Capital Market Expectations

Report
Chapter 4
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Discuss the role of capital market
expectations in the portfolio management
process
Review a framework for setting capital market
expectations
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Identify and discuss the following as they
affect the setting of capital market
expectations:
The limitations of economic data
Data measurement errors and biases
The limitations of historical estimates
Ex post risk as a biased measure of ex ante risk
Biases in analysts’ methods
The failure to account for conditioning
information
◦ The misinterpretation of correlations
◦ Psychological traps
◦ Model uncertainty
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Explain the use of survey and panel
methods and judgment in setting capital
market expectations
Distinguish between the inventory cycle and
the business cycle
Identify and interpret business cycle phases
and their relationship to short- and longterm capital market returns
Review the relationship of inflation to the
business cycle and characterize the
relationship between inflation/deflation and
cash, bonds, equity, and real estate
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Distinguish between business cycles and
economic growth trends and demonstrate
the application of business cycle and
economic growth trend analysis to the
formulation of capital market expectations
Identify and interpret the components of
economic growth trends and explain how
governmental policies and exogenous
shocks can affect economic growth trends
Identify and interpret macroeconomic and
interest and exchange rate linkages
between economies
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Evaluate how economic and competitive
factors affect investment markets, sectors,
and specific securities
Recommend and justify changes in the
component weights of a global investment
portfolio based on trends and expected
changes in macroeconomic factors
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Capital market expectations are the investor’s
expectations concerning the risk and return
prospects of various asset classes (macro) as
opposed to specific assets (micro)
Essential input to formulating a strategic
asset allocation
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Specify the final set of expectations needed, including
the time horizon to which they apply
Research the historical record
Specify the method(s) and/or model(s) that will be used
and their information requirements
Determine the best sourced for information needs
Interpret the current investment environment using the
selected data and methods, applying experience and
judgment
Provide the set of expectations that are needed,
documenting conclusions
Monitor actual outcomes and compare them to
expectations, providing feedback to improve the
expectations-setting process
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Analyst must understand definition,
construction, timeliness and accuracy of
any data used, including biases
Time lag can be an impediment to use
Data are frequently revised by collecting
officials
Definitions and calculation methods change
over time
Data collectors often re-index, introducing
the risk of mixing data indexed to different
bases
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Transcription errors – errors in gathering
and recording data (most serious if they
reflect a bias)
Survivorship bias – including only those
entities that have survived for the entire
measurement period tends to paint an
overly optimistic picture
Appraisal (smoothed) data – understates
volatility and correlation with other assets
due to infrequent measurement
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Things could be different, altering
risk/return characteristics
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Technological
Political
Legal and regulatory environments
Disruptions (war, natural disaster)
Such regime changes result in
nonstationarity – differing underlying
properties during different parts of a time
series
Amount of data needed for statistical
analysis may require a long time series,
increasing nonstationarity risk (if the data is
even available)
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Prices at any one time reflect risk factors
that may not materialize
When the risk fails to materialize, asset
prices rise
Since the risk did not materialize, it is not
incorporated in the ex-ante estimate (i.e.
risk is underestimated
The return did materialize, so it is
incorporated in ex-ante estimates (return is
overestimated)
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Data mining – with enough data there will be
random correlations that are not
economically meaningful
Time period bias – research findings often
sensitive to start and end dates for
measurement period
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Anchoring – gives disproportionate weight to the
first information received on a topic
Status quo – tendency to perpetuate recent
observations in forecasts
Confirming evidence – giving greater weight to
information that supports existing or preferred
point of view than to evidence that contradicts it
Overconfidence – overestimating accuracy of
forecasts
Prudence – tempering forecasts to not appear
extreme
Recallability – forecasts overly influenced by events
that left a strong impression on the forecaster’s
memory
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Model uncertainty is the risk that the model is
not correct or appropriate
Input uncertainty relates to whether the
inputs to the model are correct
Model and input uncertainty make it difficult
to evaluate inefficiencies or market anomalies
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Discounted cash flow models – expressing
current value as discounted value of future
cash flows
Risk Premium Approach – expresses
expected return as risk free rate plus an
appropriate risk premium
Financial Market Equilibrium Models –
describe relationships between expected
return and risk in which supply and demand
are in balance
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Survey and panel methods consolidate the
opinions of a group of experts
Judgment can improve forecasts relative to
objective methods
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Business cycle (9-11 years) – These are generalizations – realize that each of the listed
effects do not always happen or do not occur at the same rate or frequency during
different cycles across time – also realize that in order to take advantage of the listed
occurrences, you must be in the correct stocks or bonds ahead of the phases occurring
which means that you must predict the phases ahead of time
◦ Recovery
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still large output gap
bond yields bottoming, stocks often surge
risk pays.
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robust growth without inflation
Rising capacity utilization and profits
Short rates start rising, long rates stable
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Output gap closed, danger of overheating
Inflation starts to pick up
Rising interest rates, stock markets rising but volatile
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Slowing economy, sensitive to shocks
Peaking interest rates
Interest sensitive stocks perform best
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Declining GDP
Falling short-term interest rates and bond yields
Stock market bottoms early and begins to rise ahead of business cycle recovery
◦ Early upswing
◦ Late upswing
◦ Slowdown
◦ Recession
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Consumers
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Business
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Monetary policy
◦ 60-70% of GDP so typically the most important
factor
◦ Monitor retail sales and personal income
◦ Smaller but more volatile
◦ Monitor surveys such as ISM, PMI
◦ Mechanism for intervening in cycle
◦ Watch inflation, pace of growth, unemployment
and capacity utilization
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Emerging countries are catching up
economically
Need higher investment rates in physical
and human capital
If domestic savings are inadequate foreign
capital is needed
More volatile political and social
environments
Many need major structural reform to
unlock potential
Tend to be commodity-driven or have
expertise in a narrow industrial range
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How sound are fiscal and monetary policy
What are the economic growth prospects
Is the currency competitive, and are external
accounts under control
Is external debt under control
Is liquidity plentiful
Is political situation supportive of required
policies
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econometric models approach
◦ advantages
◦ disadvantages
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leading indicator based approach
◦ advantages
◦ disadvantages
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checklist approach
◦ advantages
◦ disadvantages

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