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Canada
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University of Manitoba
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MIS 4500
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Pourang Irani
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Management Info. Systems

MIS 4500

Pourang Irani

Winter

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Chapter 23: Capital Market Expectations
Frameworks for Developing Capital Market Expectations:
1. Specify the final set of expectations that are needed, including time horizon to which they
apply
2. Research historical record
i. collect macroeconomic and market info on 1. geographic area; or 2. broad asset
class
3. Specify the method(s) and/or model(s) that will be used and their information
4. Determine the best sources for information needs
5. Interpret the current investment environment using the selected data and methods,
applying experience and judgment
6. Provide the set of expectations that are needed, documenting conclusions
7. Monitor actual outcomes and compare them to expectations, providing feedback to
improve the expectations-setting process
Beta research: related to systematic risk and returns to systematic risk; development of capital
market expectations
Alpha research: related to capturing excess risk-adjusted returns by a particular strategy
Good forecasts are:
unbiased, objective and well researched
efficient (reducing magnitude of forecast errors to a minimum)
internally consistent
Challenges in Forecasting:
Limitations of economic data
o time lag of collection, processing and dissemination
o changes in definitions and calculation methods (say CPI-U)
re-basing indices
Data measurement errors and biases
o Transcription errors: errors in gathering and recording
o Survivorship bias: data series reflect only survivors
o Appraisal (smoothed) data (say real estate or alternative investments): results in 1.
calculated correlations w/ other assets tend to be smaller in absolute value than
true correlations; 2. true standard deviation of asset is biased downward
Limitations of historical estimates: analysis should include discussion of what may be
different from past
o changes in technological, political, legal, and regulatory environments;
disruptions such as wars
o change of regime: change of governing set of relationships creates nonstationarity
(different parts of data series reflect different underlying statistical properties)
o long data series:
risk that data cover multiple regimes
time series of required length may not be available in order to get data series of required length, temptation is to use high-
frequency data (weekly or daily): more sensitive to asynchronism
(discrepancy in dating of observations that occurs b/c stale (out-of-date)
data may be used in absence of current date) across variables, producing
lower correlation estimates
Ex Post Risk Can be a Biased Measure of Ex Ante Risk
o ex post returns may reflect that didn’t materialize resulting in overstated estimates
of ex ante returns
Biases in Analysts’ Methods:
o Data-mining bias: repeatedly ―drilling‖ or searching dataset to find statistically
significant pattern
o Time-period bias: research findings that are sensitive to selection of starting
and/or ending dates, may bias out-of-time period analysis
Failure to account for conditioning info: analyst should condition forecasts on the state of
economy to formulate most accurate expectations (say different betas in expansion
economies and recession economies)
Misinterpretation of correlations:
o distinguish b/w exogenous and endogenous variables;
o correlation may be spurious w/ no predictive relationship
test w/ multiple regression variable significance
test using time series analysis w/ independent variables including lagged
value of dependent variable, lagged value of tested variable and lagged
value of control variables
Psychological traps:
o anchoring trap: tendency to give disproportionate weight to first info received on
topic
o status quo trap: tendency to perpetuate recent forecasts—to predict no change
o confirming evidence trap: bias that leads individuals to give greater weight to info
that supports existing or preferred point of view
examine all evidence w/ rigor
enlist an independent-minded person to argue against
be honest about motives
o overconfidence trap: tendency to overestimate accuracy of forecasts
o prudence trap: tendency to temper forecasts so that they don’t appear extreme; to
be overly cautious in forecasting
o recallability trap: tendency of forecasts to be overly influenced by events that
have left strong impression on person’s memory
o model uncertainty: uncertainty whether selected model is correct
input uncertainty: uncertainty whether inputs are correct
Tools for Formulating Capital Market Expectations:
Formal tools: established research methods amenable to precise definition and
independent replication of results
o Statistical methods: descriptive statistics; inferential statistics
historical statistical approach: sample estimators (assuming stationarity) sample arithmetic mean total return or sample geometric mean
total return as estimate of expected return
sample variance as estimate of variance
sample correlations as estimate of correlations
Shrinkage estimation: taking weighted average of historical estimate of
parameter and some other parameter estimate based on analyst’s belief of
weights
target covariance matrix: selecting an alternative estimator of
covariance matrix
Time-Series Estimators: forecasting a variable based on lagged variables
good for short-term forecasts
volatility clustering: tendency for large (small) swings in prices to
be followed by large (small) swings of random direction
2 2 2
o t t1 1 t ; beta is the rate of decay of the
influence of the value of volatility in one period on future
volatility; epsilon is random noise
Multifactor Models:
useful for estimating covariances:
o estimates of covariances b/w asset returns can be derived
using assets’ factor sensitivities
o may filter out noise
o make it relatively easy to verify consistency of covariance
matrix
factor covariance matrix: cross tabulations showing covariances /
variances of factors
o Discounted Cash Flow Models:
equity markets:
Gordon (constant) growth model
o E(R ) D 1 g

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