Table of Contents

1.

2.

3.

4.

Getting Started Flyer

Contents

Readings and Learning Outcome Statements

Capital Market Expectations

1. Exam Focus

2. Formulating Capital Market Expectations

3. LOS 15.a

4. Problems in Forecasting

5. LOS 15.b

6. LOS 15.c

7. The Use of Surveys and Judgment for Capital Market Expectations

8. LOS 15.d

9. Economic Analysis

10. LOS 15.e

11. LOS 15.f

12. Inflation and Asset Returns

13. LOS 15.g

14. The Taylor Rule

15. LOS 15.h

16. The Yield Curve

17. LOS 15.i

18. Economic Growth Trends

19. LOS 15.j

20. LOS 15.k

21. Links Between Economies

22. LOS 15.l

23. Emerging Market Economies

24. LOS 15.m

25. Economic Forecasting

26. LOS 15.n

27. Economic Conditions and Asset Class Returns

28. LOS 15.o

29. LOS 15.p

30. Forecasting Exchange Rates

31. LOS 15.q

32. Reallocating a Global Portfolio

33. LOS 15.r

34. Key Concepts

1. LOS 15.a

2. LOS 15.b

3. LOS 15.c

4. LOS 15.d

5. LOS 15.e

6. LOS 15.f

7. LOS 15.g

8. LOS 15.h

9. LOS 15.i

10. LOS 15.j

11. LOS 15.k

12. LOS 15.l

13. LOS 15.m

14. LOS 15.n

15. LOS 15.o

16. LOS 15.p

17. LOS 15.q

18. LOS 15.r

35. Concept Checkers

36. Answers – Concept Checkers

5. Equity Market Valuation

1. Exam Focus

2. Cobb-Douglas Production Function

3. LOS 16.a

4. LOS 16.b

5. LOS 16.c

6. LOS 16.d

7. LOS 16.e

8. Relative Equity Market Valuation

9. LOS 16.f

10. LOS 16.g

11. Key Concepts

1. LOS 16.a

2. LOS 16.b

3. LOS 16.c LOS 16.d

4. LOS 16.e

5. LOS 16.f LOS 16.g

12. Concept Checkers

13. Answers – Concept Checkers

6. Self-Test: Economic Analysis

7. Asset Allocation

1. Exam Focus

2. Strategic Asset Allocation

3. LOS 17.a

4. Tactical Asset Allocation

5. LOS 17.b

6. LOS 17.c

7. LOS 17.d

8. Dynamic and Static Asset Allocation

9. LOS 17.e

10. LOS 17.f

11. Specifying Risk and Return Objectives

12. LOS 17.g

13. Specifying Asset Classes

14. LOS 17.h

15. LOS 17.j

16. LOS 17.k

17. Risk in International Assets

18. LOS 17.l

19. LOS 17.m

20. LOS 17.n

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

Steps in Asset Allocation

LOS 17.o

Approaches to Asset allocation

LOS 17.p

Constraints Against Short Sales

LOS 17.q

LOS 17.i

LOS 17.r

Strategic Asset Allocation Issues

LOS 17.s

Tactical Allocation

LOS 17.t

Key Concepts

1. LOS 17.a

2. LOS 17.b

3. LOS 17.c

4. LOS 17.d

5. LOS 17.e

6. LOS 17.f

7. LOS 17.g

8. LOS 17.h

9. LOS 17.i LOS 17.r

10. LOS 17.j

11. LOS 17.k

12. LOS 17.l

13. LOS 17.m

14. LOS 17.n

15. LOS 17.o

16. LOS 17.p

17. LOS 17.q

18. LOS 17.s

19. LOS 17.t

34. Concept Checkers

35. Answers – Concept Checkers

8. Currency Management: An Introduction

1. Exam Focus

2. Introduction

3. Effects of Currency on Portfolio Risk and Return

4. LOS 18.a

5. Calculating Portfolio Return for Multiple Investments in Foreign Assets

6. Risk

7. Strategic Decisions

8. LOS 18.b

9. LOS 18.c

10. Tactical Currency Management

11. LOS 18.d

12. LOS 18.e

13. Currency Management Tools

14. LOS 18.f

15. Roll Yield

16. Strategies to Modify Risk and Lower Hedging Costs

17. LOS 18.g

18.

19.

20.

21.

22.

Hedging Multiple Currencies

LOS 18.h

Managing Emerging Market Currency

LOS 18.i

Key Concepts

1. LOS 18.a

2. LOS 18.b

3. LOS 18.c

4. LOS 18.d

5. LOS 18.e

6. LOS 18.f

7. LOS 18.g

8. LOS 18.h

9. LOS 18.i

23. Concept Checkers

24. Answers – Concept Checkers

9. Market Indexes and Benchmarks

1. Exam Focus

2. Benchmarks vs. Indexes

3. LOS 19.a

4. Investment Uses of Benchmarks

5. LOS 19.b

6. Types of Benchmarks

7. LOS 19.c

8. LOS 19.d

9. Use of Market Indexes

10. LOS 19.e

11. Index Construction

12. LOS 19.f

13. The Pros and Cons of Approaches to Index Weighting

14. LOS 19.g

15. LOS 19.h

16. Key Concepts

1. LOS 19.a

2. LOS 19.b

3. LOS 19.c

4. LOS 19.d

5. LOS 19.e

6. LOS 19.f

7. LOS 19.g

8. LOS 19.h

17. Concept Checkers

18. Answers – Concept Checkers

10. Self-Test: Asset Allocation

11. Fixed-Income Portfolio Management—Part I

1. Exam Focus

2. Bond Portfolio Benchmarks

3. LOS 20.a

4. Bond Indexing Strategies

5. LOS 20.b

6. Selecting a Benchmark Bond Index

7. LOS 20.c

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

LOS 20.d

LOS 20.e

Scenario Analysis

LOS 20.f

Immunization

LOS 20.g

Warm-Up: Duration as a Measure of Bond Portfolio Risk

Duration Contribution

Adjusting Dollar Duration

LOS 20.h

Spread Duration

LOS 20.i

Extensions to Classical Immunization

LOS 20.j

Immunization Risks

LOS 20.k

Immunizing Single Liabilities, Multiple Liabilities, and General Cash Flows

LOS 20.l

Risk Minimization vs. Return Maximization

LOS 20.m

Cash Flow Matching

LOS 20.n

Key Concepts

1. LOS 20.a

2. LOS 20.b

3. LOS 20.c

4. LOS 20.d

5. LOS 20.e

6. LOS 20.f

7. LOS 20.g

8. LOS 20.h

9. LOS 20.i

10. LOS 20.j

11. LOS 20.k

12. LOS 20.l

13. LOS 20.m

14. LOS 20.n

31. Concept Checkers

32. Answers – Concept Checkers

12. Relative-Value Methodologies for Global Credit Bond Portfolio Management

1. Exam Focus

2. Relative Value Analysis

3. LOS 21.a

4. Cyclical and Secular Changes

5. LOS 21.b

6. Liquidity

7. LOS 21.c

8. Rationales for Secondary Bond Trades

9. LOS 21.d

10. Duration Management

11. Relative Value Analysis

12. LOS 21.e

13.

14.

15.

16.

17.

Measuring Spread

Spread Analysis

Bond Structures

Credit Analysis

Key Concepts

1. LOS 21.a

2. LOS 21.b

3. LOS 21.c

4. LOS 21.d

5. LOS 21.e

18. Concept Checkers

19. Answers – Concept Checkers

13. Fixed-Income Portfolio Management—Part II

1. Exam Focus

2. Leverage

3. LOS 22.a

4. Repurchase Agreements

5. LOS 22.b

6. Bond Risk Measures

7. LOS 22.c

8. Futures Contracts

9. Advantages of Interest Rate Futures

10. LOS 22.d

11. Dollar Duration

12. Duration Management

13. LOS 22.e

14. LOS 22.f

15. Managing Default Risk, Credit Spread Risk, and Downgrade Risk With Derivatives

16. LOS 22.g

17. International Bond Excess Returns

18. LOS 22.h

19. International Bond Durations

20. LOS 22.i

21. The Hedging Decision

22. LOS 22.j

23. Breakeven Spread Analysis

24. LOS 22.k

25. Emerging Market Debt

26. LOS 22.l

27. Selecting a Fixed-Income Manager

28. LOS 22.m

29. Key Concepts

1. LOS 22.a

2. LOS 22.b

3. LOS 22.c

4. LOS 22.d

5. LOS 22.e

6. LOS 22.f

7. LOS 22.g

8. LOS 22.h

9. LOS 22.i

10. LOS 22.j

14.

15.

16.

17.

11. LOS 22.k

12. LOS 22.l

13. LOS 22.m

30. Concept Checkers

31. Answers – Concept Checkers

Self-Test: Fixed-Income Portfolio Management

Formulas

Copyright

Pages List Book Version

BOOK 3 – ECONOMIC ANALYSIS, ASSET ALLOCATION AND

FIXED-INCOME PORTFOLIO MANAGEMENT

Readings and Learning Outcome Statements

Study Session 7 – Applications of Economic Analysis to Portfolio Management

Self-Test – Economic Analysis

Study Session 8 – Asset Allocation and Related Decisions in Portfolio Management (1)

Study Session 9 – Asset Allocation and Related Decisions in Portfolio Management (2)

Self-Test – Asset Allocation

Study Session 10 – Fixed-Income Portfolio Management (1)

Study Session 11 – Fixed-Income Portfolio Management (2)

Self-Test – Fixed-Income Portfolio Management

Formulas

READINGS AND LEARNING OUTCOME S TATEMENTS

R EADI NGS

The following material is a review of the Fixed Income Portfolio Management, Fixed Income

Derivatives, and Equity Portfolio Management principles designed to address the learning outcome

statements set forth by CFA Institute.

STUDY SESSION 7

Reading Assignments

Applications of Economic Analysis to Portfolio Management, CFA Program 2017 Curriculum, Volume

3, Level III

15. Capital Market Expectations (page 1)

16. Equity Market Valuation (page 56)

STUDY SESSION 8

Reading Assignments

Asset Allocation and Related Decisions in Portfolio Management (1), CFA Program 2017 Curriculum,

Volume 3, Level III

17. Asset Allocation (page 84)

STUDY SESSION 9

Reading Assignments

Asset Allocation and Related Decisions in Portfolio Management (2), CFA Program 2017 Curriculum,

Volume 3, Level III

18. Currency Management: An Introduction (page 144)

19. Market Indexes and Benchmarks (page 186)

STUDY SESSION 10

Reading Assignments

Fixed-Income Portfolio Management (1), CFA Program 2017 Curriculum, Volume 4, Level III

20. Fixed-Income Portfolio Management—Part I (page 200)

21. Relative-Value Methodologies for Global Credit Bond Portfolio Management (page 245)

STUDY SESSION 11

Reading Assignments

Fixed-Income Portfolio Management (2), CFA Program 2017 Curriculum, Volume 4, Level III

22. Fixed-Income Portfolio Management—Part II (page 259)

L EARNI NG O UTCOME S TATEMENTS (LOS)

The CFA Institute learning outcome statements are listed in the following. These are repeated in each

topic review. However, the order may have been changed in order to get a better fit with the flow of

the review.

STUDY SESSION 7

The topical coverage corresponds with the following CFA Institute assigned reading:

1 5 . Capital Mar ket Ex pectations

The candidate should be able to:

a. discuss the role of, and a framework for, capital market expectations in the portfolio management process. (page 1)

b. discuss challenges in developing capital market forecasts. (page 2)

c. 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. (page 6)

d. explain the use of survey and panel methods and judgment in setting capital market expectations. (page 17)

e. discuss the inventory and business cycles, the impact of consumer and business spending, and monetary and fiscal

policy on the business cycle. (page 18)

f. discuss the impact that the phases of the business cycle have on short-term/long-term capital market returns. (page 19)

g. explain the relationship of inflation to the business cycle and the implications of inflation for cash, bonds, equity, and

real estate returns. (page 21)

h. demonstrate the use of the Taylor rule to predict central bank behavior. (page 23)

i. evaluate 1) the shape of the yield curve as an economic predictor and 2) the relationship between the yield curve and

fiscal and monetary policy. (page 25)

j. identify and interpret the components of economic growth trends and demonstrate the application of economic growth

trend analysis to the formulation of capital market expectations. (page 26)

k. explain how exogenous shocks may affect economic growth trends. (page 28)

l. identify and interpret macroeconomic, interest rate, and exchange rate linkages between economies. (page 29)

m. discuss the risks faced by investors in emerging-market securities and the country risk analysis techniques used to

evaluate emerging market economies. (page 30)

n. compare the major approaches to economic forecasting. (page 31)

o. demonstrate the use of economic information in forecasting asset class returns. (page 33)

p. explain how economic and competitive factors can affect investment markets, sectors, and specific securities. (page 33)

q. discuss the relative advantages and limitations of the major approaches to forecasting exchange rates. (page 36)

r. recommend and justify changes in the component weights of a global investment portfolio based on trends and

expected changes in macroeconomic factors. (page 38)

The topical coverage corresponds with the following CFA Institute assigned reading:

1 6 . Equity Mar ket Valuation

The candidate should be able to:

a. explain the terms of the Cobb-Douglas production function and demonstrate how the function can be used to model

growth in real output under the assumption of constant returns to scale. (page 56)

b. evaluate the relative importance of growth in total factor productivity, in capital stock, and in labor input given relevant

historical data. (page 58)

c. demonstrate the use of the Cobb-Douglas production function in obtaining a discounted dividend model estimate of the

intrinsic value of an equity market. (page 60)

d. critique the use of discounted dividend models and macroeconomic forecasts to estimate the intrinsic value of an equity

market. (page 60)

e. contrast top-down and bottom-up approaches to forecasting the earnings per share of an equity market index. (page

63)

f. discuss the strengths and limitations of relative valuation models. (page 64)

g. judge whether an equity market is under-, fairly, or over-valued using a relative equity valuation model. (page 64)

STUDY SESSION 8

The topical coverage corresponds with the following CFA Institute assigned reading:

1 7 . A sset A llocation

The candidate should be able to:

a. explain the function of strategic asset allocation in portfolio management and discuss its role in relation to specifying

and controlling the investor’s exposures to systematic risk. (page 84)

b. compare strategic and tactical asset allocation. (page 85)

c. discuss the importance of asset allocation for portfolio performance. (page 85)

d. contrast the asset-only and asset/liability management (ALM) approaches to asset allocation and discuss the investor

circumstances in which they are commonly used. (page 85)

e. explain the advantage of dynamic over static asset allocation and discuss the trade-offs of complexity and cost. (page

86)

f. explain how loss aversion, mental accounting, and fear of regret may influence asset allocation policy. (page 86)

g. evaluate return and risk objectives in relation to strategic asset allocation. (page 88)

h. evaluate whether an asset class or set of asset classes has been appropriately specified. (page 91)

i. select and justify an appropriate set of asset classes for an investor. (page 114)

j. evaluate the theoretical and practical effects of including additional asset classes in an asset allocation. (page 92)

k. demonstrate the application of mean–variance analysis to decide whether to include an additional asset class in an

existing portfolio. (page 93)

l. describe risk, cost, and opportunities associated with nondomestic equities and bonds. (page 95)

m. explain the importance of conditional return correlations in evaluating the diversification benefits of nondomestic

investments. (page 98)

n. explain expected effects on share prices, expected returns, and return volatility as a segmented market becomes

integrated with global markets. (page 99)

o. explain the major steps involved in establishing an appropriate asset allocation. (page 100)

p. discuss the strengths and limitations of the following approaches to asset allocation: mean–variance, resampled efficient

frontier, Black–Litterman, Monte Carlo simulation, ALM, and experience based. (page 100)

q. discuss the structure of the minimum-variance frontier with a constraint against short sales. (page 112)

r. formulate and justify a strategic asset allocation, given an investment policy statement and capital market expectations.

(page 114)

s. compare the considerations that affect asset allocation for individual investors versus institutional investors and critique

a proposed asset allocation in light of those considerations. (page 120)

t. formulate and justify tactical asset allocation (TAA) adjustments to strategic asset class weights, given a TAA strategy and

expectational data. (page 123)

STUDY SESSION 9

The topical coverage corresponds with the following CFA Institute assigned reading:

1 8 . Cur r ency Management: A n Intr oduction

The candidate should be able to:

a. analyze the effects of currency movements on portfolio risk and return. (page 149)

b. discuss strategic choices in currency management. (page 153)

c. formulate an appropriate currency management program given financial market conditions and portfolio objectives and

constraints. (page 156)

d. compare active currency trading strategies based on economic fundamentals, technical analysis, carry-trade, and

volatility trading. (page 156)

e. describe how changes in factors underlying active trading strategies affect tactical trading decisions. (page 161)

f. describe how forward contracts and FX (foreign exchange) swaps are used to adjust hedge ratios. (page 163)

g. describe trading strategies used to reduce hedging costs and modify the risk–return characteristics of a foreign-currency

portfolio. (page 169)

h. describe the use of cross-hedges, macro-hedges, and minimum-variance-hedge ratios in portfolios exposed to multiple

foreign currencies. (page 171)

i. discuss challenges for managing emerging market currency exposures. (page 174)

The topical coverage corresponds with the following CFA Institute assigned reading:

1 9 . Mar ket Index es and Benchmar ks

The candidate should be able to:

a. distinguish between benchmarks and market indexes. (page 186)

b. describe investment uses of benchmarks. (page 187)

c. compare types of benchmarks. (page 187)

d. contrast liability-based benchmarks with asset-based benchmarks. (page 188)

e. describe investment uses of market indexes. (page 188)

f. discuss tradeoffs in constructing market indexes. (page 189)

g. discuss advantages and disadvantages of index weighting schemes. (page 190)

h. evaluate the selection of a benchmark for a particular investment strategy. (page 191)

STUDY SESSION 10

The topical coverage corresponds with the following CFA Institute assigned reading:

2 0 . Fix ed-Income Por tfolio Management—Par t I

The candidate should be able to:

a. compare, with respect to investment objectives, the use of liabilities as a benchmark and the use of a bond index as a

benchmark. (page 200)

b. compare pure bond indexing, enhanced indexing, and active investing with respect to the objectives, advantages,

disadvantages, and management of each. (page 201)

c. discuss the criteria for selecting a benchmark bond index and justify the selection of a specific index when given a

description of an investor’s risk aversion, income needs, and liabilities. (page 204)

d. critique the use of bond market indexes as benchmarks. (page 205)

e. describe and evaluate techniques, such as duration matching and the use of key rate durations, by which an enhanced

indexer may seek to align the risk exposures of the portfolio with those of the benchmark bond index. (page 206)

f. contrast and demonstrate the use of total return analysis and scenario analysis to assess the risk and return

characteristics of a proposed trade. (page 209)

g. formulate a bond immunization strategy to ensure funding of a predetermined liability and evaluate the strategy under

various interest rate scenarios. (page 211)

h. demonstrate the process of rebalancing a portfolio to reestablish a desired dollar duration. (page 219)

i. explain the importance of spread duration. (page 221)

j. discuss the extensions that have been made to classical immunization theory, including the introduction of contingent

immunization. (page 223)

k. explain the risks associated with managing a portfolio against a liability structure including interest rate risk, contingent

claim risk, and cap risk. (page 226)

l. compare immunization strategies for a single liability, multiple liabilities, and general cash flows. (page 227)

m. compare risk minimization with return maximization in immunized portfolios. (page 229)

n. demonstrate the use of cash flow matching to fund a fixed set of future liabilities and compare the advantages and

disadvantages of cash flow matching to those of immunization strategies. (page 229)

The topical coverage corresponds with the following CFA Institute assigned reading:

2 1 . Relative-Value Methodologies for Global Cr edit Bond Por tfolio Management

The candidate should be able to:

a. explain classic relative-value analysis, based on top-down and bottom-up approaches to credit bond portfolio

management. (page 245)

b. discuss the implications of cyclical supply and demand changes in the primary corporate bond market and the impact of

secular changes in the market’s dominant product structures. (page 246)

c. explain the influence of investors’ short- and long-term liquidity needs on portfolio management decisions. (page 247)

d. discuss common rationales for secondary market trading. (page 247)

e. discuss corporate bond portfolio strategies that are based on relative value. (page 249)

STUDY SESSION 11

The topical coverage corresponds with the following CFA Institute assigned reading:

2 2 . Fix ed-Income Por tfolio Management—Par t II

The candidate should be able to:

a. evaluate the effect of leverage on portfolio duration and investment returns. (page 259)

b. discuss the use of repurchase agreements (repos) to finance bond purchases and the factors that affect the repo rate.

(page 262)

c. critique the use of standard deviation, target semivariance, shortfall risk, and value at risk as measures of fixed-income

portfolio risk. (page 264)

d. demonstrate the advantages of using futures instead of cash market instruments to alter portfolio risk. (page 266)

e. formulate and evaluate an immunization strategy based on interest rate futures. (page 267)

f. explain the use of interest rate swaps and options to alter portfolio cash flows and exposure to interest rate risk. (page

272)

g. compare default risk, credit spread risk, and downgrade risk and demonstrate the use of credit derivative instruments

to address each risk in the context of a fixed-income portfolio. (page 275)

h. explain the potential sources of excess return for an international bond portfolio. (page 278)

i. evaluate 1) the change in value for a foreign bond when domestic interest rates change and 2) the bond’s contribution

to duration in a domestic portfolio, given the duration of the foreign bond and the country beta. (page 279)

j. recommend and justify whether to hedge or not hedge currency risk in an international bond investment. (page 281)

k. describe how breakeven spread analysis can be used to evaluate the risk in seeking yield advantages across

international bond markets. (page 287)

l. discuss the advantages and risks of investing in emerging market debt. (page 288)

m. discuss the criteria for selecting a fixed-income manager. (page 289)

The following is a review of the Capital Market Expectations in Portfolio Management principles designed to address the

learning outcome statements set forth by CFA Institute. Cross-Reference to CFA Institute Assigned Reading #15.

CAPITAL MARKET E XPECTATIONS

Study Session 7

EXAM FOCUS

Combining capital market expectations with the client’s objectives and constraints leads to the

portfolio’s strategic asset allocation. A variety of economic tools and techniques are useful in forming

capital market expectations for return, risk, and correlation by asset class. Unfortunately, no one

technique works consistently, so be prepared for any technique and its issues as covered here.

FORMULATING CAPITAL MARKET EXPECTATIONS

LOS 15.a: Discuss the role of, and a framework for, capital market expectations in the portfolio

management process.

Capital market expectations can be referred to as macro expectations (expectations regarding

classes of assets) or micro expectations (expectations regarding individual assets). Micro

expectations are most directly used in individual security selection. In other assignments, macro

expectations are referred to as top-down while micro expectations are referred to as bottom-up.

Using a disciplined approach leads to more effective asset allocations and risk management.

Formulating capital market expectations is referred to as beta research because it is related to

systematic risk. It can be used in the valuation of both equities and fixed-income securities. Alpha

research, on the other hand, is concerned with earning excess returns through the use of specific

strategies within specific asset groups.

To formulate capital market expectations, the analyst should use the following 7-step process.

Step 1: Determine the specific capital market expectations needed according to the investor’s tax

status, allowable asset classes, and time horizon. Time horizon is particularly important in

determining the set of capital market expectations that are needed.

Step 2: Investigate assets’ historical performance to determine the drivers that have affected past

performance and to establish some range for plausible future performance. With the drivers of past

performance established, the analyst can use these to forecast expected future performance as well

as compare the forecast to past results to see if the forecast appears reasonable.

Step 3: Identify the valuation model used and its requirements. For example, a comparables-based,

relative value approach used in the United States may be difficult to apply in an emerging market

analysis.

Step 4: Collect the best data possible. The use of faulty data will lead to faulty conclusions. The

following issues should be considered when evaluating data for possible use:

Calculation methodologies.

Data collection techniques.

Data definitions.

Error rates.

Investability and correction for free float.

Turnover in index components.

Potential biases.

Step 5: Use experience and judgment to interpret current investment conditions and decide what

values to assign to the required inputs. Verify that the inputs used for the various asset classes are

consistent across classes.

Step 6: Formulate capital market expectations. Any assumptions and rationales used in the analysis

should be recorded. Determine that what was specified in Step 1 has been provided.

Step 7: Monitor performance and use it to refine the process. If actual performance varies

significantly from forecasts, the process and model should be refined.

PROBLEMS IN FORECASTING

LOS 15.b: Discuss challenges in developing capital market forecasts.

As mentioned earlier, poor forecasts can result in inappropriate asset allocations. The analyst should

be aware of the potential problems in data, models, and the resulting capital market expectations.

Nine problems encountered in producing forecasts are (1) limitations to using economic data, (2)

data measurement error and bias, (3) limitations of historical estimates, (4) the use of ex post risk

and return measures, (5) non-repeating data patterns, (6) failing to account for conditioning

information, (7) misinterpretation of correlations, (8) psychological traps, and (9) model and input

uncertainty.

1. There are several limitations to using economic data. First, the time lag between

collection and distribution is often quite long. The International Monetary Fund, for

example, reports data with a lag of as much as two years. Second, data are often revised

and the revisions are not made at the same time as the publication. Third, data definitions

and methodology change over time. For example, the basket of goods in the Consumer

Price Index changes over time. Last, data indices are often rebased over time (i.e., the base

upon which they are calculated is changed). Although a rebasing is not a substantial change

in the data itself, the unaware analyst could calculate changes in the value of the indices

incorrectly if she does not make an appropriate adjustment.

2. There are numerous possible data measurement errors and biases. Transcription errors

are the misreporting or incorrect recording of information and are most serious if they are

biased in one direction. Survivorship bias commonly occurs if a manager or a security return

series is deleted from the historical performance record of managers or firms. Deletions

are often tied to poor performance and bias the historical return upward. Appraisal

(smoothed) data for illiquid and infrequently priced assets makes the path of returns appear

smoother than it actually is. This biases downward the calculated standard deviation and

makes the returns seem less correlated (closer to 0) with more liquid priced assets. This is a

particular problem for some types of alternative assets such as real estate. Rescaling the

data based on underlying economic drivers can be used to leave the mean return

unaffected but increase the variance.

3. The limitations of historical estimates can also hamper the formation of capital market

expectations. The values from historical data must often be adjusted going forward as

economic, political, regulatory, and technological environments change. This is particularly

true for volatile assets such as equity. These changes are known as regime changes and

result in nonstationary data. For example, the bursting of the technology bubble in 2000

resulted in returns data that were markedly different than that from the previous five years.

Nonstationarity would mean different periods in the time series have different statistical

properties and create problems with standard statistical testing methods.

Historical data is the starting point for estimating the following capital market expectations:

expected return, standard deviation, and correlations. However, it is not obvious how to

select the time period of historical data. A long time period is preferable for several

reasons.

It may be statistically required. To calculate historical covariance (and

correlation), the number of data points must exceed the number of covariances to

be calculated.

A larger data set (time period) provides more precise statistical estimates with

smaller variance to the estimates.

As a related issue, if the time period is longer for a larger data set, the calculated

statistics are generally less sensitive to the starting and ending points selected for

the time period.

However, long time periods also create potential problems.

A longer time period is more likely to include regime changes, which are shifts in

underlying fundamentals. Each regime change creates a subperiod with distinctly

different characteristics. For example, the behavior of real estate and virtually

every financial asset was different before and after the Financial Market

Meltdown of 2008. 1) This creates nonstationarity, which invalidates many

statistics calculated from time periods starting before and ending after the

meltdown. 2) It forces the analyst to use judgment to decide whether the

subperiod before or after the meltdown will be more relevant going forward.

It may mean the relevant time period is too short to be statistically significant.

It creates a temptation to use more frequent data, such as weekly data, rather

than monthly data points in order to have a larger sample size. Unfortunately,

more frequent data points are often more likely to have missing or outdated

values (this is called asynchronism) and can result in lower, distorted correlation

calculations.

Two questions can be used to help resolve the issue of time period to select:

1. Is there a reason to believe the entire (longer) time period is not appropriate?

2. If the answer to the first question is yes, does a statistical test confirm there is a

regime change and the point in the time series where it occurs?

If both answers are yes, the analyst must use judgment to select the relevant sub period.

Professor’s Note: I hope most candidates recognize the discussions above have

been referring to many of the statistical testing issues covered at Level I and II.

The focus here is not on performing such tests or even knowing which specific

tests to use, but on recognizing times and ways testing can be relevant. Think of

a senior portfolio manager who understands the larger issues and when to ask

others with relevant technical skills to do further analysis. This is a common

perspective at Level III.

4. Using ex post data (after the fact) to determine ex ante (before the fact) risk and return

can be problematic. For example, suppose that several years ago investors were fearful that

the Federal Reserve was going to have to raise interest rates to combat inflation. This

situation would cause depressed stock prices. If inflation abated without the Fed’s

intervention, then stock returns would increase once the inflation scenario passes. Looking

back on this situation, the researcher would conclude that stock returns were high while

being blind to the prior risk that investors had faced. The analyst would then conclude that

future (ex ante) returns for stocks will be high. In sum, the analyst would underestimate the

risks that equity investors face and overestimate their potential returns.

5. Using historical data, analysts can also uncover patterns in security returns that are unlikely

to occur in the future and can produce biases in the data. One such bias is data mining. Just

by random chance, some variables will appear to have a relationship with security returns,

when, in fact, these relationships are unlikely to persist. For example, if the analyst uses a

5% significance level and examines the relationship between stock returns and 40 randomly

selected variables, two (5%) of the variables are expected to show a statistically significant

relationship with stock returns just by random chance. Another potential bias results from

the time span of data chosen (time period bias). For example, small-cap U.S. stocks are

widely thought to outperform large-cap stocks, but their advantage disappears when data

from the 1970s and 1980s is excluded.

To avoid these biases, the analyst should first ask himself if there is any economic basis for

the variables found to be related to stock returns. Second, he should scrutinize the modeling

process for susceptibility to bias. Third, the analyst should test the discovered relationship

with out-of-sample data to determine if the relationship is persistent. This would be done by

estimating the relationship with one portion of the historical data and then reexamining it

with another portion.

6. Analysts’ forecasts may also fail to account for conditioning information. The relationship

between security returns and economic variables is not constant over time. Historical data

reflects performance over many different business cycles and economic conditions. Thus,

analysts should account for current conditions in their forecasts. As an example, suppose a

firm’s beta is estimated at 1.2 using historical data. If, however, the original data are

separated into two ranges by economic expansion or recession, the beta might be 1.0 in

expansions and 1.4 in recessions. Going forward, the analyst’s estimate of the firm’s beta

should reflect whether an expansion is expected (i.e., the expected beta is 1.0) or a

recession is expected (i.e., the expected beta is 1.4). The beta used should be the beta

consistent with the analyst’s expectations for economic conditions.

7. Another problem in forming capital market expectations is the misinterpretation of

correlations (i.e., causality). Suppose the analyst finds that corn prices were correlated with

rainfall in the Midwestern United States during the previous quarter. It would be reasonable

to conclude that rainfall influences corn prices. It would not be reasonable to conclude that

corn prices influence rainfall, although the correlation statistic would not tell us that.

Rainfall is an exogenous variable (i.e., it arises outside the model), whereas the price of

corn is an endogenous variable (i.e., it arises within the model).

It is also possible that a third variable influences both variables. Or it is possible that there is

a nonlinear relationship between the two variables that is missed by the correlation

statistic, which measures linear relationships.

These scenarios illustrate the problem with the simple correlation statistic. An alternative to

correlation for uncovering predictive relationships is a multiple regression. In a multiple

regression, lagged terms, control variables, and nonlinear terms can all be included as

independent variables to better specify the relationship. Controlling for other effects, the

regression coefficient on the variable of interest is referred to as the partial correlation and

would be used for the desired analysis.

8. Analysts are also susceptible to psychological traps:

In the anchoring trap, the first information received is overweighted. If during a

debate on the future of the economy, the first speaker forecasts a recession, that

forecast is given greater credence.

In the status quo trap, predictions are highly influenced by the recent past. If

inflation is currently 4%, that becomes the forecast, rather than choosing to be

different and potentially making an active error of commission.

In the confirming evidence trap, only information supporting the existing belief is

considered, and such evidence may be actively sought while other evidence is

ignored. To counter these tendencies, analysts should give all evidence equal

scrutiny, seek out opposing opinions, and be forthcoming in their motives.

In the overconfidence trap, past mistakes are ignored, the lack of comments from

others is taken as agreement, and the accuracy of forecasts is overestimated. To

counter this trap, consider a range of potential outcomes.

In the prudence trap, forecasts are overly conservative to avoid the regret from

making extreme forecasts that could end up being incorrect. To counter this trap,

consider a range of potential outcomes.

In the recallability trap, what is easiest to remember (often an extreme event) is

overweighted. Many believe that the U.S. stock market crash of 1929 may have

depressed equity values in the subsequent 30 years. To counter this trap, base

predictions on objective data rather than emotions or recollections of the past.

Professor’s Note: Nothing to dwell on here. Just one more discussion of

behavioral biases.

9. Model and input uncertainty. Model uncertainty refers to selecting the correct model. An

analyst may be unsure whether to use a discounted cash flow (DCF) model or a relative

value model to evaluate expected stock return. Input uncertainty refers to knowing the

correct input values for the model. For example, even if the analyst knew that the DCF

model was appropriate, the correct growth and discount rates are still needed.

Tests of market efficiency usually depend on the use of a model. For example, many

researchers use the market model and beta as the relevant measure of risk. If beta is not

the correct measure of risk, then the conclusions regarding market efficiency will be

invalid. Some believe that market anomalies, which have been explained by behavioral

finance, are in fact due to the actions of investors who are rational but use different

valuation models (which include the human limitations of cognitive errors and emotional

biases).

FORECASTING TOOLS

LOS 15.c: 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.

The use of formal tools helps the analyst set capital market expectations. Formal tools are those that

are accepted within the investment community. When applied to reputable data, formal tools

provide forecasts replicable by other analysts. The formal tools we examine are statistical tools,

discounted cash flow models, the risk premium approach, and financial equilibrium models.

Statistical Tools

Descriptive statistics summarize data. Inferential statistics use the data to make forecasts. If the

past data is stationary, the parameters driving the past and the future are unchanged. Therefore,

the historical estimates are reasonable estimates of the future.

Return estimates can be based on the arithmetic or geometric average of past returns.

To estimate the return in a single period, the arithmetic average is used. For example, if a portfolio

has a 50/50 chance of making or losing 10% in any given period, there is an equal chance $100 will

increase to $110 or decrease to $90. Thus, on average, the portfolio is unchanged at $100 for a 0%

return, the arithmetic average of the + and –10% returns.

Over multiple periods, the geometric average is generally preferred. Unannualized, the geometric

return of the portfolio is (1.10)(0.90) – 1 = –1.0%. This reflects the most likely value of the portfolio

over two periods, as the $100 could either increase 10% to $110 and then decline 10% to $99, or

decrease 10% to $90 and then increase 10% to $99. Under either path, the most likely change is –1%.

Another approach is to use the historical equity risk premium plus a current bond yield to estimate

the expected return on equities.

Alternatively, a shrinkage estimate can be applied to the historical estimate if the analyst believes

simple historical results do not fully reflect expected future conditions. A shrinkage estimate is a

weighted average estimate based on history and some other projection.

For example, suppose the historical covariance between two assets is 180 and the analyst has used a

model to project covariances and develop a target covariance matrix). If the model estimated

covariance is 220 and the analyst weights the historical covariance by 60% and the target by 40%, the

shrinkage estimate would be 196 (= 180 × 0.60 + 220 × 0.40). If conditions are changing and the

model and weights are well chosen, the shrinkage estimate covariances are likely to be more

accurate.

Time series models are also used to make estimates. A time series model assumes the past value of

a variable is, at least in part, a valid estimator of its future value. Time series models are frequently

used to make estimates of near term volatility. Volatility clustering has been observed where either

high or low volatility tends to persist, at least in the short run. A model developed by JP Morgan

states that variance in the next period (σ t2) is a weighted average of the previous period variance

and the square of the residual error. The two weights sum to 1.0 and can be denoted as β and 1 – β.

Professor’s Note: Some authors use θ rather than β to denote the weights. β is a generic symbol used to

denote weight or exposure to a factor.

For example, suppose β is 0.80 and the standard deviation in returns is 15% in period t − 1. If the

random error is 0.04, then the forecasted variance for period t is:

The forecasted standard deviation of 13.54% is close to the historical standard deviation of 15%

because the historical standard deviation is weighted so heavily.

Multifactor models can be used in a top down analysis to forecast returns based on sensitivities (β)

and risk factors (F). A two-factor model would take the form:

Ri = αi + βi,1F1 + βi,2F2 + εi

In this two-factor model, returns for an asset i, Ri, are a function of factor sensitivities, β, and factors,

F. A random error, εi, has a mean of zero and is uncorrelated with the factors.

A rigorous approach can be used to work through a sequence of analysis levels and a consistent set of

data to calculate expected return, covariance, and variance across markets. For example, Level 1

may consider the factors which affect broad markets, such as global equity and bond. Level 2 then

proceeds to more specific markets, such as market i, j, k, l. In turn, further levels of analysis can be

conducted on sectors within each market (for example, within market l).

The advantages of this approach include the following:

Returns, covariances, and variances are all derived from the same set of driving risk factors

(betas).

A set of well-chosen, consistent factors reduces the chance for random variation in the

estimates.

Such models allow for testing the consistency of the covariance matrix.

The choice of factors to consider and levels of analysis is up to the analyst.

Professor’s Note: The following example illustrates this analysis method. This type of hard core statistical

calculation is not common on the exam. The CFA® text has one similar example but no end of chapter

questions on the topic.

In this reading you will see “inconsistencies” of scale. Do not let them throw you off. The key issue within any

one question is to be consistent using only whole numbers or decimal versions for standard deviation,

covariance, and variance.

For example, in shrinkage estimators, covariance is presented as the whole number 220. It can also be shown

as 0.0220. In the time series discussion, standard deviation was expressed as the decimal 0.15 (for 15%). In

the following example and in the corresponding CFA example, decimals are used with 0.0211 for variance

and 0.0015 for covariance. It is up to you to know the material well enough to interpret the scale of the data

in a given question. For example, 15% standard deviation and its variance can be expressed as 15 and 225 in

whole numbers or as 0.15 and 0.0225 in decimal numbers.

Example: Two-Level Factor Analysis

Thom Jones is a senior strategist examining equity and bond markets in countries C and D. He assigns the quantitative

group to prepare a series of consistent calculations for the two markets. The group begins at Level 1 by assuming

there are two factors driving the returns for all assets—a global equity factor and a global bond factor. At Level 2, this

data is used to analyze each market. The data used is shown in Figure 1 and Figure 2:

Figure 1: Factor Covariance Matrix for Global Assets

Global Equity Factor Global Bond Factor

Global equity factor

0.0211 = σF12

0.0015 = cov(F1,F2)

Global bond factor

0.0015 = cov(F1,F2)

0.0019 = σF22

Figure 2: Factor Sensitivities for Countries

Country

Global Equity

Global Fixed Income

C

0.90 = βC1

0.00 = βC2

D

0.80 = βD1

0.00 = βD2

The 0.00 sensitivities to global fixed income in country markets C and D indicate both markets are equity markets.

(Note that this does not mean the pairwise correlation between each market and the global bond market is zero. It

means that, once the effect of the equity market is controlled for, the partial correlation of each market and the global

bond factor is zero.)

Estimate the covariance between markets C and D:

Cov(C,D) = (0.90)(0.80)(0.0211) + (0)(0)(0.0019) + [(0.90)(0) +

(0.00)(0.80)]0.0015 = 0.0152

Estimate the variance for market C:

(0.90)2(0.0211) + (0.00)2(0.0019) + 2(0.90)(0.00)(0.0015) = 0.0171

For market D, this is:

(0.80)2(0.0211) + (0.00)2(0.0019) + 2(0.80)(0.00)(0.0015) = 0.0135

Note that the variance of the markets will be higher than estimated because the analysis has not accounted for the

variance of residual risk (σ2ε ). Each market will have residual or idiosyncratic risk not explained by that market’s factor

sensitivities.

Discounted Cash Flow Models

A second tool for setting capital market expectations is discounted cash flow models. These models

say that the intrinsic value of an asset is the present value of future cash flows. The advantage of

these models is their correct emphasis on the future cash flows of an asset and the ability to back out

a required return. Their disadvantage is that they do not account for current market conditions such

as supply and demand, so these models are viewed as being more suitable for long-term valuation.

Applied to equity markets, the most common application of discounted cash flow models is the

Gordon growth model or constant growth model. It is most commonly used to back out the expected

return on equity, resulting in the following:

This formulation can be applied to entire markets as well. In this case, the growth rate is proxied by

the nominal growth in GDP, which is the sum of the real growth rate in GDP plus the rate of inflation.

The growth rate can be adjusted for any differences between the economy’s growth rate and that of

the equity index. This adjustment is referred to as the excess corporate growth rate. For example, the

analyst may project the U.S. real growth in GDP at 2%. If the analyst thinks that the constituents of

the Wilshire 5000 index will grow at a rate 1% faster than the economy as a whole, the projected

growth for the Wilshire 5000 would be 3%.

Grinold and Kroner (2002) 1 take this model one step further by including a variable that adjusts for

stock repurchases and changes in market valuations as represented by the price-earnings (P/E) ratio.

The model states that the expected return on a stock is its dividend yield plus the inflation rate plus

the real earnings growth rate minus the change in stock outstanding plus changes in the P/E ratio:

The variables of the Grinold-Kroner model can be grouped into three components: the expected

income return, the expected nominal growth in earnings, and the expected repricing return.

1. The expected income return is the cash flow yield for that market:

D1 / P0 is current yield as seen in the constant growth dividend discount model. It is the

expected dividend expressed as a percentage of the current price. The Grinold-Kroner

model goes a step further in expressing the expected current yield by considering any

repurchases or new issues of stock.

Professor’s Note: To keep the ΔS analysis straight, just remember net stock:

Repurchase increases cash flow to investors and increases expected return.

Issuance decreases cash flow to investors and decreases expected return.

The long way around to reaching these conclusions is:

Repurchase is a reduction in shares outstanding, and – ΔS, when subtracted

in GK, is –(–ΔS), which becomes + ΔS and an addition to expected return.

Issuance is an increase in shares outstanding, and + ΔS, when subtracted in

GK, becomes –ΔS and a reduction in expected return.

2. The expected nominal earnings growth is the real growth in the stock price plus expected

inflation (think of a nominal interest rate that includes the real rate plus inflation):

expected nominal earnings growth = (i + g)

3. The repricing return is captured by the expected change in the P/E ratio:

It is helpful to view the Grinold-Kroner model as the sum of the expected income return, the

expected nominal growth, and the expected repricing return.

Suppose an analyst estimates a 2.1% dividend yield, real earnings growth of 4.0%, long-term inflation

of 3.1%, a repurchase yield of –0.5%, and P/E re-pricing of 0.3%:

expected current yield (income return) = dividend yield + repurchase yield

= 2.1% – 0.5% = 1.6%

expected capital gains yield = real growth + inflation + re-pricing

= 4.0% + 3.1% + 0.3% = 7.4%

The total expected return on the stock market is 1.6% + 7.4% = 9.0%.

Estimating Fixed Income Returns

Discounted cash flow analysis of fixed income securities supports the use of YTM as an estimate of

expected return. YTM is an IRR calculation and, like any IRR calculation, it will be the realized return

earned if the cash flows are reinvested at the YTM and the bond is held to maturity. For zero-coupon

bonds, there are no cash flows to reinvest, though the held-to-maturity assumption still applies.

Alternatively, the analyst can make other reinvestment and holding period assumptions to project

expected return.

Risk Premium Approach

An alternative to estimating expected return using YTM is a risk premium or buildup model. Risk

premium approaches can be used for both fixed income and equity. The approach starts with a lower

risk yield and then adds compensation for risks. A typical fixed income buildup might calculate

expected return as:

RB = real risk-free rate + inflation risk premium + default risk premium + illiquidity risk premium + maturity risk premium +

tax premium

The inflation premium compensates for a loss in purchasing power over time.

The default risk premium compensates for possible non-payment.

The illiquidity premium compensates for holding illiquid bonds.

The maturity risk premium compensates for the greater price volatility of longer-term

bonds.

The tax premium accounts for different tax treatments of some bonds.

To calculate an expected equity return, an equity risk premium would be added to the bond yield.

Professor’s Note: Equity buildup models vary in the starting point.

Begin with rf. The Security Market Line starts with rf and can be considered a variation of this

approach.

Other models start with a long-term default free bond.

Or the corporate bond yield of the issuer.

The point is to use the data provided.

Financial Equilibrium Models

The financial equilibrium approach assumes that supply and demand in global asset markets are in

balance. In turn, financial models will value securities correctly. One such model is the International

Capital Asset Pricing Model (ICAPM). The Singer and Terhaar approach begins with the ICAPM.

The equation for the ICAPM is:

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