CFA 2018 level 3 schweser practice exam CFA 2018 level 3 question bank CFA 2018 CFA 2018 r14 capital market expectations IFT notes

Tools for Formulating Capital Market Expectations In order to produce these estimates, an analyst can use:  Formal research tools see Section 3.1  Survey and panel methods see Section

Capital Market Expectations

1 Introduction 3

2 Organizing the Task: Framework and Challenges 3

2.1 A Framework for Developing Capital Market Expectations 3

2.2 Challenges in Forecasting 4

3 Tools for Formulating Capital Market Expectations 8

3.1 Formal Tools 8

3.2 Survey and Panel Methods 15

3.3 Judgment 16

4 Economic Analysis 16

4.1 Business Cycle Analysis 16

4.2 Economic Growth Trends 22

4.3 Exogenous Shocks 23

4.4 International Interactions 23

4.5 Economic Forecasting 25

4.6 Using Economic Information in Forecasting Asset Class Returns 26

4.7 Information Sources for Economic Data and Forecasts 28

Summary 29

Examples from the Curriculum 39

Example 1 Capital Market Expectations Setting: Information Requirements (1) 39

Example 2 Capital Market Expectations Setting: Information Requirements (2) 40

Example 3 Historical Analysis 41

Example 4 Incorporating Economic Analysis into Expected Return Estimates 41

Example 5 Inconsistency of Correlation Estimates: An Illustration 42

Example 6 A Change in Focus from GNP to GDP 42

Example 7 Smoothed Data: The Case of Alternative Investments (1) 42

Example 8 Smoothed Data: The Case of Alternative Investments (2) 44

Example 9 Using Regression Analysis to Identify a Change in Regime 44

Example 10 Causality Relationships 45

Example 11 Traps in Forecasting 45

Example 12 Adjusting a Historical Covariance 45

Example 13 The Grinold–Kroner Forecast of the US Equity Risk Premium 46

Example 14 Forecasting the Return on Equities Using the Grinold–Kroner Model 47

Example 15 The Long-Term Real Risk-Free Rate 47

Example 16 The Real Interest Rate and Inflation Premium in Equilibrium 48

Example 17 The Risk Premium: Some Facts 48

Example 18 Justifying Capital Market Forecasts 48

Example 19 Setting CME Using the Singer–Terhaar Approach 50

Example 20 Short-Term Consumer Spending in the United Kingdom 53

Example 21 Judgment Applied to Correlation Estimation 53

Example 22 The Yield Curve, Recessions, and Bond Maturity 54

Example 23 Inflation, Disinflation, and Deflation 54

Example 24 An Inflation Forecast for Germany 55

Example 25 The 1980–1982, 2001, and 2008–09 US Recessions 56

Example 26 A Taylor Rule Calculation 57

Example 27 Monetary Policy in the Eurozone Compared with the United States in 2001 and 2010 57

Example 28 Cycles and Trends: An Example 58

Example 29 Forecasting GDP Trend Growth 58

Example 30 An Analyst’s Forecasts 59

Example 31 Central Bank Watching and Short-Term Interest Rate Expectations 60

Example 32 Economic Return Drivers: Energy and Transportation 60

Example 33 Researching US Equity Prospects for a Client 61

Example 34 Modifying Historical Capital Market Expectations 63

Example 35 A Currency Example 65

Example 36 The USD/Euro Exchange Rate, 1999–2004 65

This document should be read in conjunction with the corresponding reading in the 2018 Level III CFA®

Program curriculum Some of the graphs, charts, tables, examples, and figures are copyright

2017, CFA Institute Reproduced and republished with permission from CFA Institute All rights reserved

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1 Introduction

In this reading we will discuss capital market expectations i.e the investor’s expectations about the risk

and return prospects of asset classes Capital market expectations are a key input in creating a strategic

asset allocation An important point to note here is that capital market expectations are macro

expectations By contrast, expectations about individual assets are micro expectations Macro

expectations help us in strategic asset allocation, whereas, micro expectations help us in security

selection and valuation This reading focuses on macro expectations, we will discuss micro expectations

in a later reading

The major sections in this reading are:

 Framework and challenges

 Tools for formulating capital market expectations

 Economic analysis

2 Organizing the Task: Framework and Challenges

2.1 A Framework for Developing Capital Market Expectations

This section addresses LO.a:

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

management process

As discussed, capital market expectations are used to determine an investor’s strategic asset allocation

They are an important part of the portfolio management process

To formulate capital market expectations, the following framework should be used:

Step 1: Specify the final set of expectations that are needed, including the time horizon to which they

apply An analyst needs to understand the scope of the analysis, set boundaries and only focus only on

what is relevant An investment policy statement can guide you in this task The analyst should write

down the questions which need to be answered Examples 1 and 2 in the curriculum contrast the

expectation settings for two managers Manger 1’s information requirement relates to US equity and

fixed income markets only By contrast, Manger 2’s information requirement relates not only to US and

non-US equity and fixed-income markets, but also to three alternative investment types

Refer to Example 1 from the curriculum

Refer to Example 2 from the curriculum

Step 2: Research the historical record Analyzing historical return data can be a useful starting point

when forecasting returns However, beyond simply providing average returns over a certain time

horizon, historical data should be analyzed to determine the factors which drive returns As noted in

Example 3, forecasters who make predictions without regard to past experience have no benchmarks to

distinguish between what is new about their expectations and what may be a continuation of past

experience If your forecast contradicts the historical trend, you need to supply supporting analysis for

your forecast

Refer to Example 3 from the curriculum

Step 3: Specify the method(s) and/or model(s) that will be used and their information requirements

Section 3 discusses several methods that can be used to set capital market expectations, some of which

may be more appropriate than others in different circumstances You should consider the time horizon

when selecting appropriate model For example, if the time horizon is long, a discounted cash flow

model can be used

Step 4: Determine the best sources for information needs Using relevant and accurate data is critical

to the process of setting capital market expectations You need to consider the quality of data, the cost

involved and the frequency of data (for example, daily data, monthly data etc.) Exhibit 33 provides a list

of useful data sources

Step 5: Interpret the current investment environment using the selected data and methods, applying

experience and judgment For example, if you believe that the economy is headed towards a recession,

then you need to factor this in your expectations You cannot say that the historically high returns on

equities will continue in the current investment environment

Step 6: Provide the set of expectations that are needed, documenting conclusions Having analyzed

and interpreted the data, in this step you actually document your expectations You basically answer the

questions that were formulated in Step 1 A good forecast should be:

 Unbiased, objective and well researched

 Efficient i.e you need to minimize forecast errors

 Internally consistent, you should not make contradicting predictions For e.g you cannot predict

that economy will go in a recession and equities will continue giving high returns

Step 7: Monitor actual outcomes and compare them to expectations, providing feedback to improve

the expectations-setting process This purpose of this step is to continually refine and improve the

forecasting process

Refer to Example 4 from the curriculum

Refer to Example 5 from the curriculum

2.2 Challenges in Forecasting

This section addresses LO.b:

LO.b: Discuss challenges in developing capital market forecasts

Nine challenges encountered in developing capital market forecasts are:

2.2.1 Limitations of Economic Data

Three basic issues to consider for any economic data are:

 Timeliness: Making accurate forecasts requires access to timely data For example, the US

Bureau of Labor Statistics releases monthly non-farm payroll data on the first Friday of the

following month By contrast, data measures for smaller, less developed economies may take

months to collect, process, and disseminate Older, stale data is less useful when developing

capital market expectations

 Accuracy: In addition to being timely, data must also be accurate Data that requires significant

revisions after its initial publication is less reliable and therefore less useful for the purpose of

forecasting capital market expectations

 Definitions and calculation methods: Statistics agencies often make changes to their methods of

collecting and processing economic data Analysts must be aware of the effect of the changes

and whether data produced using the new methods is consistent with data produced using the

old methods For example, many years ago GNP was popularly used but in the last few decades

GDP has become the standard for measuring an economy

Refer to Example 6 from the curriculum

2.2.2 Data Measurement Errors and Biases

The three major issues are:

 Transcription errors: Transcription errors, which are “errors in gathering and recording data”,

can be as simple as data entry errors For example, the number “52” may have been entered

when the correct number is 25 Ideally, data providers will have processes to reduce or

eliminate transcription errors

 Survivorship bias: For example, if you are looking at the returns of a hedge fund index, then you

need to be aware of the fact that only hedge funds that performed well and survived have

reported their performance Hedge funds that did not perform well and did not survive, have

not reported their performance So survivorship bias causes:

o An upward bias for reported returns

o Overly-optimistic expectations of future returns

 Appraisal (smoothed) data: The prices of assets such as real estate, which do not trade in liquid

market, are based on periodic appraisals If appraisals are done, for example, each month, the

daily prices may be interpolated As shown in Example 7, the periodic snapshots from appraisals

smooth out the true volatility of returns The consequences of smoothed data are:

o A reported standard deviation of returns that is below the true standard deviation

o A downward bias for reported correlations with other assets

Refer to Example 7 from the curriculum

Refer to Example 8 from the curriculum

2.2.3 The Limitations of Historical Estimates

Regime change: A key question when using historical data is determining the appropriate time period to

analyze If we are certain that the same return drivers observed in historical data will continue to drive

future returns, we can include data going back as long as these return drivers are relevant (i.e., the data

is “stationary”) However, events such as regime changes often cause return drivers to change, which

results in nonstationary data Effectively, the data predating a change is representative of one regime

and the data from the subsequent period represents a different regime Extending the length of the

historical period being analyzed increases the risk of including data from multiple regimes Only

historical data from a regime that is fully representative of current and expected market conditions

should be used

To overcome the problem, and to identify if a regime change has occurred, we use regression analysis

with dummy variables

Refer to Example 9 from the curriculum

2.2.4 Ex Post Risk Can Be a Biased Measure of Ex Ante Risk

Ex ante risk is the risk that you are anticipating In contrast, ex post risk is the risk that is based on the

data that you have seen in the past Often, analysts are influenced by the historical value of risk while

making future estimates of risk

2.2.5 Biases in Analysts’ Methods

Commonly observed biases are:

 Data mining bias: If an analyst takes the same set of data and keeps running computer

simulations till he finds some patterns This pattern may not have an economic justification This

is an example of data-mining bias To overcome this problem, the best forecasting models limit

the variables used to those that have an “explicit economic rationale”

 Time period bias: Analysts can demonstrate time-period bias by basing their forecasts on time

periods were things were a little different For e.g small cap stocks usually outperform large cap

stocks But if you looked at data only from the 1975-1983 time period, you would conclude that

large cap stocks outperform small cap stocks

2.2.6 The Failure to Account for Conditioning Information

Capital market expectations depend heavily on the assumptions analysts make regarding market

conditions For example, Exhibit 7 (below) from Asset Allocation, Section 4.2.3 shows that correlations

between developed market and emerging market indexes tend to spike during periods of economic

crisis A forecast of return correlations that is based on the assumption of normal market conditions is of

no relevance during periods of economic crisis

2.2.7 Misinterpretation of Correlations

Correlation is not (necessarily) causation As noted in the curriculum, there are at least three possible

explanations for a high correlation between variable A and variable B:

1 A predicts B

2 B predicts A

3 A third variable C predicts both A and B

After observing a high correlation between two variables, you need to correctly predict where that high

correlation is coming from

Refer to Example 10 from the curriculum

2.2.8 Psychological Traps

Biases that should be considered with respect to forecasting capital market expectations are discussed

below:

 Anchoring trap: This refers to the tendency of investors to focus on a specific purchase price or

price target In the context of capital market expectations, an analyst may become anchored on

the first information he receives and fail to adequately incorporate new information that

suggests the first information is no longer accurate

 Status quo trap: The status quo trap is the tendency to set capital market expectations based on

the assumption that current economic trends will continue In Example 11, Philip Lasky expects

the recent market downturn to continue despite the fact that his portfolio has generated

positive risk-adjusted returns over the past three years

Refer to Example 11 from the curriculum

 Confirming evidence trap: The confirming evidence trap, is the tendency to give greater weight

to information that supports one’s preexisting beliefs In Example 11, Philip Lasky has read

numerous estimates of the Canadian equity risk premium, but repeatedly refers to the most

pessimistic of those in his conversation with Cynthia Casey

 Overconfidence trap: When setting capital market expectations, analysts often rely on models,

which can lead them to provide overly-precise forecasts and refuse to consider the possibility

that outcomes may fall outside a narrow range

 Prudence trap: Analysts may moderate their capital market expectations in order to avoid

appearing extreme and out-of-line with the market consensus In Example 11, Cynthia Casey

revises her initial estimate of economic growth downward after perceiving that many of her

clients consider this view to be overly-optimistic

 Recallability trap: When setting capital market expectations, analysts can be unduly influenced

by memories of past events, which result in skewed forecasts For example, a manager with

strong memories of failing to profit from a bull market may produce overly-optimistic forecasts

2.2.9 Model Uncertainty

The uncertainty surrounding which model can be used to generate accurate forecasts is called model

uncertainty

By contrast, an analyst may be certain about which model to use, but uncertain about the quality of the

input data This second form of uncertainty is called input uncertainty

3 Tools for Formulating Capital Market Expectations

In order to produce these estimates, an analyst can use:

 Formal research tools (see Section 3.1)

 Survey and panel methods (see Section 3.2)

 Judgment based on their past experiences (see Section 3.3)

3.1 Formal Tools

This section addresses LO.c:

LO.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

Compared to the methods discussed in sections 3.2 and 3.3, formal research tools are empirically-based

methods designed to produce precise forecasts of variables such as the expected return for a given asset

class over a specific period Formal quantitative tools are used extensively throughout the investment

sector, because they allow analysts to document the use of an objective forecasting process

The tools discussed in this section are:

 Statistical methods (3.1.1)

 Discounted cash flow (DCF) models (3.1.2)

 Risk premium approach (3.1.3)

 Financial market equilibrium (3.1.4)

Note that Example 4 (in Section 2.1) provides a brief discussion of each of these tools

3.1.1 Statistical Methods

When studying the statistical methods that can be used to develop capital market expectations, it is

helpful to remember the end product of this process Exhibit 10 from Example 18 (Section 3.1.4) is

shown below as a reminder

Historical Statistical Approach: Sample Estimators: The “simplest approach” to generate the returns,

standard deviations, and correlations that appear in Exhibit 10 would be to use their historical averages

For example, as shown in Exhibit 3, the arithmetic mean annual return for US equities was 8.3 percent

with a standard deviation of 20.3 percent between 1900 and 2010 If the analyst believes that the

factors that drive US equity returns were constant during this period and are representative of current

and expected market conditions, she may use on these historical averages

Shrinkage Estimators: Rather than relying exclusively on historical data, an analyst may use the

shrinkage estimation method, which produces a forecast that is a weighted average of historical data

and data generated using another forecasting method In Example 12, Cynthia Casey gives a 0.3

weighting the covariance figure derived from historical data and a 0.7 weighting to the covariance figure

generated using a factor model approach

Refer to Example 12 from the curriculum

Time-Series Estimators: Time-series models estimate a variable’s future value based on its past (lagged)

values (and possibly lagged values of other variables) The relevant lagged values are plugged into a

regression formula, which produces a forecast of the dependent variable For example, volatility in the

current period can be stated as the weighted average of the previous period volatility and a random

error term

Multifactor Models: Multi-factor models use regression analysis of historical data to identify return

drivers, which are used as independent variables in a regression formula that produces a forecast of the

dependent variable They are useful for the following reasons:

 Returns on all assets are related to a common set of return divers

 Filters out noise (when factors are well chosen)

 Makes it easy to verify the consistency of the covariance matrix

Exhibit 4 assumes that the two factors Global Equity Factor and Global Bonds Factor drive the returns of

all assets Given a standard deviation of 14% for equities and 4% for bonds and a correlation of 0.3

among the two factors, we can create the following factor covariance matrix

Factor Covariance Matrix

To derive the asset covariance market we need to know how a market responds to factor movements

Refer to Exhibit 5 which shows the hypothetical statistics for five markets The numbers are derived

through statistical methods such as regression of historical data

Global Equity (F1) Global Bonds (F2)

In the above example, if Market A moves by 110 points in response to 100 point move of global equities,

then the factor sensitivity of Market A to Factor 1 (Global Equity) is 1.1

We can say that Market A is a pure equity market since the factor sensitivity to global bonds is 0

The covariance between Markets A and B can be calculated using the following formula:

Mij=bi1bj1Var(F1)+bi2bj2Var(F2)+(bi1bj2+bi2bj1)Cov(F1,F2)

(For i=1 to 5, j = 1 to 5, and i≠j)

M12 = (1.1)(1.05)(0.0196) + (0)(0)(0.0016) + [(1.10)(0) + (0)(1.05)](0.0017) = 0.0226

3.1.2 Discounted Cash Flow Models

Discounted cash flow (DCF) models provide an expected return (or fair price) based on cash flows and

expected growth Because they are forward-looking, DCF models are especially useful in the process of

setting long-term capital market expectations in stable, developed markets They are much less

appropriate for short-term forecasts DCF models can be applied to equity markets as well as

fixed-income markets

Equity Markets

Gordon Growth Model: The best-known DCF model is the Gordon (constant) growth model, which

appears below:

D0 = Dividend (current period)

D1 = Dividend (next period)

E(Re) = Expected return

P0 = Market value or price

g = Growth rate

A critical component of the Gordon growth model is the growth rate for corporate earnings and

dividends (g), which can be estimated using the nominal GDP growth rate for the overall economy

However, market indexes are not perfectly representative of the overall economy and it may be

necessary to adjust the nominal GDP growth rate by adding (or subtracting) an estimate of excess

corporate growth An index that is more representative of the overall economy will require a smaller

adjustment compared to a less representative index

Grinold-Kroner model: Like the Gordon growth model, the Grinold-Kroner model (shown below) can also

be used to calculate the expected return for a market index

𝐸(𝑅𝑒) ≈𝐷1

𝑃 − ∆𝑆 + 𝑖 + 𝑔 + ∆𝑃𝐸

The components of the Grinold-Kroner model are:

 Income Return = D1/P – ΔS

 Nominal Earnings Growth Return = i + g

 Re-pricing Return = ΔPE

Important points to note are:

 Income return: D1/P is the same dividend yield that was used in the Gordon growth model

However, because many companies have chosen to distribute cash to shareholders in the form

of share repurchases (as opposed to dividends), the Grinold-Kroner model includes ΔS, which is

called the repurchase yield and is expressed as the percentage change in the number of shares

outstanding, as an adjustment for share repurchases An increase in the number of shares

outstanding is a negative repurchase yield and lowers the income return By contrast, a

decrease in the number of shares outstanding means that investors are getting money and

results in a higher income return

 Nominal earnings growth: This includes both estimated real long-term earnings growth (g) and a

long-term inflation forecast (i)

 Repricing return: This component of expected return is simply the expected percentage change

in price/earnings ratio (ΔPE)

Refer to Example 13 from the curriculum

Refer to Example 14 from the curriculum

Fixed-Income Markets

Discounting future cash flows is as “standard tool” for determining the value of fixed-income securities

The rate at which future cash flows are discounted is referred to as the yield to maturity (YTM)

However, using a DCF model to valued fixed-income securities is based on the (often unrealistic)

assumption that cash flows received in the form of coupon payments can be reinvested at the YTM

3.1.3 The Risk Premium Approach

Future cash flows that can be predicted with complete certainty can be discounted at the risk-free rate

However, if cash flows are subject to risks such as the credit risk associated with corporate bonds,

investors will apply a higher discount rate and valuations will be lower The risk premium approach

combines (or builds up) various risk premiums into a single discount rate that is used to value future

cash flows

A General Expression

The single discount rate produced by the risk premium approach is referred to as the expected return

for an asset – that is to say, investors expect this return as compensation for the various sources of risk

that they will be exposed to As shown in the exhibit below, risk premiums of 2.5 percent and 1.5

percent are added to the risk-free rate of 4.5 percent in order to arrive at an overall discount rate of 8.5

percent

Fixed-Income Premiums

For fixed-income securities, the base discount rate is the nominal risk-free rate, which is the sum of the

real risk-free rate and a premium for expected inflation In practice, the YTM on a government bond,

such as the 10-year US Treasury, is used as a proxy for the nominal risk-free rate

For corporate bonds, investors will demand a default risk premium, which increases the discount rate

Additional premiums may be applied to compensate for illiquidity, longer maturity, and even taxes

Refer to Example 15 from the curriculum

Refer to Example 16 from the curriculum

Refer to Example 17 from the curriculum

[SERIES NAME]

[VALUE]

Overall discount rate [VALUE]

The Equity Risk Premium

Investors always have the option of simply investing in the risk-free asset Therefore, risky assets must

offer higher expected returns as compensation for their higher level of risk For equities, this systematic

risk is captured by the equity risk premium, which is the difference between expected returns for

equities and the risk-free rate As shown in Exhibit 9, the average equity risk premium observed in 17

developed economies over the period of 1900 to 2010 was 5 percent

Assuming a nominal risk-free rate of 4.5 percent (as represented by the YTM on a long-term government

bond) and an equity risk premium of 5 percent, the discount rate applied to equities would be 9.5

percent This method of calculating an equity market discount rate is also known as the

bond-yield-plus-risk-premium method

3.1.4 Financial Market Equilibrium Models

Financial equilibrium models are used to value asset classes during period in which the supply and

demand are in balance (i.e., the market is at equilibrium) The most well-known financial market

equilibrium model is the international capital asset pricing model (ICAPM), which is the basis for the

Singer-Terhaar approach to forecasting capital market expectations

The Singer-Terhaar model is used to calculate a risk premium for an asset class (e.g., Latin American real

estate or European equities) based on the sensitivity of its returns relative to those of the global

investable market (GIM), which is discussed further below Formally, this is calculated using Equation 10:

𝑅𝑃𝑖 = 𝜎𝑖𝜌𝑖,𝑀(𝑅𝑃𝑀

𝜎𝑀) where,

RPi is the risk premium for a given asset class i

σi is the standard deviation of returns for asset class i

ρi,M is the correlation between returns for asset class i and returns for the global investable market (GIM)

RPM is the risk premium for the GIM (expected returns above the risk-free rate)

σM is the standard deviation of returns for the GIM

Note that (RPM/ σM) is the GIM Sharpe ratio

Global Investable Market (GIM):

The global investable market (GIM) is defined as “a practical proxy for the world market portfolio

consisting of traditional and alternative asset classes with sufficient capacity to absorb meaningful

investment.” The Singer-Terhaar model calculates the expected return for an asset class based on the

sensitivity of its returns with the GIM For exam purposes, the key piece of information you will need is

the GIM Sharpe ratio, which is typically estimated at approximately 0.30 You may simply be given a GIM

Sharpe ratio of, for example, 0.32 Alternatively, you may be required to calculate this figure using the

GIM risk premium (the expected return for the GIM minus the investor’s domestic risk-free rate) and the

standard deviation of the GIM’s returns (σM)

Market Integration:

As mentioned, the Singer-Terhaar model is used to calculate a risk premium for an asset class If all

investors from all markets could easily invest in the asset class, it be perfectly integrated In the case of

perfect integration, the risk premium could be calculated using Equation 10 The curriculum gives the

example of Canadian equities, which have a standard deviation of 17% and a 0.70 correlation with the

GIM Using a GIM Sharpe ratio of 0.28, the risk premium for Canadian equities is:

17% x 0.70 x 0.28 = 3.33%

However, asset classes are rarely (if ever) fully-integrated with the GIM Therefore, the Singer-Terhaar

model produces a risk premium that is a weighted average of two numbers:

1 The risk premium assuming full integration with the GIM

2 The risk premium assuming no integration with (fully segmented from) the GIM

In both cases, the risk premium is calculated using Equation 10 When full integration with the GIM is

assumed, ρi,M is simply the correlation between the asset class and the GIM In the curriculum’s example

of Canadian equities, ρi,M is 0.70 and, as shown above, generates a risk premium of 3.33%

When full segmentation is assumed, ρi,M becomes 1.0 because the asset class is assumed to be perfectly

correlated with itself Equation 10 is still used, but ρi,M can be dropped However, it may be helpful to be

consistent and simply use a different number for ρi,M In the curriculum’s example of Canadian equities,

the risk premium assuming full segmentation is:

17% x 1.0 x 0.28 = 4.76%

Note that the risk premium will always be higher when full segmentation is assumed because ρi,M will

always be less than 1.0 when full integration is assumed

Illiquidity Premium

As mentioned in Section 3.1.3.2, investors may demand extra yield (i.e., a premium, which is added to

the discount rate) as compensation for holding an illiquid bond With the Singer-Terhaar model, an

illiquidity premium (if given) is added

Steps in Singer-Terhaar Model

Below is a demonstration of how to use the Singer-Terhaar model to calculate a risk premium and

expected return The data are taken from Example 19

Exam tips related to the Singer-Terhaar model:

 You should be able to do a basic Singer-Terhaar calculation in 5 minutes or less

 Be sure to note whether they are asking for the risk premium or the expected return (which is simply

the risk premium plus the risk-free rate)

 If given more than one risk-free rate, use the investor’s domestic risk-free rate

Refer to Example 18 from the curriculum

Refer to Example 19 from the curriculum

3.2 Survey and Panel Methods

Sections 3.2 and 3.3 address LO.d:

LO.d: Explain the use of survey and panel methods and judgment in setting capital market

expectations

Surveys

Section 3.1 covered several methods that can be used to estimate the returns, standard deviations, and

correlations that are required to set capital market expectations An alternative approach is to ask a

group of academics and/or professionals for their estimates For example, Exhibit 12 shows the results

of surveys of over 200 financial economists who were asked to provide their estimate of the long-term

US equity risk premium In Example 24 (Section 4.1.3), Hans Vermalen bases his forecast of German

inflation in part on a survey of manufacturers

Panels

The panel of forecasting capital market expectations is simply the survey method, but the respondents

remain relatively unchanged over time

Refer to Example 20 from the curriculum

Step 4: Calculate the expected return by adding the weighted risk premium and

the risk-free rate

2.39% + 3.00 = 5.39%

Step 3: Calculate the weighted average of the risk premiums based on level of

integration with GIM

(1.91% x 0.70) + (3.52% x 0.30) = 2.39%

Step 2: Calculate the risk premium for the same asset assuming full segmentation

from GIM Add illiquidity premium (if given)

(11.5 x 1.0 x 0.28) + 0.30% = 3.52%

Step 1: Calcultate the risk premium assuming full integration with GIM Add

illiquidity premium (if given)

(11.5% x 0.5 x 0.28) + 0.30% = 1.91%

3.3 Judgment

An analyst may use formal statistical methods to generate a set of capital market expectations and then

make adjustments based personal experience and judgment In Example 21, William Chew uses a

multifactor model to produce a correlation estimate of between 0.40 and 0.45, but adjusts this

correlation down to 0.30 based on his analysis of inflation forecasts While judgments may not be

empirically precise, they are commonly used in practice As noted in Section 4.5.1, even forecasters who

develop highly-specialized econometric models “use a great deal of personal judgment in arriving at

forecasts.”

Refer to Example 21 from the curriculum

4 Economic Analysis

“History has shown that there is a direct yet fluid relationship between actual realized asset returns,

expectations for future asset returns, and economic activity.”

This is a long session which covers:

 Business cycle and inventory cycle

 Economic growth trends

 Exogenous shocks

 International interactions

 Economic forecasting

 Forecasting asset class returns

This section addresses LO.e:

LO.e: Discuss the inventory and business cycles, and the effects that consumer and business

spending, and monetary and fiscal policy have on the business cycle

4.1 Business Cycle Analysis

Business cycle analysis is an important component of capital market expectations because asset class

returns are significantly influenced by patters of overall economic activity However, correctly predicting

future economic activity and returns is a challenge

Gross Domestic Product (GDP)

Business cycle analysis focuses on fluctuations in the growth rate for an economy’s gross domestic

product (GDP) Formally, GDP is defined as “the total value of final goods and services produced in an

economy during a year.”

In any given year, GDP growth will be faster or slower depending on economic conditions For example,

US GDP grew by 3.3 percent (in real terms) in 2005 and 1.8 percent in 2007 However, as will be

discussed in Section 4.2, a developed economy like America’s can be expected to grow at an average

annual rate of approximately 2.5 percent (in real terms) over the long run This is referred to as the

long-term trend growth rate

Output Gap

The output gap can be thought of as the difference between the current GDP growth rate and the

long-term trend growth rate When short-long-term GDP growth is below the long-long-term trend growth rate, the

output gap is positive and economic output is below-capacity Conversely, when the economy is growing

faster than the long-term trend growth rate, the output gap is negative and inflationary pressures build

Using the figures from above, the US economy was growing above the assumed long-term trend growth

rate of 2.5 percent in 2005 and below this rate in 2007

Recession

The generally accepted definition of a recession is two consecutive quarters of negative economic

growth

4.1.1 The Inventory Cycle

As overall economic growth fluctuates, manufacturers adjust their production levels based on expected

sales When the economy is growing, companies are bullish about their future sales prospects and

production is increased As the economy slows, companies lower their expectations of future sales and

reduce production

Interpreting the inventory-to-sales ratio: As shown in Exhibit 14, a rising inventory-to-sales ratio is

typically associated with slower economic growth, as consumers spend less and inventories pile up By

contrast, a declining inventory-to-sales ratio is typically associated with faster economic growth, as

consumer spending increases at a faster rate than production However, caution is required when

interpreting inventory data For example, a rising inventory-to-sales ratio may be a positive or negative

signal for economic growth depending on the stage of the business cycle Additionally, while inventory

cycles of 2 to 4 years have been observed, the overall trend shown in Exhibit 14 is for declining

inventory-to-sales ratios as retailers, manufacturers, and suppliers have adopted “just in time” inventory

practices

4.1.2 The Business Cycle

This section covers LO.f

LO.f discuss the effects that the phases of the business cycle have on short-term/long-term capital

Phase Economy

Fiscal and Monetary

1 Initial

recovery

Inflation still declining

Stimulatory fiscal policies

Confidence starts to rebound

Short rates low or declining;

bond yields bottoming; stock prices strongly rising

2 Early

upswing

Healthy economic growth; inflation remains low

Increasing confidence

Short rates moving up; bond yields stable to up slightly; stock prices trending upward

3 Late

upswing

Inflation gradually picks up

Policy becomes restrictive

Boom mentality Short rates rising; bond yields

rising; stocks topping out, often volatile

4 Slowdown Inflation continues to

accelerate; inventory correction begins

Confidence drops

Short-term interest rates peaking; bond yields topping out and starting to decline; stocks declining

5 Recession Production declines;

inflation peaks

Confidence weak

Short rates declining; bond yields dropping; stocks bottoming and then starting to rise

Refer to Example 22 from the curriculum

4.1.3 Inflation and Deflation in the Business Cycle

This section addresses LO.g:

LO.g: Explain the relationship of inflation to the business cycle and the implications of inflation for

cash, bonds, equity, and real estate returns

Inflation tends to be highest in the late upswing phase of the economic cycle when short-term GDP

growth is above the trend rate and the economy is operating above-capacity and recedes as economic

growth is below the trend rate Central banks strive to prevent inflation from increasing above an

acceptable level – indeed many central banks are mandated to keep inflation below a specific target

level As shown in Exhibit 18, inflation has a neutral effect most asset classes when it is at or below an

expected level, which can be thought of as the economy being in an equilibrium state The exception is

equities, which tend to outperform during periods of low inflation By contrast, both bonds and equities

underperform when inflation rises above expectations

Deflation is simply negative inflation, which means that overall prices are lower than they were in the

previous period Deflation is considered to be a threat because it undermines debt-financed

investments Note in Exhibit 18 that real estate, which is a highly-leveraged investment, suffers during

periods of deflation More importantly, deflation reduces the ability of central banks to stimulate the

economy by lowering interest rates because their target interest rate cannot be set below zero

When interest rates are very low and unemployment is high, central banks may engage in quantitative

easing (QE) QE is a form of monetary policy tool in which a central bank injects liquidity into the

financial system by purchasing high-quality fixed-income securities, mortgage-backed securities and

high-quality corporate bonds from banks and other financial institutions As a result of QE, central bank

balance sheets and bank reserves increase while sovereign bond yields fall Unlike conventional open

market operations, QE involves large-scale, ongoing purchases of securities which may result in

quasi-permanent increases in the level of bank reserves In order to fund these purchases, a central bank

creates an equally large quantity of bank reserves in the form of central bank deposits

Refer to Exhibit 18 which shows the effects of inflation/deflation on asset classes

Real Estate/Other Real Assets

Inflation at or

below

expectations

Short-term yields steady

equilibrium [Neutral]

Inflation above

expectations

Bias toward rising rates

companies/industries able to pass on inflated

costs [Negative]

Asset values increasing; increased cash flows and higher expected

returns [Positive]

Deflation Bias toward

0% term rates

short-[Negative]

Purchasing power increasing Bias toward steady to lower rates (may

be offset by increased risk of potential defaults due to falling asset

asset-to commodity-using), and highly levered

companies [Negative]

Cash flows steady to falling Asset prices face downward

pressure [Negative]

Refer to Example 23 from the curriculum

Refer to Example 24 from the curriculum

4.1.4 Market Expectations and the Business Cycle

If an investor can identify the current phase of the cycle and correctly predict when the next phase will

begin, he or she should be able to outperform the market Furthermore, if a recession is being

predicted, it is useful to estimate the severity For developed economies, recessions will be less severe

if:

 The upswing was relatively short of mild

 There was no asset bubble

 Inflation is relatively low (so central bank can respond)

 Global economic and political environments are stable

Refer to Example 25 from the curriculum

4.1.5 Evaluating Factors that Affect the Business Cycle

To set capital market expectations, we need to focus on business cycle analysis on four areas:

1 Consumer spending: In most economies, this is the biggest component of GDP It accounts for

nearly 60% to 70% of the GDP To predict consumer spending we look at:

1 Store sales data, consumption data

2 Consumer income data after tax

3 Employment data

2 Business spending: This is a smaller share of the GDP, relative to consumer spending However it

is considerably more volatile To predict business spending we look at business investment and

inventories US Example: Purchasing Managers Index (PMI) This is a measure of how much

businesses are purchasing

This section addresses LO.h:

LO.h: Demonstrate the use of the Taylor rule to predict central bank behavior

Monetary Policy: Central banks should use monetary policy (primarily interest rates) to control the

economy and prevent it from either overheating or suffering in a recession for too long

As an analyst you should try to predict the central bank’s policy rate This can be done using the Taylor

rule

Roptimal = Rneutral + [0.5(GDPforecast − GDPtrend) + 0.5(Iforecast − Itrend)]

Where,

Roptimal = the target for the short-term interest rate

Rneutral = the short-term interest rate that would be targeted if GDP growth were on trend and

inflation on target GDPgforecast = the GDP forecast growth rate

GDPgtrend = the observed GDP trend growth rate

Iforecast = the forecast inflation rate

Itarget = the target inflation rate

Short-term GDP growth rate above the trend rate and above-target expected inflation will result in a

target rate that is above the neutral rate

What Happens When Interest Rates Are Zero or Negative?

Under “zero lower bound,” negative policy rates are not sustainable as the negative interest rates lead

to substantial deposit withdrawals and fall in banks reserves, resulting in upward pressure on interest

rates and downward pressure on economic growth due to credit contraction However, as of the

beginning of 2017, negative policy rates have proven to be sustainable because unlike vault cash, bank

deposits and bank reserves provide an implicit yield or convenience value by facilitating trade in goods,

services, and financial instruments As long as this convenience value is greater than the explicit cost of

holding those deposits, negative policy rates are sustainable Nonetheless, the effectiveness of

expansionary monetary policy is dubious at low and negative interest rate levels because in a negative

interest rate environment, consumers, investors, businesses, and banks tend to have greater levels of

uncertainty and therefore, they may not act as desired by monetary policy makers

Implications of Negative Interest Rates for Capital Market Expectation

It is difficult to incorporate negative interest rates into capital market expectations over finite horizons

When short-term rates are negative, the long-run equilibrium short-term rate can be used as the

baseline rate for forming capital market expectations This rate can be estimated using the neutral policy

rate (Rneutral) in the Taylor rule adjusted for a modest spread between policy rates and default-free rates

Following are some key considerations when forming capital market expectations in a negative interest

rate environment:

 Useful historical data (including instances of negative rates) may not be available or is less reliable;

as a result, quantitative models (statistical models, in particular) based on such historical data may

not provide accurate results;

 Since historical averages may be less useful, forecasting models must account for differences

between the current environment and historical averages;

 Simultaneous use of different monetary policy tools may distort market relationships, e.g shape of

the yield curve or the performance of specific sectors;

Refer to Example 26 from the curriculum

Refer to Example 27 from the curriculum

This section addresses LO.i

LO.i: Interpret the shape of the yield curve as an economic predictor and discuss the relationship

between the yield curve and fiscal and monetary policy

Fiscal Policy: Fiscal policy refers to the government’s manipulation of tax revenue and spending in order

to influence the economy In analyzing fiscal policy, an analyst should remember two points:

1 Focus on changes in budget deficit rather than the absolute level An increase in the budget deficit

(increase in government spending > increase in tax revenue) is referred to as an expansionary fiscal

policy Governments typically engage in such a policy to get out of a recession

2 Focus on changes in deficit due to deliberate changes in government fiscal policy Recognize that

budget deficit changes even without deliberate changes in government fiscal policy During

recessions the budget deficit will automatically widen because unemployment benefits (spending)

increase and tax revenue falls On the other hand, when the economy grows the budget deficit

automatically decrease because unemployment benefits decrease and tax revenue increases

Shape of the yield curve as an economic predictor: An upward sloping yield curve indicates that

short-term rates are low relative to long-short-term rates This shape implies that economic activity will improve It

has been observed that the yield curve tends to flatten (or even become inverted) prior to a recession

The fiscal/monetary policy usually impacts the shape of the yield curve Refer to Exhibit 20 which shows

the four possibilities

Fiscal Policy

Monetary Policy Loose Yield curve steep Yield curve moderately steep

Tight Yield curve flat Yield curve inverted

4.2 Economic Growth Trends

This section addresses LO.j:

LO.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

The components of economic growth trends are:

 Growth from labor inputs

o Labor force growth

o Labor force participation

 Growth from labor productivity

o Growth from capital inputs

o Total factor productivity (TFP) growth

Refer to Example 28 from the curriculum

Investments also play a crucial role in GDP growth, because a high investment amount leads to more

capital In fast-growing economies like Singapore and China, between 30 percent and 40 percent of GDP

is invested annually Slower-growing countries in South America have typically been able to manage

capital investment rates of only 15 to 20 percent of GDP

Economic growth is also influenced by government policies Some of the pro-growth structural policies

are:

 Fiscal policy is sound

 Public sector is minimally invasive on the private sector

 Competition within the private sector is encouraged

 Infrastructure and human capital development are supported

 Tax policies are sound

Refer to Example 29 from the curriculum

4.3 Exogenous Shocks

This section addresses LO.k:

LO.k: Explain how exogenous shocks may affect economic growth trends

Events such as natural disasters or political events that come from outside the economic system can be

difficult to predict, but their impact can be devastating Because they are not expected, exogenous

shocks are not factored into current asset prices An economy that experiences an exogenous shock

should eventually return to its long-term growth rate, but it may be several years before a full recovery

Some examples of exogenous shocks are:

 Shifts in government policies: Most shifts in trends are likely to come from shifts in government

policies For example, a major fiscal law that prevents the government from borrowing beyond

certain limits can be a very effective constraint on excessive spending

 Oil Shocks: Disruptions in the oil supply caused by, for example, geopolitical crises in the Middle

East, can cause prices to spike Because much economic activity depends on oil, higher prices

can have ripple effects throughout the economy, such as lower business investment and

consumer spending power, which can lead to higher inflation and job losses

 Financial Crises: When asset bubbles burst, commercial banks who have made loans based on

inflated valuations, will sharply curtail lending, which can have a devastating effect for an entire

economy In response, central banks are forced to intervene by lowering short-term interest

rates, but their scope for action is limited if a financial crisis occurs when interest rates are

already low

4.4 International Interactions

This section addresses LO.l:

LO.l: Identify and interpret macroeconomic, interest rate, and exchange rate linkages between

economies

Dependence of a particular economy on international interaction depends on the size and degree of

specialization Large countries with diverse economies, tend to be less influenced by developments

elsewhere than small countries

Macroeconomic Linkages

A country’s economy is impacted by:

1 Changes in foreign demand for their exports Take a country like India An increase in foreign

demand of Indian exports will cause the economy to improve

2 Changes in cross-border investments Continuing with India, increases in foreign investments in

India will cause the economy to improve A reduction in foreign investments will have a negative

impact on the Indian economy

Interest Rate/Exchange Rate Linkages

Some countries directly peg their currencies at a fixed exchange rate with another currency (often the

US dollar) The advantages of a peg are:

 Businesses know that exchange rates won’t change dramatically

 Smaller economies can use currency pegs to control inflation

If Country X has pegged its exchange rate to the US dollar, the interest rates in Country X will depend on

market confidence in the peg If confidence in the peg is high the interest rate in Country X will be

similar to the US dollar interest rate If the peg is perceived to be unsustainable the interest rate

differential will increase In other words, investors will demand a premium for holding Country X

currency

For floating exchange rates, the:

 currency of the country with the higher real interest rate will appreciate, all else equal

 currency of the country with the higher inflation rate will depreciate, all else equal

Emerging Markets

This section addresses LO.m:

LO.m: Discuss the risks faced by investors in emerging-market securities and the country risk

analysis techniques used to evaluate emerging market economies

Emerging markets offer higher risk, higher return relative to developed markets

Emerging economies depend on foreign debt to finance their capital investment By contrast, developed

economies borrow from domestic sources

Additionally, emerging economies are often characterized by dependence on specific commodities or a

manufacturing sector that is heavily concentrated in one industry

Important factors to consider when investing in emerging markets are:

 Fiscal and monetary policy

o Deficit to GDP (2 – 4% is acceptable)

o Debt to GDP (70 – 80% is acceptable)

 Economic growth prospects

o Emerging economies should be growing faster than 4% annually (in real terms)

Because of their dependence on foreign debt and relatively undiversified economies, investors in

emerging markets should pay particular attention to the following factors:

 External accounts

o Current account deficit should be below 3 percent of GDP

o See Example 35

 External debt

o Foreign debt should be below 50 percent of GDP

o Debt to current account receipts should be below 200 percent

This section addresses LO.n:

LO.n: Compare the major approaches to economic forecasting

The major approaches to economic forecasting are:

4.5.1 Econometric Modeling

Econometric modeling generates capital market expectations by combining quantitative methods and

economic theory Points to note are:

 Model complexity depends on a number of variables; bigger models (more variables) are not

always better

 Requires good data, which may not always be available

 Inputs are selected based on their ability to predict future economic growth

 Relationships between inputs may change over time

The advantages of this approach are:

 Good at simulating the effects of changes in specific variables

 Imposes a consistency constraint in making forecasts

 Forces the analyst to reassess prior views

 Good at forecasting economic upswings

The disadvantages of this approach are:

 Complex and time-consuming to create

 Requires careful analysis of output

 Historical relationships between variables change

 Better at predicting recoveries than recessions

4.5.2 Economic Indicators

Economic indicators are economic statistics that contain information on an economy’s recent past

activity or its current or future position in the business cycle This is the simplest forecasting approach to

use because it requires following only a limited number of variables Leading economic indicators are

best thought of as early signs of probable events to come

Some of the commonly used leading indicators in the US are:

 Average weekly hours, manufacturing

 Manufacturer’s new orders, consumer goods and materials

 Manufacturer’s new orders, non-defense capital goods

 Index of consumer expectations

 Average weekly initial claims for unemployment insurance

 Vendor performance, slower deliveries diffusion index

 Building permits, new private housing starts

 Stock prices

 Money supply

 Interest rate spread: YTM on 10-year Treasury bonds minus YTM on 3-month T-bills

An important point to note is that sometimes we get ambiguous signals, where not all economic

indicators will point in the same direction This is better measured by a diffusion index, which shows

how many indicators are up and how many are down

The advantages of this approach are:

 Simple to construct

 Intuitive

 May be available from 3rd parties

 May be tailored to suit individual needs

 Literature exists on the effectiveness of using various 3rd party indicators

The disadvantages of this approach are:

 Historical relationships between variables change

 Can provide false signals

4.5.3 Checklist Approach

In this approach, the forecaster asks a series of questions about likely components of spending and

then, aggregating the information gathered, reaches a conclusion about the outlook for economy

The advantages of this approach are:

 Straightforward

 Flexibility

The disadvantages of this approach are:

 The checklist approach “involves a substantial amount of subjective judgment…”

 Time consuming

 Can’t be overly complex

Refer to Example 30 from the curriculum

4.6 Using Economic Information in Forecasting Asset Class Returns

This section addresses LO.o:

LO.o: Demonstrate the use of economic information in forecasting asset class returns

The sub-sections below show how principle asset classes are influenced by different economic variables:

4.6.1 Cash and Equivalents

The key economic variable is the overnight rate If interest rates are likely to fall, money managers

should increase duration (e.g move funds from 1-month maturity to 9-month maturity)

Refer to Example 31 from the curriculum

4.6.2 Nominal Default-Free Bonds

The yield on a long-term default-free bond reflects the real risk-free rate and a component attributable

to expected inflation The key factor is inflation, which is factored into nominal interest rates If money

managers expect inflation will be lower than what is implied in current nominal rates, they should invest

in longer-term bonds

4.6.3 Defaultable Debt

Yields on corporate debt reflect an assessment of credit risk The key factor is credit spread on

defaultable debt above the yield on risk-free bonds Spreads increase during economic downturns and

narrow during economic upswings

4.6.4 Emerging Market Debt

The key factor is policy changes Investors typically look at the spread between emerging market

government bonds and the US Treasury yield curve

4.6.5 Inflation-Indexed Bonds

The key factor is real interest rates Volatility of Treasury Inflation-Protected Securities (TIPS) depends

on volatility of real interest rates As the economy grows, TIPS yields increase As inflation expectation

rise, TIPS yields fall

4.6.6 Common Shares

This section addresses LO.p

LO.p: Explain how economic and competitive factors can affect investment markets, sectors, and

specific securities

Economic Factors Affecting Earnings: Stocks in different industries will react differently to economic

factors If a stock is cyclical, economic factors will have a higher impact on its earnings as compared to a

non-cyclical stock For example, transportation sector is more cyclical than the energy sector Therefore,

the energy sector is more capable that the transportation sector of passing the costs of higher inflation

on to consumers (because profits have a higher correlation with inflation)

Refer to Example 32 from the curriculum

Refer to Example 33 from the curriculum

The P/E Ratio and the Business Cycle: PE ratios are highest in the early stages of an economic recovery

when earnings are still low, but prices are starting to increase High inflation depresses PE ratios

Emerging Market Equities: Equity risk premiums are higher for emerging markets relative to those of

developed economies

4.6.7 Real Estate

Key factors are:

 Growth in consumption

 Real interest rates

 Term structure of interest rates

This section addresses LO.q:

LO.q: Discuss the relative advantages and limitations of the major approaches to forecasting

exchange rates

Four broad approaches to exchange rate forecasting are:

 Purchasing Power Parity: Movements in exchanges rates should offset differences in inflation

rates

 Relative Economic Strength: This approach focuses on investment flows If a country offers good

investment opportunities, demand for its currency will increase

 Capital Flows: This approach focuses on expected long-term capital flows (FDI and equity

investments)

 Savings-Investment Imbalances: Currency movements are explained by domestic

savings-investment imbalances If the savings-investment is greater than domestic savings, then capital must

flow into the country from abroad to finance the investment

Refer to Example 35 from the curriculum

Refer to Example 36 from the curriculum

4.7 Information Sources for Economic Data and Forecasts

Refer to Exhibit 33 for a list of information sources However note that, while this material is good for

the real word use, it is not too useful for the exam

Reallocating a global portfolio

This section addresses LO.r

LO.r: Recommend and justify changes in the component weights of a global investment portfolio

based on trends and expected changes in macroeconomic factors

This LO asks you to use the material you have learned in this reading and apply it to portfolio

management You need to perform a thorough economic analysis and determine which global

investments are most appropriate for the investor

The qualities that make an economy attractive are:

Production Above trend, declining Well above trend Below trend, rising

Summary

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

process;

1) Specify set of expectations

that are needed

Determine relevant asset classes; for n asset classes we need to estimate: n expected returns, n standard deviations and (n2 – n) / 2 distinct correlations

2) Research historical record Analyze each asset class’s historical performance by gathering

relevant information

3) Specify methods and/or

models and their

information requirement

Methods selected should be consistent with the objectives of the analysis and investment time horizon Example: Use DCF method for developing long-term equity market forecasts

4) Determine the best

sources for the information

needed

Ensure data quality and select appropriate data frequency Example:

Use quarterly or annual (daily) data series for long-term (short-term) CME

5) Interpret current

investment environment

Interpret information to make mutually consistent decisions

6) Provide the set of

expectations that are

needed and document

conclusions

Document answers (along with reasoning and assumptions) to the questions formulated in Step 1 to develop forward-looking forecasts

on capital markets

7) Monitor actual outcomes

to provide feedback to

improve CME process

Monitor and compare actual outcomes against expected outcomes to improve forecasts Good forecasts are 1) unbiased, objective and well researched, 2) efficient and 3) internally consistent

LO.b: discuss challenges in developing capital market forecasts;

• Statistical problems associated with regime changes

• Using high-frequency data (weekly or even daily) results in underestimated correlations estimates

Ex post risk a biased

measure of ex ante risk

Ex-post risk estimates may be poor proxy of the ex ante risk estimate

Biases in analysts’

methods

• Data mining bias

• Time-period bias Failure to account for

conditioning

information

Expectations concerning systematic risk of an asset class should be conditioned upon on the state of the economy because systematic risk varies with business cycle

Misinterpretation of

correlations

High correlation between A and B could be because “A predicts B” or “B predicts A” or “C predicts A and B”

Psychological traps Anchoring trap, status quo trap, confirming evidence trap

Model and input

uncertainty

Uncertainty about whether selected model is correct; uncertainty about input data

LO.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;

LO.d: explain the use of survey and panel methods and judgment in setting capital market expectations;

Survey/Panel  Survey: inquire a group of experts for their expectations

 Panel: inquire a panel of experts for their expectations Financial

 where E (RM) is the expected return on the world market portfolio

 Global investable market (GIM) is a proxy for the world market portfolio

With perfect integration: RPi = [(σi) x (ρi, M) x (Sharpe ratio of GIM)] + Illiquidity premium

With complete segmentation: RPi = [(σi) x Sharpe ratio of GIM)] + Illiquidity premium

Sharpe ratio = risk premium / standard deviation

Singer-Terhaar approach:

Risk premium = (Degree of integration × risk premium under perfectly integrated markets)

+ ({1 - degree of integration} × risk premium under perfectly segmented markets)

LO.e: discuss the inventory and business cycles and the effects that consumer and business spending

and monetary and fiscal policy have on the business cycle;

Inventory cycle reflects fluctuations in inventories

 Falling inventory/sales ratio indicates faster economic growth, as consumer spending increases at a

faster rate than production

 Sharply rising inventory/sales ratio indicates slower economic growth, as consumers spend less and

inventories pile up

Business cycle represents short-run fluctuations in GDP

 Output gap = Trend GDP – Actual GDP

 Output gap is positive (negative) and inflation is low (high) during recession (expansion)

 Wealth effect: consumers spend more in response to perceived increase in wealth

 Permanent income hypothesis: consumer spending behavior is determined by long-term income

expectations rather than temporary or unexpected (or one-time) change in income/wealth

LO.f: discuss the effects that the phases of the business cycle have on short-term/ long-term capital

market returns;

Fiscal and Monetary

1 Initial

recovery

Inflation still declining

Stimulatory fiscal and monetary policies

Confidence starts to rebound

Short rates low or declining; bond yields bottoming; stock prices strongly rising

2 Early

upswing

Healthy economic growth;

inflation remains low

Withdrawal of stimulatory monetary &

fiscal policies start

Confidence increasing

Short rates moving up; bond yields stable

to up slightly; stock prices trending upward

3 Late

upswing

Inflation gradually picks

up

Restrictive monetary policy

Boom mentality

Short rates rising; bond yields rising;

stocks topping out, often volatile

4 Slowdown Inflation

continues to accelerate;

inventory correction begins

Withdrawal of restrictive monetary policy start

Confidence starts to drop

Short-term interest rates peaking; bond yields topping out and starting to decline (inverted yield curve); stocks declining

5 Recession Production

declines;

inflation peaks

Stimulatory monetary policy

Confidence weakens

Short rates declining; bond yields dropping; stocks bottoming and then starting to rise

LO.g: explain the relationship of inflation to the business cycle and the implications of inflation for cash,

bonds, equity, and real estate returns;

Inflation tends to

 rise in late phases of a business cycle

 decline during recession and early stages of recovery

Real Assets Inflation at or

below

expectations

Short-term yields steady or declining

(Neutral)

Yield levels maintained; Market

is in equilibrium

(Neutral)

Bullish while market

is in equilibrium state (Positive)

Cash flow steady to rising slightly;

returns equate to long-term average;

market in general equilibrium

(Neutral)

Inflation above

expectations

Bias toward rising rates

(Positive)

Bias toward higher yields due to a higher inflation premium (Negative)

Negative for financial assets; less negative for companies/

industries able to pass on inflated costs (Negative)

Asset values increase (Positive)

short-term rates

especially affects asset-intensive, commodity-producing and highly levered companies

(Negative)

Cash flows steady to falling; asset prices fall (Negative)