Tải bản đầy đủ

CFA 2017 level 3 schweser notes book 3

Table of Contents

Getting Started Flyer
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


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


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


LOS 20.d
LOS 20.e
Scenario Analysis
LOS 20.f
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


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


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
Pages List Book Version

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

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.

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)

Reading Assignments
Asset Allocation and Related Decisions in Portfolio Management (1), CFA Program 2017 Curriculum,
Volume 3, Level III
17. Asset Allocation (page 84)

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)

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)

Reading Assignments

Fixed-Income Portfolio Management (2), CFA Program 2017 Curriculum, Volume 4, Level III
22. Fixed-Income Portfolio Management—Part II (page 259)

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.

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
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)


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
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)

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)

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)

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
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.

Study Session 7

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.

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
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.

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
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
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
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

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

Global Equity

Global Fixed Income


0.90 = βC1

0.00 = βC2


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

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
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
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:

Tài liệu bạn tìm kiếm đã sẵn sàng tải về

Tải bản đầy đủ ngay