios? There are three reasons why Quants are so popular.

First, the financial markets are very complicated places. There are many interwoven

variables that can affect the price of securities in an investment portfolio. For example, the

stock price of a public company can be affected by macroeconomic factors such as the level

of interest rates, current account deficits, government spending, and economic cycles. These

factors may affect the cost of capital at which a corporation finances its new projects, or

influence the spending patterns of the company’s customers, or provide economic impetus

through government spending programs.

In addition to macro variables, the value of a company’s stock can be affected by factors

that are peculiar to the company itself. Factors such as cash flow, working capital, bookto-market value, earnings growth rates, dividend policy, and debt-to-equity ratios affect the

individual value of each public company. These are considered to be the fundamental factors

that have an impact on the specific company as opposed to the broader stock market.

Then we come to the financial market variables that affect a company’s valuation. Its

‘‘beta’’ or measure of systematic risk will impact the expected return for the company and, in

turn, its stock price. The famous Capital Asset Pricing Model that measures a stock’s beta is

really just a linear regression equation of the type described in Chapter 8.

Last, there are behavioral variables that can affect security values. Such behavior as

herding, overconfidence, overreaction to earnings announcements, and momentum trading

can all impact the price of a company’s stock. These behavioral variables can have a lasting

impact on a stock price (remember the technology bubble of 1998–2001 when tech stocks

were going to take over the world?) as well as generate a significant amount of ‘‘noise’’ around

a security’s true value.

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Foreword

Considering all of these variables together at one time to determine the true value of

a security can be an overwhelming task without some framework in which to analyze their

impact. It is simply not possible for the human mind alone (at least, not mine) to be able

to weigh the impact of individual company specific factors such as price-to-earnings ratios,

macroeconomic variables such as government spending programs, investor behavioral patterns

such as momentum trading, and other potentially influential variables in a rigorous fashion

within the human brain.

This is where Quantitative Investment Analysis can help. Factor modeling techniques such

as those described in Chapter 11 can be used to supplement the intuition of the human mind

to produce a quantitative framework that digests the large number of plausible variables that

can impact the price of a security. Further, given the many variables that can affect a security’s

value, it is not possible to consider each variable in isolation. The economic factors that cause

a security’s price to go up or down are interwoven in a complex web such that the variables

must be considered together to determine their collective impact on the price of a security.

This is where the value of Chapters 8 and 9 are most useful. These two chapters provide

the basic knowledge for building regression equations to study the impact of economic factors

on security prices. The regression techniques provided in Chapters 8 and 9 can be used to filter

out which variables have a significant impact on the price of a security, and which variables

just provide ‘‘noise.’’

In addition, Chapter 9 introduces the reader to ‘‘dummy variables.’’ Despite their name,

you don’t have to be a dummy like me to use them. Dummy variables are a neat way to study

different states of the world and their impact on security prices. They are often referred to as

‘‘binary’’ variables because they divide the world into two states for observation, for example,

financial markets up versus financial markets down; Republicans in control of the White

House versus Democrats in control of the White House; Chicago Cubs win (almost never)

versus Chicago Cubs lose; and so on. This last variable—the record of the Chicago Cubs—I

can attest has no impact on security valuations, although, as a long-standing and suffering

Cub fan, it does have an impact on my morale.

As another example, consider a recent research paper where I studied the behavior of

private equity managers in the way they price their private equity portfolios depending on

whether the public stock markets were doing well versus when the public stock markets were

doing poorly. To conduct this analysis, I ran a regression equation using dummy variables to

divide the world into two states: up public stock markets versus down public stock markets.

By using dummy variables in this manner, I was able to observe different behavioral patterns

of private equity managers in how they marked up or down their private equity portfolios

depending on the performance of the public stock markets.

The second reason Quantitative Investment Analysis will add value to the reader is that it

provides the basic tools to consider a breadth of economic factors and securities. It is not only

the fact that there are many interwoven economic variables that impact the value of a security,

the sheer number of securities in the market place can be daunting. Therefore, most investors

only look at a subset of the investable securities in the market.

Consider the U.S. stock market. Generally, this market is divided into three categories

based on company size: large-cap, mid-cap, and small-cap stocks. This division is less so because

there might be ‘‘size’’ effects in valuation, but rather, because of the pragmatic limitation

that asset managers simply cannot analyze stocks beyond a certain number. So traditional

fundamental investors select different parts of the U.S. stock market in which to conduct their

security analysis. However, the division of the stock market into size categories effectively

establishes barriers for investment managers. There is no reason, for example, why a portfolio

Foreword

xv

manager with insight into how earnings surprises affect stock prices cannot invest across the

whole range of stock market capitalization.

This is where Chapters 6 and 7 can be useful. The quantitative skills of sampling,

estimation, and hypothesis testing can be used to analyze large baskets of data. This allows

portfolio managers to invest across a broad universe of stocks, breaking down traditional

barriers such as cap-size restrictions. When viewed in this light, quantitative analysis does not

displace the fundamental stock picking skills of traditional asset managers. Rather, quantitative

analysis extends the portfolio manager’s insight with respect to company, macro, and market

variables to a broader array of investment opportunities.

This also has implications for the statistical tools and probability concepts provided in

Chapters 3 and 4. The larger the data set to be analyzed the greater the reliability of the

parameter estimation derived from that data set. Breadth of economic analysis will improve not

only the statistical reliability of the quantitative analysis, but will also increase the predictability

of the relationships between economic factors and stock price movement. The statistical tools

provided in this book allow the portfolio manager to realize the full potential of his or her skill

across a larger universe of securities than may have previously been achieved.

Another example might help. Every year the California Public Employees’ Retirement

System (CalPERS), my former employer, publishes a list of the most poorly governed

companies in the United States. This list has now been published for 16 years and has been

very successful. Early on in the process, the selection was conducted on a subset of the U.S.

stock market. However, this process has evolved to consider every U.S. stock held in CalPERS’s

portfolio regardless of stock market capitalization range. This requires the analysis of up to

1,800 stocks every year based on both economic factors and governance variables. The sheer

number of securities in this data sample could not be analyzed without the application of

quantitative screening tools to expand the governance universe for CalPERS.

Last, Quantitative Investment Analysis can provide a certain amount of discipline to the

investment process. We are all human, and as humans, we are subject to making mistakes. If I

were to recount all of the investment mistakes that I have made over my career, this Foreword

would exceed the length of the chapters in this book. Just as a brief example, one of my ‘‘better

calls’’ was Starbucks Coffee. Early on when Starbucks was just getting started, I visited one

of their shops to see what the buzz was all about. At that time a Latte Grande was selling for

about $1.50. I recall that I thought this was an outrageous price and I can remember distinctly

saying: ‘‘Oh, this is a dumb idea, this will never catch on!’’ Ah yes . . .

So back to quantitative techniques—how can they help? In this instance, they could have

helped me remove my human biases and to think more analytically about Starbucks’ prospects.

If I had taken the time to conduct an empirical review using the quantitative tools provided

in this text, I would have seen the fundamental value underlying that buck-fifty Latte.

The fact is that we are all subject to behavioral biases such as overconfidence, momentum,

and overreaction. Not only can these be analyzed as discussed above, they can be revealed

and discounted when we make our investment decisions. Perhaps the single biggest behavioral

hurdle to overcome for investors is the inability to sell a security when its value declines. All

too often we become almost emotionally attached to the securities in our portfolio such that

we find it hard to sell a security that begins to decline in price.

Yet, this is precisely, where Quantitative Investment Analysis can help because it is

dispassionate. Quantitative tools and modeling techniques can take the emotion and cognitive

biases out of the portfolio decision-making process. As portfolio managers, our goal is to

be objective, critical, and demanding. Unfortunately, sometimes our embedded habits and

opinions can get in the way. However, quantitative models are unemotional and they can root

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Foreword

out our cognitive biases in a way that we simply cannot do ourselves by looking in the mirror

(in fact, when I look in the mirror I see someone who is six feet and four inches tall and

incredibly good looking but then my wife Mary reminds me that I am only six feet and one

inch tall and she had better offers).

All in all, the investor will appreciate the methods, models, and techniques provided in

this text. This book serves as an excellent introduction to those investors who are just beginning

to use quantitative tools in their portfolio management process as well as an excellent reference

guide for those already converted. Quantitative investing is not difficult to grasp—even a

lawyer can do it.

Mark J. P. Anson

CEO, Hermes Pensions Management

CEO, British Telecomm Pension Scheme

mark@hermes.co.uk

ACKNOWLEDGMENTS

W

e would like to thank the many individuals who played important roles in producing

this book.

Robert R. Johnson, CFA, Managing Director of the CFA and CIPM Programs Division,

saw the need for specialized curriculum materials and initiated this project. We appreciate his

support for the timely revision of this textbook. Senior executives in the CFA Program Division

have generously given their advice and time in the writing of both editions of this book.

Philip J. Young, CFA, provided continuous assistance in writing the book’s learning outcome

statements and participated in final manuscript reviews. Jan R. Squires, CFA, contributed an

orientation stressing motivation and testability. Mary K. Erickson, CFA, made contributions

to the accuracy of the text. John D. Stowe, CFA, supplied suggestions for revising several

chapters.

The Executive Advisory Board of the Candidate Curriculum Committee provided

invaluable input: James Bronson, CFA, Chair; Peter Mackey, CFA, Immediate Past Chair;

and members, Alan Meder, CFA, Victoria Rati, CFA, and Matt Scanlan, CFA, as well as the

Candidate Curriculum Committee Working Body.

The manuscript reviewers for this edition were Philip Fanara, Jr., CFA; Jane Farris,

CFA; David M. Jessop; Lisa M. Joublanc, CFA; Asjeet S. Lamba, CFA; Mario Lavallee, CFA;

William L. Randolph, CFA; Eric N. Remole; Vijay Singal, CFA; Zoe L. Van Schyndel, CFA;

Charlotte Weems, CFA; and Lavone F. Whitmer, CFA. We thank them for their excellent

work.

We also appreciate the many comments received from those who used the first edition.

Jacques R. Gagne, CFA, Gregory M. Noronha, CFA, and Sanjiv Sabherwal provided

highly detailed proofreading of the individual chapters. We thank each for their dedicated

and painstaking work. We are also indebted to Dr. Sabherwal for his expert assistance in

running regressions, revising in-chapter examples, and creating some of the end-of-chapter

problems/solutions.

Fiona D. Russell provided incisive copyediting that substantially contributed to the book’s

accuracy and readability.

Wanda A. Lauziere of the CFA Program Division, the project manager for the revision,

expertly guided the manuscript from planning through production and made many other

contributions to all aspects of the revision.

Finally, we thank Ibbotson Associates of Chicago for generously providing us with EnCorr

AnalyzerTM .

xvii

INTRODUCTION

CFA Institute is pleased to provide you with this Investment Series covering major areas in

the field of investments. These texts are thoroughly grounded in the highly regarded CFA

Program Candidate Body of Knowledge (CBOK) that draws upon hundreds of practicing

investment professionals and serves as the anchor for the three levels of the CFA Examinations.

In the year this series is being launched, more than 120,000 aspiring investment professionals

will each devote over 250 hours of study to master this material as well as other elements of

the Candidate Body of Knowledge in order to obtain the coveted CFA charter. We provide

these materials for the same reason we have been chartering investment professionals for over

40 years: to improve the competency and ethical character of those serving the capital markets.

PARENTAGE

One of the valuable attributes of this series derives from its parentage. In the 1940s, a handful

of societies had risen to form communities that revolved around common interests and work

in what we now think of as the investment industry.

Understand that the idea of purchasing common stock as an investment—as opposed to

casino speculation—was only a couple of decades old at most. We were only 10 years past the

creation of the U.S. Securities and Exchange Commission and laws that attempted to level the

playing field after robber baron and stock market panic episodes.

In January 1945, in what is today CFA Institute Financial Analysts Journal, a fundamentally driven professor and practitioner from Columbia University and Graham-Newman

Corporation wrote an article making the case that people who research and manage portfolios

should have some sort of credential to demonstrate competence and ethical behavior. This

person was none other than Benjamin Graham, the father of security analysis and future

mentor to a well-known modern investor, Warren Buffett.

The idea of creating a credential took a mere 16 years to drive to execution but by 1963,

284 brave souls, all over the age of 45, took an exam and launched the CFA credential. What

many do not fully understand was that this effort had at its root a desire to create a profession

where its practitioners were professionals who provided investing services to individuals in

need. In so doing, a fairer and more productive capital market would result.

A profession—whether it be medicine, law, or other—has certain hallmark characteristics.

These characteristics are part of what attracts serious individuals to devote the energy of their

life’s work to the investment endeavor. First, and tightly connected to this Series, there must

be a body of knowledge. Second, there needs to be some entry requirements such as those

required to achieve the CFA credential. Third, there must be a commitment to continuing

education. Fourth, a profession must serve a purpose beyond one’s direct selfish interest. In

this case, by properly conducting one’s affairs and putting client interests first, the investment

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Introduction

professional can work as a fair-minded cog in the wheel of the incredibly productive global

capital markets. This encourages the citizenry to part with their hard-earned savings to be

redeployed in fair and productive pursuit.

As C. Stewart Sheppard, founding executive director of the Institute of Chartered Financial

Analysts said, ‘‘Society demands more from a profession and its members than it does from a

professional craftsman in trade, arts, or business. In return for status, prestige, and autonomy, a

profession extends a public warranty that it has established and maintains conditions of entry,

standards of fair practice, disciplinary procedures, and continuing education for its particular

constituency. Much is expected from members of a profession, but over time, more is given.’’

‘‘The Standards for Educational and Psychological Testing,’’ put forth by the American

Psychological Association, the American Educational Research Association, and the National

Council on Measurement in Education, state that the validity of professional credentialing

examinations should be demonstrated primarily by verifying that the content of the examination accurately represents professional practice. In addition, a practice analysis study, which

confirms the knowledge and skills required for the competent professional, should be the basis

for establishing content validity.

For more than 40 years, hundreds upon hundreds of practitioners and academics have

served on CFA Institute curriculum committees sifting through and winnowing all the many

investment concepts and ideas to create a body of knowledge and the CFA curriculum. One of

the hallmarks of curriculum development at CFA Institute is its extensive use of practitioners

in all phases of the process.

CFA Institute has followed a formal practice analysis process since 1995. The effort

involves special practice analysis forums held, most recently, at 20 locations around the world.

Results of the forums were put forth to 70,000 CFA charterholders for verification and

confirmation of the body of knowledge so derived.

What this means for the reader is that the concepts contained in these texts were driven

by practicing professionals in the field who understand the responsibilities and knowledge that

practitioners in the industry need to be successful. We are pleased to put this extensive effort

to work for the benefit of the readers of the Investment Series.

BENEFITS

This series will prove useful both to the new student of capital markets, who is seriously

contemplating entry into the extremely competitive field of investment management, and to

the more seasoned professional who is looking for a user-friendly way to keep one’s knowledge

current. All chapters include extensive references for those who would like to dig deeper into

a given concept. The workbooks provide a summary of each chapter’s key points to help

organize your thoughts, as well as sample questions and answers to test yourself on your

progress.

For the new student, the essential concepts that any investment professional needs to

master are presented in a time-tested fashion. This material, in addition to university study

and reading the financial press, will help you better understand the investment field. I believe

that the general public seriously underestimates the disciplined processes needed for the best

investment firms and individuals to prosper. These texts lay the basic groundwork for many

of the processes that successful firms use. Without this base level of understanding and an

appreciation for how the capital markets work to properly price securities, you may not find

Introduction

xxi

competitive success. Furthermore, the concepts herein give a genuine sense of the kind of work

that is to be found day to day managing portfolios, doing research, or related endeavors.

The investment profession, despite its relatively lucrative compensation, is not for

everyone. It takes a special kind of individual to fundamentally understand and absorb the

teachings from this body of work and then convert that into application in the practitioner

world. In fact, most individuals who enter the field do not survive in the longer run. The

aspiring professional should think long and hard about whether this is the field for him or

herself. There is no better way to make such a critical decision than to be prepared by reading

and evaluating the gospel of the profession.

The more experienced professional understands that the nature of the capital markets

requires a commitment to continuous learning. Markets evolve as quickly as smart minds can

find new ways to create an exposure, to attract capital, or to manage risk. A number of the

concepts in these pages were not present a decade or two ago when many of us were starting

out in the business. Hedge funds, derivatives, alternative investment concepts, and behavioral

finance are examples of new applications and concepts that have altered the capital markets in

recent years. As markets invent and reinvent themselves, a best-in-class foundation investment

series is of great value.

Those of us who have been at this business for a while know that we must continuously

hone our skills and knowledge if we are to compete with the young talent that constantly

emerges. In fact, as we talk to major employers about their training needs, we are often

told that one of the biggest challenges they face is how to help the experienced professional,

laboring under heavy time pressure, keep up with the state of the art and the more recently

educated associates. This series can be part of that answer.

CONVENTIONAL WISDOM

It doesn’t take long for the astute investment professional to realize two common characteristics

of markets. First, prices are set by conventional wisdom, or a function of the many variables

in the market. Truth in markets is, at its essence, what the market believes it is and how it

assesses pricing credits or debits on those beliefs. Second, as conventional wisdom is a product

of the evolution of general theory and learning, by definition conventional wisdom is often

wrong or at the least subject to material change.

When I first entered this industry in the mid-1970s, conventional wisdom held that

the concepts examined in these texts were a bit too academic to be heavily employed in the

competitive marketplace. Many of those considered to be the best investment firms at the

time were led by men who had an eclectic style, an intuitive sense of markets, and a great

track record. In the rough-and-tumble world of the practitioner, some of these concepts were

considered to be of no use. Could conventional wisdom have been more wrong? If so, I’m not

sure when.

During the years of my tenure in the profession, the practitioner investment management

firms that evolved successfully were full of determined, intelligent, intellectually curious

investment professionals who endeavored to apply these concepts in a serious and disciplined

manner. Today, the best firms are run by those who carefully form investment hypotheses

and test them rigorously in the marketplace, whether it be in a quant strategy, in comparative

shopping for stocks within an industry, or in many hedge fund strategies. Their goal is to

create investment processes that can be replicated with some statistical reliability. I believe

xxii

Introduction

those who embraced the so-called academic side of the learning equation have been much

more successful as real-world investment managers.

THE TEXTS

Approximately 35 percent of the Candidate Body of Knowledge is represented in the initial

four texts of the series. Additional texts on corporate finance and international financial

statement analysis are in development, and more topics may be forthcoming.

One of the most prominent texts over the years in the investment management industry

has been Maginn and Tuttle’s Managing Investment Portfolios: A Dynamic Process. The third

edition updates key concepts from the 1990 second edition. Some of the more experienced

members of our community, like myself, own the prior two editions and will add this

to our library. Not only does this tome take the concepts from the other readings and

put them in a portfolio context, it also updates the concepts of alternative investments,

performance presentation standards, portfolio execution and, very importantly, managing

individual investor portfolios. To direct attention, long focused on institutional portfolios,

toward the individual will make this edition an important improvement over the past.

Quantitative Investment Analysis focuses on some key tools that are needed for today’s

professional investor. In addition to classic time value of money, discounted cash flow

applications, and probability material, there are two aspects that can be of value over

traditional thinking.

First are the chapters dealing with correlation and regression that ultimately figure into

the formation of hypotheses for purposes of testing. This gets to a critical skill that many

professionals are challenged by: the ability to sift out the wheat from the chaff. For most

investment researchers and managers, their analysis is not solely the result of newly created

data and tests that they perform. Rather, they synthesize and analyze primary research done

by others. Without a rigorous manner by which to understand quality research, not only can

you not understand good research, you really have no basis by which to evaluate less rigorous

research. What is often put forth in the applied world as good quantitative research lacks rigor

and validity.

Second, the last chapter on portfolio concepts moves the reader beyond the traditional

capital asset pricing model (CAPM) type of tools and into the more practical world of

multifactor models and to arbitrage pricing theory. Many have felt that there has been a

CAPM bias to the work put forth in the past, and this chapter helps move beyond that point.

Equity Asset Valuation is a particularly cogent and important read for anyone involved

in estimating the value of securities and understanding security pricing. A well-informed

professional would know that the common forms of equity valuation—dividend discount

modeling, free cash flow modeling, price/earnings models, and residual income models (often

known by trade names)—can all be reconciled to one another under certain assumptions.

With a deep understanding of the underlying assumptions, the professional investor can better

understand what other investors assume when calculating their valuation estimates. In my

prior life as the head of an equity investment team, this knowledge would give us an edge over

other investors.

Fixed Income Analysis has been at the frontier of new concepts in recent years, greatly

expanding horizons over the past. This text is probably the one with the most new material for

the seasoned professional who is not a fixed-income specialist. The application of option and

derivative technology to the once staid province of fixed income has helped contribute to an

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