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CFA® EXAM REVIEW
1
W IL E Y
Wiley Study Guide for 2018 
Level III CFA Exam Review
Complete Set
Thousands of candidates from more than 100 countries have relied on these Study Guides 
to pass the CFA® Exam. Covering every Learning Outcome Statement (LOS) on the exam, 
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Wiley Study Guide for 2018 
Level III CFA Exam Review
Wi l e y
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ISBN 978-1-119-43611-9 (ePub) 
ISBN 978-1-119-43610-2 (ePDF)
Contents
About the Authors xi
Wiley Study Guide for 2018 Level III CFA Exam
Volume 1: Ethical and Professional Standards & Behavioral Finance
Study Session 1: Code of Ethics and Standards of Professional Conduct
Reading 1: Code of Ethics and Standards of Professional Conduct 3
Lesson 1: Code of Ethics and Standards of Professional Conduct 3
Reading 2: Guidance for Standards l-VII 9
Lesson 1: Standard I: Professionalism 9
Lesson 2: Standard II: Integrity of Capital Markets 36
Lesson 3: Standard III: Duties to Clients 46
Lesson 4: Standard IV: Duties to Employers 70
Lesson 5: Standard V: Investment Analysis, Recommendations, and Actions 84
Lesson 6: Standard VI: Conflicts of Interest 97
Lesson 7: Standard VII: Responsibilities as a CFA Institute Member or CFA Candidate 107
Study Session 2: Ethical and Professional Standards in Practice
Reading 3: Application of the Code and Standards 119
Lesson 1: Ethical and Professional Standards in Practice, Part 1: The Consultant 119
Lesson 2: Ethical and Professional Standards in Practice, Part 2: Pearl Investment 
Management 120
Reading 4: Asset Manager Code of Professional Conduct 121
Lesson 1: Asset Manager Code of Professional Conduct 121
Study Session 3: Behavioral Finance
Reading 5: The Behavioral Finance Perspective 131
Lesson 1: Behavioral versus Traditional Perspectives 131
Lesson 2: Decision Making 136
Lesson 3: Perspectives on Market Behavior and Portfolio Construction 140
Reading 6: The Behavioral Biases of Individuals 147
Lesson 1: Cognitive Biases 148
Lesson 2: Emotional Biases 154
Lesson 3: Investment Policy and Asset Allocation 159
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Reading 7: Behavioral Finance and Investment Processes 165
Lesson 1:The Uses and Limitations of Classifying Investors into Types 165
Lesson 2: How Behavioral Factors Affect Advisor-Client Relations 168
Lesson 3: How Behavioral Factors Affect Portfolio Construction 169
Lesson 4: Behavioral Finance and Analyst Forecasts 172
Lesson 5: How Behavioral Factors Affect Committee Decision Making 178
Lesson 6: How Behavioral Finance Influences Market Behavior 179
Wiley Study Guide for 2018 Level III CFA Exam
Volume 2: Private Wealth Management & Institutional Investors
Study Session 4: Private Wealth Management (1)
Reading 8: Managing Individual Investor Portfolios 3
Lesson 1: Investor Characteristics: Situational and Psychological Profiling 3
Lesson 2: Individual IPS: Return Objective Calculation 6
Lesson 3: Individual IPS: Risk Objective 7
Lesson 4: Individual IPS: The Five Constraints 8
Lesson 5: A Complete Individual IPS 10
Lesson 6: Asset Allocation Concepts: The Process of Elimination 18
Lesson 7: Monte Carlo Simulation and Personal Retirement Planning 20
Reading 9: Taxes and Private Wealth Management in a Global Context 21
Lesson 1: Overview of Global Income Tax Structures 21
Lesson 2: After-Tax Accumulations and Returns forTaxable Accounts 23
Lesson 3: Types of Investment Accounts and Taxes and Investment Risk 31
Lesson 4: Implications for Wealth Management 34
Reading 10: Domestic Estate Planning: Some Basic Concepts 39
Lesson 1: Basic Estate Planning Concepts 39
Lesson 2: Core Capital and Excess Capital 42
Lesson 3: Transferring Excess Capital 46
Lesson 4: Estate Planning Tools 51
Lesson 5: Cross-Border Estate Planning 53
Study Session 5: Private Wealth Management (2)
Reading 11: Concentrated Single-Asset Positions 59
Lesson 1: Concentrated Single-Asset Positions: Overview and Investment Risks 59
Lesson 2: General Principles of Managing Concentrated Single-Asset Positions 60
Lesson 3: Managingthe Risk of Concentrated Single-Stock Positions 66
Lesson 4: Managing the Risk of Private Business Equity 71
Lesson 5: Managing the Risk of Investment in Real Estate 74
Reading 12: Risk Management for Individuals 77
Lesson 1: Human Capital and Financial Capital 77
Lesson 2: Seven Financial Stages of Life 78
Lesson 3: A Framework for Individual Risk Management 80
Lesson 4: Life Insurance 83
Lesson 5: Other Types of Insurance 88
Lesson 6: Annuities 91
Lesson 7: Implementation of Risk Management for Individuals 95
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CONTENTS
Study Session 6: Portfilio Management for Institutional Investors
Reading 13: Managing Institutional Investor Portfolios 103
Lesson 1: Institutional IPS: Defined Benefit (DB) Pension Plans 103
Lesson 2: Institutional IPS: Foundations 111
Lesson 3: Institutional IPS: Endowments 115
Lesson 4: Institutional IPS: Life Insurance and 117
Non-Life Insurance Companies (Property and Casualty)
Lesson 5: Institutional IPS: Banks 120
Wiley Study Guide for 2018 Level III CFA Exam
Volume 3: Economic Analysis, Asset Allocation, Equity & Fixed Income Portfolio Management
Study Session 7: Applications of Economic Analysis to Portfolio Management
Reading 14: Capital Market Expectations 3
Lesson 1: Organizing the Task: Framework and Challenges 3
Lesson 2: Tools for Formulating Capital Market Expectations, Part 1: Formal Tools 8
Lesson 3: Tools for Formulating Capital Market Expectations, Part 2: Survey and
Panel Methods and Judgment 13
Lesson 4: Economic Analysis, Part 1: Introduction and Business Cycle Analysis 19
Lesson 5: Economic Analysis, Part 2: Economic Growth Trends, Exogenous Shocks, and 
International Interactions 27
Lesson 6: Economic Analysis, Part 3: Economic Forecasting 30
Lesson 7: Economic Analysis, Part 4: Asset Class Returns and Foreign Exchange Forecasting 33
Reading 15: Equity Market Valuation 39
Lesson 1: Estimating a Justified P/E Ratio and Top-Down and Bottom-Up Forecasting 39 
Lesson 2: Relative Value Models 46
Study Session 8: Asset Allocation and Related Decisions in Portfolio Management (1)
Reading 16: Introduction to Asset Allocation 53
Lesson 1: Asset Allocation in the Portfolio Construction Process 53
Lesson 2: The Economic Balance Sheet and Asset Allocation 54
Lesson 3: Approaches to Asset Allocation 55
Lesson 4: Strategic Asset Allocation 57
Lesson 5: Implementation Choices 64
Lesson 6: Strategic Considerations for Rebalancing 65
Reading 17: Principles of Asset Allocation 67
Lesson 1: The Traditional Mean-Variance Optimization (MVO) Approach 67
Lesson 2: Monte Carlo Simulation and Risk Budgeting 70
Lesson 3: Factor-Based Asset Allocation 71
Lesson 4: Liability-Relative Asset Allocation 72
Lesson 5: Goal-Based Asset Allocation, Heuristics, Other Approaches to Asset Allocation, 
and Portfolio Rebalancing 75
Study Session 9: Asset Allocation and Related Decisions in Portfolio Management (2)
Reading 18: Asset Allocation with Real-World Constraints 81
Lesson 1: Constraints in Asset Allocation 81
Lesson 2: Asset Allocation for the Taxable Investor 84
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Lesson 3: Altering or Deviating from the Policy Portfolio 85
Lesson 4: Behavioral Biases in Asset Allocation 87
Reading 19: Currency Management: An Introduction 89
Lesson 1: Review of Foreign Exchange Concepts 89
Lesson 2: Currency Risk and Portfolio Return and Risk 95
Lesson 3: Currency Management: Strategic Decisions 98
Lesson 4: Currency Management: Tactical Decisions 101
Lesson 5: Tools of Currency Management 104
Lesson 6: Currency Management for Emerging Market Currencies 112
Reading 20: Market Indexes and Benchmarks 113
Lesson 1: Distinguishing between a Benchmark and a Market Index and
Benchmark Uses and Types 113
Lesson 2: Market Index Uses and Types 117
Lesson 3: Index Weighting Schemes: Advantages and Disadvantages 119
Study Session 10: Fixed-Income Portfolio Management (1)
Reading 21: Introduction to Fixed-Income Portfolio Management 127
Lesson 1: Roles of Fixed Income Securities in Portfolios 127
Lesson 2: Fixed Income Mandates 129
Lesson 3: Bond Market Liquidity 133
Lesson 4: Components of Fixed Income Return 135
Lesson 5: Leverage 137
Lesson 6: Fixed Income Portfolio Taxation 140
Reading 22: Liability-Driven and Index-Based Strategies 143
Lesson 1: Liability-driven Investing 143
Lesson 2: Managing Single and Multiple Liabilities 144
Lesson 3: Risks in Managing a Liability Structure 147
Lesson 4: Liability Bond Indexes 148
Lesson 5: Alternative Passive Bond Investing 148
Lesson 6: Liability Benchmarks 149
Lesson 7: Laddered Bond Portfolios 149
Study Session 11: Fixed-Income Portfolio Management (2)
Reading 23: Yield Curve Strategies 153
Lesson 1: Foundational Concepts for Yield Curve Management 153
Lesson 2: Yield Curve Strategies 155
Lesson 3: Formulating a Portfolio Postioning Strategy for a Given Market View 161
Lesson 4: A Framework for Evaluating Yield Curve Trades 167
Reading 24: Fixed-Income Active Management: Credit Strategies 169
Lesson 1: Investment-Grade and High-Yield Corporate Bond Portfolios 169
Lesson 2: Credit Spreads 172
Lesson 3: Credit Strategy Approaches 175
Lesson 4: Liquidity Risk and Tail Risk in Credit Portfolios 185
Lesson 5: International Credit Portfolios 189
Lesson 6: Structured Financial Instruments 191
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Study Session 12: Equity Portfolio Management
Reading 25: Equity Portfolio Management 197
Lesson 1:The Role of the Equity Portfoli and Approaches to Equity Investing 197
Lesson 2: Passive Equity Investing 198
Lesson 3: Active Equity Investing 204
Lesson 4: Semiactive Equity Investing 215
Lesson 5: Managing a Portfolio of Managers 218
Lesson 6: Identifying, Selecting, and Contracting with Equity Portfolio Managers 222
Lesson 7: Distressed Securities 223
Wiley Study Guide for 2018 Level III CFA Exam
Volume 4: Alternative Investments, Risk Management, & Derivatives
Study Session 13: Alternative Investments for Portfolio Management
Reading 26: Alternative Investments for Portfolio Management 3
Lesson 1: Alternative Investments: Definitions, Similarities, and Contrasts 3
Lesson 2: Real Estate 5
Lesson 3: Private Equity/Venture Capital 9
Lesson 4: Commodity Investments 16
Lesson 5: Hedge Funds 21
Lesson 6: Managed Futures 30
Lesson 7: Distressed Securities 32
Study Session 14: Risk Management
Reading 27: Risk Management 39
Lesson 1: Risk Management as a Process and Risk Governance 39
Lesson 2: Identifying Risk 40
Lesson 3: Measuring Risk: Value at Risk (VaR) 44
Lesson 4: Measuring Risk: VaR Extensions and Stress Testing 52
Lesson 5: Measuring Risk: Credit Risk 53
Lesson 6: Managing Risk 60
Study Session 15: Risk Management Applications of Derivatives
Reading 28: Risk Management Applications of Forward and Futures Strategies 65
Lesson 1: Strategies and Applications for Managing Equity Market Risk 65
Lesson 2: Asset Allocation with Futures 74
Lesson 3: Strategies and Applications for Managing Foreign Currency Risk 83
Reading 29: Risk Management Applications of Option Strategies 89
Lesson 1: Options Strategies for Equity Portfolios 89
Lesson 2: Interest Rate Option Strategies 103
Lesson 3: Option Portfolio Risk Management Strategies 116
Reading 30: Risk Management Applications of Swap Strategies 121
Lesson 1: Strategies and Applications for Managing Interest Rate Risk 121
Lesson 2: Strategies and Applications for Managing Exchange Rate Risk 137
Lesson 3: Strategies and Applications for Managing Equity Market Risk 148
Lesson 4: Strategies and Applications Using Swaptions 153
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Wiley Study Guide for 2018 Level III CFA Exam
Volume 5: Trading, Monitoring and Rebalancing, Performance Evaluation,
& Global Investment Performance Standards
Study Session 16: Trading, Monitoring, and Rebalancing
Reading 31: Execution of Portfolio Decisions 3
Lesson 1: The Context of Trading: Market Microstructure 3
Lesson 2: The Costs of Trading 10
Lesson 3:Types ofTraders and Their Preferred OrderTypes 15
Lesson 4: Trade Execution Decisions and Tactics and Serving theClient's Interests 17
Reading 32: Monitoring and Rebalancing 25
Lesson 1: Monitoring for IPS Changes (Individual and Institutional) 25
Lesson 2: Rebalancing the Portfolio 32
Lesson 3: The Perold-Sharpe Analysis of Rebalancing Strategies 35
Study Session 17: Performance Evaluation
Reading 33: Evaluating Portfolio Performance 41
Lesson 1: Performance Measurement 41
Lesson 2: Benchmarks 49
Lesson 3: Performance Attribution (4 Models) 55
Lesson 4: Performance Appraisal 66
Lesson 5: The Practice of Performance Evaluation 71
Study Session 18: Global Investment Performance Standards
Reading 34: Overview of the Global Investment Performance Standards 75
Lesson 1: Background of the GIPS Standards 75
Lesson 2: Fundamentals of Compliance 76
Lesson 3: Input Data 78
Lesson 4: Return Calculation Methodologies 79
Lesson 5: Composite Construction Lesson 85
Lesson 6: Disclosure, Presentation, and Reporting 89
Lesson 7: Real Estate, Private Equity, and Wrap Fee/Separately Managed Accounts 96
Lesson 8: Valuation Principles and Advertising Guidelines 102
Lesson 9: Verification and Other Issues 104
© ©2018 Wiley
ABOUTTHE AUTHORS
Wiley’s Study Guides are written by a team of highly qualified CFA charterholders 
and leading CFA instructors from around the globe. Our team of CFA experts work 
collaboratively to produce the best study materials for CFA candidates available today.
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Elan Guides was acquired by John Wiley & Sons, Inc., where Basit continues his work 
as Director of CFA Content. Basit graduated magna cum laude from the Wharton School 
of Business at the University of Pennsylvania with majors in finance and legal studies.
He went on to obtain his CFA charter in 2006, passing all three levels on the first attempt. 
Prior to Elan Guides, Basit ran his own private wealth management business. He is a past 
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There are many more expert CFA charterholders who contribute to the creation of 
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St u d y Sessio n 7: Appl ic a t io n s o f Ec o no mic 
An a l y sis t o Po r t f o l io Ma n a g emen t
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CAPITAL MARKET EXPECTATIONS
R e a d i n g 1 4 : Ca p i t a l M a r k e t E x p e c t a t i o n s
Time to complete: 2 to 3 hours
Reading summary: This is one of the longest readings in the entire CFA Curriculum at 
Level III, so plan your time accordingly. The focus of the first portion of the reading is 
on formal tools to determine long-term expectations, mainly for returns. Then, short- and 
long-term economic growth forecasting and analysis are explored. The reading then 
examines how various asset classes perform at different stages of the business and inflation 
cycles.
LESSON 1: ORGANIZING THE TASK: FRAMEWORK AND CHALLENGES
LOS I4a: Discuss the role of, and a framework for, capital market 
expectations in the portfolio management process. Vol 3, pp 6-13
LEARNING OBJECTIVES
The portfolio management process begins with understanding the client’s objectives and 
constraints, which are documented in the investment policy statement (IPS). This client- 
specific information is then combined with the portfolio manager’s expectations about the 
long-term performance of asset classes to establish a unique strategic asset allocation. But 
how do managers form their capital market expectations? That is the question addressed by 
this reading.
After studying this material, the candidate should be able to:
1. Explain how capital market expectations fit within the portfolio management 
process;
2. Describe analytical tools and models used to develop capital market expectations;
3. Discuss the implications of the business cycle and economic policy for capital 
market expectations;
4. Explain how macroeconomic variables like inflation, interest rates, and exchange 
rates are forecast and how they influence capital market expectations.
A SYSTEMATIC APPROACH
The strategic asset allocation represents the base case, or normal state, partitioning of a 
portfolio among the various asset classes available in the investment universe. Each asset 
class (e.g., stocks, bonds, real estate, etc.) has unique risk and return characteristics that 
respond to changing economic conditions. So in order to ascertain which asset classes 
belong in a particular investor’s portfolio and in what proportion, the manager must have 
some idea of what the prevailing economic environment might look like and how asset 
classes might react under those conditions. These insights are collectively referred to as the 
manager’s capital market expectations.
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CAPITAL MARKET EXPECTATIONS
Given the vast amount of data available to asset managers, developing capital market 
expectations is best implemented using a systematic approach. In a general framework, the 
manager must address each of the following functions:
• Data collection, analysis, and interpretation of output.
• Deriving conclusions that lead to forecasts of expected returns, risk, and 
correlations.
• Monitoring, evaluating performance, and adjusting the process to improve future 
performance.
The process of setting capital market expectations is usually considered beta research, 
which emphasizes systematic risk of broad asset classes (e.g., equities, fixed income, and 
real estate). The research takes a macroeconomic perspective that uses the same inputs 
(e.g., interest rates, inflation, GDP growth, etc.) to develop expectations that are used to 
design a strategic asset allocation. Alpha research is an investment-specific approach 
that seeks to earn excess risk-adjusted returns, which is more closely related to security 
selection.
Steps in Formulating Capital Market Expectations
Step 1: Specify the final objectives o f the process and the relevant time period. The purpose 
of this step is to limit the scope of the research. With so much information available, the 
manager cannot consider everything under the sun. For example, if the client’s IPS limits 
acceptable asset classes to only domestic stocks and bonds, there is no need to research 
emerging-market real estate or commodity futures. Managers often complete this step by 
drafting a set of questions to be answered, which helps to focus their efforts.
Step 2: Review past performance and conditions. While the past is not necessarily 
indicative of the future, it is a logical place to start, with the understanding that relation 
that held in the past are susceptible to change.
Step 3: Define methods and models. Establishing the methodology early in the process will 
help determine what data is required.
Step 4: Collect data. When considering data sources, the manager must evaluate their 
timeliness, accuracy, and reliability.
Step 5: Apply analytical techniques, models, and judgment to interpret results. Particular 
attention should be paid to the assumptions underlying models, the consistency with which 
data is used, and any conflicts that might challenge the plausibility of output.
Step 6: Draw conclusions. Here the manager determines and documents his or her 
expectations that will be used in establishing the portfolio’s strategic asset allocation.
Step 7: Evaluate results and adjust. Actual results are compared to previous expectations 
to assess the level of accuracy that the expectations-setting process is delivering. Good 
forecasts are:
• Unbiased,objective, and well researched;
• Efficient, minimizing the magnitude of forecast errors; and
• Internally consistent.
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CAPITAL MARKET EXPECTATIONS
LOS 14b: Discuss challenges in developing capital market forecasts. 
Vol 3, pp 13-23
Forecasting Challenges
Faulty analysis may create a portfolio that is inappropriate for the client. The analysis 
might be compromised by unrealistic assumptions, unreliable data, or analyst biases.
Data Limitations
Economic data is notorious for problems related to timeliness (released with a lag), so 
you’re always looking backward to make forward-looking projections. It can also be error- 
prone, requiring revision of previously released data. Finally, changes in the way the data 
is collected or compiled can make past reporting incompatible with future releases.
Errors and Biases
Simple mistakes during the data gathering and compiling process can show up as 
transcription errors. Elements that drop out of data sets over time, as a result of mergers 
or bankruptcies for instance, make comparing indexes difficult across periods difficult 
and can give an overly optimistic impression. This phenomenon is called survivorship 
bias. Finally, valuation of illiquid assets is often done using appraisals. Because they are 
somewhat subjective and are usually performed at wider intervals, rather than continuous 
pricing found in high-volume trading, appraisals tend to underestimate volatility and 
distort correlations between asset returns.
Historical Data
While historical data might be a good starting point in developing a forecast, simply 
extrapolating the past into the future is a naive approach. The relation between variables 
that held in the past are often subject to the conditions that prevailed at the time, such 
as central bank policies and available technology. These underlying conditions are often 
referred to as regimes. Regime changes create inconsistencies in the way variables interact 
from one period to another. This creates a non-stationarity problem where the mean, 
variance, and correlations of variables are unstable over time.
Analysts can also run into the problem of not having a long enough history of data to 
work with. If this is the case, simply looking at time series drawn at greater frequency to 
increase the number of observations is not usually an effective solution.
Ex Post Risk versus Ex Ante Measures
Ex post risk is backward-looking and may underestimate the perceived ex ante risk. In 
other words, backward-looking risk metrics will not reflect possible events that were a 
concern at the time but did not ultimately come to pass. Those concerns would be reflected 
only in forward-looking (ex ante) risk metrics.
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CAPITAL MARKET EXPECTATIONS
Analysts’ Biases
Analytical biases lead to the finding of relations that don’t really exist. So-called spurious 
correlations are often the result of data-mining or time-period bias.
Data-mining bias occurs when a data set is analyzed over and over again until some 
statistically significant relation is found. Analysts should always have some underlying 
economic rationale for including a variable in a model so as to avoid including spurious 
correlations that lead to faulty forecasts.
Time-period bias occurs when the analyst shifts time horizons in order to find the best 
fit with the conclusions he or she is seeking. The relation between variables might be 
significant for a particular time interval, but they do not hold outside that period. Testing 
models with out-of-sample data is a good way to avoid this bias.
Ignoring Conditioning Information
Conditioning is adjusting our expectations when there are new facts that are relevant to 
forecasting the future. Measurements of risk and return are often based on historical data. 
However, an analyst should also consider the current and expected market environment. 
Forecasts should be based on conditional information and not simply unconditional 
averages of the past.
Correlation Is Not Causation
Just because two variables are correlated does not necessarily mean that one causes the 
other to occur. Correlation can indicate one of three possibilities: A predicts B, B predicts 
A, or C predicts A and B. Looking solely at the correlation between A and B could be very 
misleading. Therefore, it is important to have a theoretical justification for the expected 
relation between variables.
It is also possible to reach a false negative conclusion by blindly relying on correlations. 
Recall that correlation measures the linear relation between two variables. However, it is 
possible that they are related to one another in a nonlinear way. Their linear correlation 
may be insignificant, but the relation between the two variables might still have predictive 
power.
Psychological Biases
These six biases 
affect economists 
and professional 
forecasters, and 
some might be 
familiar from the 
lessons related to 
behavioral finance 
(BF). Keep in mind 
that this part of the 
CFA Curriculum 
was written many 
years before the 
BF readings. If 
you are tested on 
these specific six 
biases, the question 
will relate to an 
economist instead 
of an individual 
investor.
Psychological traps are common mental biases that jeopardize the accuracy of forecasts.
Anchoring occurs when initial findings dominate the rest of the analysis. Analysts must 
keep an open mind and allow the entirety of the research to lead to their conclusions.
Status quo bias projects the recent past into the future. Regret avoidance might drive this 
bias as analysts are loath to look foolish by predicting a major departure from what has 
prevailed in the past. Rigorous analysis can provide confidence to pursue an objective 
forecast even if it seems out of the ordinary.
The confirming evidence trap is the tendency to emphasize information that supports 
one’s initial hypothesis and discount evidence to the contrary. Maintaining self-awareness, 
investigating contradictory evidence, and designating a person to play the devil’s advocate 
are approaches to combating this form of bias.
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CAPITAL MARKET EXPECTATIONS
Overconfidence bias is common among highly trained and specially skilled individuals 
who tend to overestimate the precision of their forecasts. Analysts should consider a wider 
range of possible outcomes as a means to avoid this trap.
Prudence trap is the temptation to moderate conclusions so as to appear more 
conventional than the research itself indicates. Unorthodox forecasts that are far outside 
consensus opinion can prove brilliant if they turn out to be accurate but humiliating if 
they fail to materialize. Analysts can defend against this bias with rigorous research and 
allowing for a wider range of possible outcomes.
Recallability bias occurs when the research is heavily influenced by events that have left 
a lasting impression on the analyst, particularly catastrophic or dramatic events such as a 
market crash. Grounding conclusions on objective data rather than on personal emotion 
minimizes the distortion and addresses this trap.
Example 1-1
Amy Cobourg is the fund manager of a small emerging market fund that invests in both 
large-cap and mid-cap stocks. Cobourg has seen large gains in her personal portfolio 
from investments in Poland and is keen to take advantage of her knowledge of the region.
Currently, 25 percent of the portfolio is invested in the mining industry in Poland, which 
saw great returns the previous year due to a global boom in commodity prices. Cobourg 
is forecasting an average 12 percent (±10 bps) return on investment for commodity 
stocks for the coming year.
Cobourg recently read an article from a highly regarded mining industry analyst who 
specializes in Polish companies. The article suggests doubt and concern for the three 
largest businesses in the sector due to the author’s forecast of economic recession and 
lower prices. Cobourg’s supervisor, JohnCurran, disagrees with the report and says 
that the three companies are in a good position to handle a downturn, which he believes 
will profit by a reduction in the supply of gold from South Africa due to a miner’s strike 
there.
Cobourg revises her forecast to an expected return of 11 percent, only slightly lower 
than her existing 12 percent expectation, believing that Poland will gain extra market 
share at South Africa’s expense.
Identify three psychological traps in forecasting, justifying your answer with one reason 
for each trap identified.
Solution:
Overconfidence—Overestimating own knowledge of Polish stocks based on her own 
portfolio. Possibly used too narrow of range (±10 bps) for her forecast.
Anchoring trap—Cobourg only slightly revised expectations to 11% from 12% despite 
the negative outlook in the article.
Confirming evidence trap—Ignores the negative outlook in the article but agrees with 
the effect of potential profit from Poland’s gain in market share at South Africa’s 
expense.
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CAPITAL MARKET EXPECTATIONS
Model Risk
Financial and economic models are abstract representations of markets. They try to 
uncover the factors that influence the behavior of the variable being forecast. However, 
as abstract representations, they are incomplete, providing only estimates of dependent 
variables.
A model used to forecast economic variables or asset returns has two sources of error. First 
is the accuracy of the model inputs. Data is often imperfect, or model inputs themselves 
must be estimated from other models. The second source of error is the model itself. To the 
extent that the abstract representation departs from reality, the model’s forecast will also 
deviate from the dependent variables’ actual value.
LESSON 2: TOOLS FOR FORMULATING CAPITAL MARKET EXPECTATIONS, 
PART 1: FORMAL TOOLS
LOS 14c: Demonstrate the application of formal tools for setting capital 
market expectations, including statistical tools, discounted cash flow 
models, the risk premium approach, and financial equilibrium models. 
Vol 3, pp 23^40
ANALYTICAL METHODS AND TOOLS
Financial theory and practice provide a variety of tools and techniques for analysis and 
forecasting asset returns. Like more menial tasks, the analyst must select the right tool for 
the right job. In this section, we consider formal tools, which include statistical models, 
discounted cash flow models, and other quantitative techniques. We also consider survey 
and consensus approaches.
Quantitative Tools: Statistical Methods
Recall that there are two types of statistics: descriptive and inferential. Descriptive 
statistics seek to organize and present data in meaningful ways. Inferential statistics 
attempt to estimate or predict the characteristics of a population by looking at smaller 
samples.
Historical Averages and Estimators
The simplest forecast looks solely at past data. An analyst can compute the average return 
and variance of a sampled time series over a specific period. If the distribution of the data 
is stable, the sample statistics might be good estimates of their future values. There are, 
however, different methods of computing an average. In finance, the most commonly used 
methods are the arithmetic average, which is best for an estimate at a single point in time, 
and the geometric average, which is best for averaging compound returns over time.
Shrinkage Estimation
Shrinkage estimation is the weighted average of two estimates of a parameter based on the 
relative confidence the analyst has in using two methods. For example, an analyst might 
use sample historical data to estimate a covariance matrix and an alternative method such 
as a factor model to estimate a second covariance matrix, called a target covariance matrix.
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CAPITAL MARKET EXPECTATIONS
Let’s consider that the estimated covariance between stocks and bonds is 24 using a factor 
model and 40 using a historical estimate, and assume that the optimal weights on the 
model and historical estimates are 0.80 and 0.20, respectively. The shrinkage estimate of 
the covariance would be 0.80(24) + 0.20(40) = 27.2.
In all cases, a shrinkage estimate using any target covariance matrix is a more efficient (or 
at least not less efficient) estimate than the historical average.
Example 2-1
Richard Ayoade is using a shrinkage estimator approach to estimating covariances 
between Mexican and Colombian equities. He estimates that the covariance between 
Mexican and Colombian equities is 76 using historical data. He also estimates the 
covariance as 64 using a factor model approach.
A. Determine the shrinkage estimate of the covariance between Mexican and 
Colombian equities if the analyst has 80 percent confidence in the factor model 
approach.
B. Contrast the quality of the shrinkage estimate of covariance versus the 
historical average alone.
Solutions:
A. 0.20(76) + 0.80(64) = 66.4
B. The shrinkage estimator approach will lead to an increase in the efficiency of 
the covariance estimates versus the historical estimate.
We can also determine a shrinkage estimate for mean returns by taking a weighted average 
of historical mean return and some other target estimate, like the average mean of a group 
of assets. For example, given five assets with sample mean returns of 7 percent, 11 percent, 
13 percent, 15 percent, and 19 percent, respectively, and a weight of 70 percent on the 
sample mean, we would calculate the grand mean return as 13 percent and the shrinkage 
estimate of the first asset’s return as 0.3(9%) + 0.7(13%) = 11.2%.
Time-Series Analysis
Time-series estimators are based on regression using lagged variables, which are past 
values of the dependent variable. For example, a model used to determine the short-term 
volatility in a variety of asset markets was developed at JPMorgan. The model shows that 
the variance (a 2) in time t is dependent upon its value in the preceding period t — 1 and the 
square of a random error term ef.
a? = p a ^ + (1 - p)e2
• • • 2 The larger the coefficient term, P, the greater influence the past variance (ĉ r_1) has on the
forecasted variance (of ). This variance “memory” from one period to the next is called
volatility clustering.
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CAPITAL MARKET EXPECTATIONS
Multifactor Regression Models
Multifactor models provide asset return forecasts (R,) based on risk factors (Fk) that are 
thought to drive returns. The risk factors represent the required return for assuming that 
particular source of risk. The factor sensitivities (bik) are the regression coefficients that 
measure the degree to which the return is affected by a particular risk factor, or the asset’s 
exposure to that risk.
Multifactor models are also well suited for estimating covariances between asset class 
returns.
Quantitative Methods: Discounted Cash Flow Models
Discounted cash flow (DCF) models are based on the fundamental premise that the value 
of any asset is the present value of its future cash flows.
DCF models estimate the intrinsic value of an asset. The expected return on the asset is 
embedded in the relation between the asset’s intrinsic value and its current market price. 
However, an expected return based on this intrinsic valuation approach is realized only 
when the market price converges to the intrinsic value, which can take a long time to 
happen. For that reason, intrinsic value approaches are generally regarded as useful in 
setting long-term, strategic expectations as opposed to short-term, tactical expectations.
Dividend Discount Model (DDM)
The Gordon (constant) growth dividend discount model is a widely recognized DCF 
model for estimating a stock’s intrinsic value. The current price (Pq) is determined by the 
next dividend [D\ = Dq(1 + g)], discounted at the required return on common equity (re) 
adjusted for the estimated growth rate of dividends (g), which are assumed to grow at the 
same rate as earnings.
A ^ Do<X + g)
re ~ 8 re - g
The expected return of the stock, E(R), is the required return on equity. Rearranging the 
preceding equation to solve for the required return provides an estimate of the expected 
return on common stock.
E(R) = Dq<1 + 8) 
Po
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We can interpret this equation to read, “The expected long-term rate of return on common 
stock is the dividend yield (D {IPq) plus the long-term growth rate of earnings (g).” The 
dividend yield represents income, while the growth rate represents the capital gains yield, 
g, which might be assumed to be the growth rate in nominal gross domestic product (GDP) 
for a broadly defined equities asset class.
Grinold-Kroner Model
In many countries, particularly in the United States, firms have preferred to return excess 
cash to shareholders in the form of share repurchases rather than dividend distributions. 
Grinold and Kroner (2002) adapted the traditional DDM to account for this form of 
distribution as well as expected changes in relative value that investors attach to earnings 
via the price-earnings (P/E) multiple.
D
P
- % AS + INFL + gr + % APE
We can read the model as: “The expected return on equity, E(R), is approximately equal 
to the dividend yield (D/P) less the expected percent change in the number of shares 
outstanding (%AS) plus the rate of inflation (INFL) plus the real expected earnings growth 
rate (gr) plus the percent change in the price-earnings multiple (%APE).”
Notice that a share repurchase would result in a negative change in the shares outstanding. 
Subtracting a negative number results in a positive impact on the expected return.
The model can also be used to decompose historical returns. The three sources of the asset 
return are:
1. An expected income return: D/P - %AS
2. An expected nominal earnings growth return: INFL + gr
3. An expected repricing return: %APE
Example 2-2
Fred Schepisi holds a $200 million equity portfolio. He is considering adding to the 
portfolio based on an assessment of the risk and return prospects facing the economy 
in Thailand. Information pertaining to the Thai economy and capital markets has been 
collected as shown:
• Expected dividend yield of 1.75 percent on equities;
• Expected repurchase yield of 1.33 percent on equities;
• Expected long-term inflation rate of 6.66 percent per year;
• Expected long-term corporate real earnings growth rate of 2.33 percent 
per year; and
• Expected P/E multiple expansion of 0.50 percent per year.
Calculate the expected annual return for Thai equities using the Grinold-Kroner model.
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CAPITAL MARKET EXPECTATIONS
Solution:
The expected rate of return on Thai equities using the Grinold-Kroner model is:
E(R)
/
v
D \
-% A S + (INF + gr) + %APE
y
= Income yield + Nominal + Repricing yield
= (1.75% + 1.33%) + (6.66% + 2.33%) + 0.50% = 12.57%
The Fed Model
The Fed model is based on the discounted cash flow approach. It relates the earnings yield 
on stocks (E/P) to the 10-year U.S. Treasury bond yield. Since stocks are riskier than 
bonds, the equity earnings yield, which is the required return of a no-growth stock and 
the inverse of the P/E ratio, should be greater than the yield on the 10-year Treasury bond. 
If the stock market’s earnings yield (E/P) is lower than the 10-year Treasury bond yield, 
stocks are overvalued, and investors would shift their money into the less risky T-bonds.
Fixed Income
The Fed model 
is discussed in 
more detail in 
Reading 15.
The discounted cash flow model is also used to price fixed-income instruments as well. 
The yield to maturity (YTM) is the single discount rate that equates the present value of 
the bond’s cash flows to the bond’s market price. Therefore, the YTM can be used as an 
estimate of the expected return to a bond.
The Build-Up Approach
The risk premium approach (build-up approach) starts with the nominal risk-free rate 
and adds premiums for the various priced risk factors for which investors require to be 
compensated for assuming.
jE(R )̂ = Rj7 + RPy + RP2 + ••• + RR/c
where E(Rt) is the asset’s expected return, RF denotes the nominal risk-free rate 
of interest, and RP represents the risk premiums.
Fixed-Income Premiums
To determine the expected return for a bond, E(Rb), the analyst begins with the real risk-
free rate of interest and adds the relevant premiums for priced risk factors.
E{Rb) - rrF + RPlNFL + RP'Default + RRLiquidity + RRMaturity + R^Tax
The risk premiums compensate the bond investor for: deferring consumption (rrF), the 
loss of purchasing power (RPINFL), difficulty in exiting the investment (RPLiquidity)’ putting 
capital at risk for longer periods of time (RPMaturity)’ and the tax disadvantage of some 
types of bonds versus others (RPTax).
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Equity Risk Premium
Because equity investors have a lower priority claim on a firm’s cash flows than debt 
holders, the equity risk premium (ERP) compensates the equity investors for additional risk 
of loss to their investment. Therefore, the equity risk premium is the excess return over the 
nominal risk-free rate (RF), which is usually estimated by the 10-year U.S. Treasury yield.
E(RS) = Rf + ERP - YTM10_year Treasury + ERP
While the yield on the 10-year Treasury is readily available, the value of the equity risk 
premium is a hotly debated topic among academics and practitioners.
LESSON 3: TOOLS FOR FORMULATING CAPITAL MARKET EXPECTATIONS, 
PART 2: SURVEY AND PANEL METHODS AND JUDGMENT
LOS 14c: Demonstrate the application of formal tools for setting capital 
market expectations, including statistical tools, discounted cash flow 
models, the risk premium approach, and financial equilibrium models. 
Vol 3, pp 40-48
Equilibrium Models
Equilibrium models are based on the principles of modern portfolio theory and mean- 
variance optimization techniques. They define the risk-return relation when the supply and 
demand for assets are equal.
The International Capital Asset Pricing Model
The International CAPM (ICAPM) approach assumes the same form as the regular CAPM 
model you are likely very familiar with. The ICAPM relies on the return for the theoretical 
global market portfolio (RM).
E(Ri) = RF + M E (R M) - R F]
The equation implies that an asset class risk premium = E(Rt) - RF] is a function of 
the world market risk premium [RPm = E{RM) - RF ] with the global investable market 
(GIM) serving as a proxy for the world market. Given that the beta term is equal to the 
covariance of the asset class with the GIM divided by the variance of the GIM, the asset 
class risk premium can be estimated by:
0 i°iWPi1M
V ° M J
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The terms in the preceding equation can be rearranged to show that the asset class risk 
premium is equal to the GIM’s Sharpe ratio [(/?M - RF) / o M = RPM /o M ] multiplied by 
the standard deviation of the asset class’s return and its correlation with the GIM’s return.
The ICAPM estimates the expected return for an asset class as the risk-free rate plus an 
asset risk premium, which is the asset class beta times the GIM risk premium. This version 
is a simple, one-factor model with very rigid, and not very realistic, assumptions. For 
instance, it assumes perfect markets that are fully integrated and efficient.
Example 3-1
A portfolio manager is working on developing his capital market expectations. He 
manages a balanced fund consisting of stocks and bonds. The manager believes that 
the ICAPM will produce the best estimate of the expected returns for these two asset 
classes. He collected the following information for each.
Asset Class Standard Deviation Correlation with GI1V1
Stocks 12.0 % 0.75
Bonds 5.0% 0.65
The estimated Sharpe ratio for the global investable market (GIM) is 0.30 and the 
nominal risk-free rate is estimated to be 3.2 percent.
Calculate the expected return for stocks and bonds using theICAPM approach. 
Solution:
For the two asset classes in question, RPst0Cks = 0.30(12.0%)(0.75) = 2.7% and 
RPbonds = 0.30(5.0%)(0.65) = 0.975%. Adding these risk premiums to the risk-free rate 
produces the manager’s estimate of the expected returns.
E(Ri) = RF +RPi 
E(Rslocks) = 3.2%+ 2.7% = 5.9%
E {R-bonds) = 3.2% + 0.975% = 4.2%
Singer-Terhaar Approach
The Singer-Terhaar approach adds a level of sophistication to the simple ICAPM approach 
by incorporating market imperfections into the analysis. The two imperfections we will 
consider are market segmentation and illiquidity. Singer-Terhaar recognizes that the 
standard ICAPM risk premium should be adjusted for market segmentation (RPl ) and
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an additional risk premium should be added to the ICAPM return to compensate for 
illiquidity (RPLiquidity).
Market integration versus segmentation refers to the ability of capital to freely flow 
from one market to another. In a fully integrated market, the flow of capital between 
countries and/or asset classes is unimpeded by excessive costs, government interventions, 
or investor biases. Fully segmented markets are completely isolated from one another 
so that no capital flows between them at all. The extent to which markets are integrated 
is best thought of as a continuum with every country showing at least some degree of 
segmentation.
To reestimate an asset class’s risk premium in light of its degree of market segmentation, we 
first recognize that the standard ICAPM risk premium assumes a fully integrated, frictionless 
market. Taken to the opposite extreme, a fully segmented market restricts the reference global 
investable market (RM) to the local market so that the correlation between its returns and the 
asset class’s returns is 1.0, which is effectively the correlation of the local market with itself.
To determine the market segmentation-adjusted risk premium (RPt ), the analyst must 
compute two risk premiums, the fully integrated premium, which is the ICAPM risk 
premium (RPj), using the correlation coefficient between the asset class and the GIM 
(p;- M), and the fully segmented risk premium, which assumes that the asset class and the 
market portfolio are perfectly positively correlated.
A shrinkage estimate is used to combine the two extreme scenarios into a single risk 
premium. Recall that a shrinkage estimate is a weighted average where the weights sum to 
1.0. The analyst may subjectively assign weights, but empirical research shows that most 
developed markets are 65 to 85 percent integrated (35 to 15 percent segmented).
An example might help to illustrate the process of estimating the market segmentation- 
adjusted risk premium. Assume we are given the following information about two asset 
classes, stocks and bonds.
Asset Class Standard Deviation Correlation with GIM[
Stocks 18.1% 0.65
Bonds 9.1% 0.45
The Sharpe ratio for the GIM is 0.30 and the markets are 70 percent integrated with the 
world market.
To compute the market segmentation-adjusted risk premium (RP( ), we first use the 
ICAPM to estimate what it would be under a perfectly integrated scenario.
f RP,
\
M
V ° w y
RPstocks = (0.30)(18.1 %)(0.65) = 3.53% 
RPbonds = (0.30)(9.1%)(0.45) = 1.23%
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On the exam, 
make sure that 
you provide the 
examiners with the 
measure they want 
you to provide. For 
example, if you are 
asked to provide 
the risk premium 
of the asset class 
and you provide 
the expected return, 
then you would 
only receive partial 
credit for a morning 
session question.
The illiquidity 
premium is 
determined using 
the multiperiod 
Sharpe ratio 
(MPSR). You do not 
need to know the 
details of how to 
calculate the MPSR, 
but just be aware 
of it.
Next, compute the market segmentation risk premium under the opposite extreme of 
completely segmented markets where the correlations rise to 1.0 (effectively dropping it 
out of the equation).
RPstocks = (0.30)(18.1%)(1.00) = 5.43% 
RPbonds = (0.30)(9.1%)(1.00) = 2.73%
Finally, compute a shrinkage estimate based on the degree of market integration, which 
was given as 70 percent, leaving 30 percent applied to the fully segmented premium.
RP*ocks = (0.70)(3.53%) + (0.30)(5.43%) = 4.10% 
RPLds = (0.70)(1.23%) + (0.30)(2.73%) = 1.68%
Adding these risk premiums to the risk-free rate gives us updated expected returns that 
reflect the degree of market segmentation.
Turning to the illiquidity premium, a CFA exam question might simply state an 
appropriate premium as part of the fact pattern. For example, an alternative asset class 
might have an illiquidity premium of 0.3 percent. This premium would simply be added to 
the risk-free rate along with the segmentation-adjusted risk premium for that asset class as 
shown again here.
If, however, you are asked to estimate the illiquidity premium, only one method is 
described in the curriculum. It employs a multiperiod Sharpe ratio (MPSR), which is a 
measure of risk-adjusted return. A rational investor would only choose an alternative asset 
if its MPSR is at least as large as the market portfolio’s MPSR over the liquidity horizon. 
So, if the alternative asset’s MPSR (RP/o,) computed using ICAPM is less than the market 
portfolio’s (RPm/Gm), we can derive the return that would make them equal. The difference 
between this derived return and the ICAPM return is the liquidity premium.
Again, an example might help to illustrate the process of estimating the illiquidity risk 
premium. Assume we are given the following information about the global investable 
market (GIM) and two asset classes, common stocks and collectibles.
Asset Class Standard Deviation Correlation with GIM[
GIM 12.0% 1.00
Stocks 18.1% 0.65
Collectibles 19.1% 0.35
The Sharpe ratio for the GIM is 0.30, the risk-free rate is 3.2 percent, and all data are 
computed over collectibles’ average one-year illiquidity period.
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Common stocks are typically very liquid and do not carry a liquidity premium. 
Collectibles, however, can be very illiquid, so we must estimate an appropriate premium 
to account for illiquidity. To compute the illiquidity risk premium, we first compute the 
ICAPM risk premium for the collectibles asset class (RPC).
/
V
RPm
GcPc, M = (0.30)(19.1%)(0.35) = 2.0%
Next, compute the MPSR for collectibles.
( RPC^ 2.0%
MPSRC = = ------- = 0.10l ° c J 19.1%
Derive what the asset class’s risk premium should be to make the MPSRC equal to the 
MPSRm.
Finally, subtract the ICAPM risk premium from the risk premium derived from the MPSR 
to estimate the liquidity premium.
RPuquidity = 5.7% - 2.0% = 3.7%
Example 3-2
An analyst is using the Singer-Terhaar approach to estimate the expected returns for 
domestic stocks, bonds, and private equity.
Asset Class
Standard
Deviation
Correlation 
with GIM
Degree of 
Integration
Illiquidity
Premium
GIM 10.0% 1.00 1.00 0.0%
Stocks 12.0% 0.75 0.65 0.0%
Bonds 5.0% 0.65 0.65 0.0%
Private equity 25.0% 0.35 0.50 2.4%
The expected return on the GIM is 8.0 percent and the risk-free rate is 3.5 percent. 
Calculate the expected return for:
i. Domestic stocks
ii. Private equity
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CAPITAL MARKET EXPECTATIONS
Solutions:
i. The Singer-Terhaar approach starts with the ICAPM and adjusts the risk
premium for the level of market segmentation and adds an illiquidity premium 
when appropriate.
£(/?, ) = Rf + RP* + RPtliquidity
Since stocks are very liquid, the illiquidity risk premium is zero, dropping out of 
the equation. The market segmentation-adjusted risk premium is the weighted 
average of the ICAPM (fully integrated) version and the fully segmented 
(perfect positive correlation), where the weights are the degree of integration and 
its complement (degree of segmentation).
RP,ICAPM
V ° M J
^sPs.M
V a M
° s ? s , M
y
8.0%-3.5% \
(12.0%)(0.75) = 4.05%
10.0% j
RPsesmented = (0.45)(12.0%)(1.00)= 5.40%
R P s - W ICAPM RPlCAPM + (1 ~ W ICAPM )RP,segmented
= 0.65(4.05%) + 0.35(5.40%) = 4.52%
With no illiquidity premium, our estimate of the expected return on domestic 
stocks is the segmentation-adjusted risk premium added to the risk-free rate.
E(RS) = Rf + RP* + RPliquidity - 3.5% + 4.4% + 0% = 8.0%
li. We use exactly the same process to estimate the segmentation-adjusted risk 
premium for private equity, but this time the illiquidity premium is not zero.
RPICAPM
V J
a p P r,M -
' Rm - R f '
V a M
°pP r ,M
/
 ̂8 .0 % -3.5'
v 10.0% y
(25.0%)(0.35) = 3.94%
^segm ented = (0.45)(25.0%)(1.00) = 11.25%
“ w ICAPM RPlCAPM + (1 “ w ICAPM )R P Segmented
= 0.50(3.94%)+ 0.50(11.25%) = 7.60%
The estimate for private equity’s expected return is the risk-free rate, plus the 
segmentation-adjusted risk premium, plus the liquidity premium, which was 
given in the fact pattern to be 2.4 percent.
E(Rp) = RF + RP* + RPliquidity = 3.5% + 7.6% + 2.4% = 13.5%
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CAPITAL MARKET EXPECTATIONS
LOS 14d: Explain the use of survey and panel methods and judgment in 
setting capital market expectations. Vol 3, pp 48-50
Consensus Opinion Methods
Capital market expectations can also be determined by surveying the opinion of experts. 
When the same group of experts is queried over a series of surveys, the approach is called 
a panel method. This is effectively a consensus opinion approach that relies on the work of 
others.
The Role of Judgment
Quantitative models provide an objective rationale for forecasts. However, experience and 
judgment are critical complements to analysis. Remember that models are completely 
dependent upon the quality of inputs. Garbage in ... garbage out.
LESSON 4: ECONOMIC ANALYSIS, PART 1: INTRODUCTION 
AND BUSINESS CYCLE ANALYSIS
LOS 14e: Discuss the inventory and business cycles, the impact of consumer 
and business spending, and monetary and fiscal policy on the business 
cycle. Vol 3, pp 50-54
MACROECONOMICS
Asset returns (expected and actual) are closely related to economic activity. Accelerating 
economic activity drives revenues higher, which in turn drive profits higher, which increase 
cash flows available to the asset owners and increases the value of assets that represent 
claims on those cash flows. Declining economic activity has the reverse effect.
This relation is consistent with asset-pricing theory, which predicts higher risk premiums 
for assets that are strongly, positively correlated with the ups and downs of economic 
activity and low risk premiums for assets with payoffs that are weakly, or negatively, 
correlated with the economy.
The Business Cycle
The economy follows a general upward, long-term trend through time but not in a straight 
line. The business cycle describes the recurring ebb and flow of economic activity along its 
long-term trend line. Economists often refer to a short-term inventory cycle, lasting two to 
four years, and a longer, nine- to eleven-year business cycle.
Although economic cycles reflect recurring and measurable variations in economic 
activity that can be clearly seen in retrospect, anticipating those movements with effective 
forecasts is a significant challenge. The key metrics for monitoring economic activity and 
the business cycle include real GDP, the output gap, and whether or not the economy is in 
recession.
Gross domestic product (GDP) is the standard measure of economic output representing 
the value of all finished goods and services produced in a particular country during 
the year regardless of who owns the assets that produced them. As measured from an
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expenditures approach, GDP is the sum of consumption (C), investment (7), government 
spending (G), and net exports ( X - M).
GDP = C + I + G + ( X - M )
The change in total output is a function of change in quantity demanded and change in 
pricing. GDP can change as a result of changes in prices (P) or a change in the quantity 
of goods and services actually produced ( 0 , where nominal GDP = PQ. Since we’re 
interested in economic output as a measure of well-being, we’ll focus on real GDP, which 
is nominal GDP adjusted to reflect a constant price level.
Output gap: Potential GDP measures the level of output that could be achieved if the 
economy operates at its most efficient level. Potential GDP gradually increases as the 
country’s capacity to produce increases. The path of this gradual rise in capacity is shown 
as the long-term trend line in Exhibit 4-1.
Exhibit 4-1: The Business Cycle
An easy way to 
remember the 
output gap is that a 
positive output gap 
is associated with 
times of recession, 
slow growth, and 
declining inflation. 
As the output gap 
closes, economic 
activity picks up 
along with rising 
inflation.
As shown in Exhibit 4-1, the output gap is the difference between potential GDP (the long-
term trend line) and the current level of GDP (the purple fine). The business cycle represents 
departures from potential GDP where a positive output gap shows real GDP below the long-
term trend line, and negative output gaps are represented by points above the trend fine. As the 
economy expands beyond efficient capacity (overtime hours and accelerating wear and tear 
on equipment), current real GDP rises above potential GDP and inflationary pressures build. 
Once the economy slows and real GDP falls below potential GDP (idle workers, mothballed 
facilities and equipment), the rate of unemployment increases.
Recession: An economic contraction follows a peak in the business cycle. It can merely 
represent movement back toward the long-term growth rate. However, if real GDP declines 
in two successive quarters, the contraction is officially deemed a recession.
The following discusses the inventory cycle and the business cycle in more detail.
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The Inventory Cycle
In the most basic model of a company, a product is manufactured and then sold to 
customers. The managers of the company try to ensure that enough product inventory is 
available to fill customer orders, but not so much that excessive storage costs are incurred 
or that too much capital is tied up in unsold merchandise. However, manufacturing takes 
time, so some inventory must be kept on hand, although carefully managed.
The inventory cycle measures the fluctuations in inventories, which come about because 
of managements’ activities in balancing inventory levels based on their near-term 
expectations about demand (economic activity). If inventories begin to build, managers 
might slow down production in anticipation of an economic contraction. If inventories 
begin to fall, they might increase production in order to meet rising demand.
In the positive phase of the inventory cycle, business confidence is high, production is 
increasing, employment is expanding, and GDP grows. This continues up to an inflection 
point when businesses view their inventories as too high, which might occur when sales 
suddenly disappoint or real GDP growth slows. Restrictive monetary policy and higher 
input prices may also provoke production cuts to reduce inventories. The inventory cycle 
then enters the contraction phase with waning confidence, slowing production, rising 
unemployment, and slow or declining GDP growth.
LOS 14f: Discuss the impact that the phases of the business cycle have on 
short-term/ long-term capital market returns. Vol 3, pp 52-54
Phases of the Business Cycle
Here we’ll take a closer look at the phases of the business cycle and the typical reactions 
they elicit in the capital markets (see Exhibit 4-2 and Table 4-1).
Exhibit 4-2: Phases of the Business Cycle
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	Contents
	Study Session 7: Applications of Economic Analysis to Portfolio Management
	Reading 14: Capital Market Expectations