Capital Market Expectations

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Capital Market Expectations

• 01/12/09

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Capital Market Expectations

• Questions to be answered
• What are capital market expectations (CME)?
• How does CMEs fit into to the bigger portfolio
  management picture?
• What is an appropriate framework for developing
  CME?
• What are some key issues that analysts face or
  must recognize while developing CME?
• What are some common tools for formulating CME?

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Capital Market Expectations

• Capital Market Expectations (CME) represent the
  investors expectations concerning the risk and
  return prospects of asset classes.
• These are different from micro expectations,
  which are expectations concerning individual
  assets.

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CME within the larger picture

• Developing CME allow portfolio managers to
• Develop return and risk expectations for broad
  asset classes.
• Ensure that return and risk objectives are
  consistent with investor expectations.
• Construct efficient portfolios
• Develop a basis for the first step in the
  top-down approach of constructing portfolios
• Broad asset class analyses, industry analyses,
  individual security analyses

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Framework for Developing CME

• Specify the final set of expectations that are
  needed, including time horizon.
• Research the historical record.

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Framework for Developing CME

• Specify the methods that will be used to
  formulate CME and the information required.
• Determine the best sources for information
  needed.

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Framework for Developing CME

• Interpret the current investment environment
  using the selected data and methods.
• Provide the set of expectations required.
• Monitor actual outcomes to provide feedback to
  improve the CME development process.

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Framework for Developing CME

• Good forecasts should generally be
• Unbiased, objective and well researched
• Efficient (minimizing forecast errors)
• Internally consistent

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Challenges in Forecasting

• Limitations of economic data lagged, revised,
  changes in definitions and calculation methods.
• Data measurement errors and biases
  transcription errors, survivorship bias,
  appraisal (smoothed) data.

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Challenges in Forecasting

• Limitations of historical estimates
• Risk/return relationships can be changed if there
  is a change in regime.
• Using historical estimates (without correcting
  for changing regimes) assumes stationarity, i.e.,
  the statistical properties of the forecasted
  variables remain the same.

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Challenges in Forecasting

• Limitations of historical estimates
• Data-mining bias. Does the variable have economic
  rationale?
• Time-period bias.

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Challenges in Forecasting

• Psychological traps
• Anchoring trap tendency to give
  disproportionate weight to the first information
  received
• Status quo trap tendency for forecasts to
  perpetuate recent observations

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Challenges in Forecasting

• Psychological traps
• Confirming evidence trap bias that leads
  individuals to give greater weight to information
  that supports a preferred point of view
• Overconfidence trap tendency to overestimate
  the accuracy of forecasts

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Challenges in Forecasting

• Psychological traps
• Prudence trap tendency to temper forecasts so
  that they do not appear extreme.
• Recallability trap tendency of forecasts to be
  overly influenced by events that have left a
  strong impression.

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Tools for Formulating CME

• Historical Statistical Approach
• Historical statistical characteristics (mean,
  variance, correlation) can be used to estimate
  future returns.
• Shrinkage Estimators
• This involves taking a weighted average of a
  historical estimate of a parameter and some other
  parameter estimate (from, for example, CAPM).

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Tools for Formulating CME

• Financial Market Equilibrium Models
• These models describe relationships between
  return and risk in which returns are correctly
  estimated if the equilibrium model is correct and
  investments are properly priced based on their
  risk levels.
• The CAPM is one example of such a model.
• Limitations of the CAPM as we know it
• It is difficult to define an appropriate market
• Other variables appear to explain returns
• The model assumes that markets are perfectly
  integrated

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Tools for Formulating CME

• Financial Market Equilibrium Models
• ICAPM International Capital Asset Pricing
  Model (Singer and Terhaar, 1997)
• The basic model is the same
• Where

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Tools for Formulating CME

• Financial Market Equilibrium Models
• ICAPM
• An appropriate proxy for the world market
  portfolio is the global investable market (GIM).
  This market should include traditional and
  alternative asset classes.
• We can rearrange the ICAPM equation to the
  following equation
• where the term in the brackets (GIM Sharpe
  Ratio) represents the expected risk premium per
  unit of standard deviation for the GIM.

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Tools for Formulating CME

• Financial Market Equilibrium Models
• ICAPM
• Based on research, a good estimate of this GIM
  Sharpe ratio is 0.28.
• The ICAPM has the ability to incorporate market
  imperfections. We consider market segmentation.
• Market segmentation means that there are
  impediments to capital market movements.
• The more the market is segmented, the more it is
  dominated by local investors
• With segmented markets, two identical assets
  (with the same risk characteristics) can have
  different expected returns.

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Tools for Formulating CME

• Financial Market Equilibrium Models
• ICAPM
• Calculating expected return with market
  segmentation
• Assume that the market is completely segmented.
  Then the risk premium of that market is
• With the degree of integration of that market, we
  can estimate the final risk premium

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Tools for Formulating CME

• Financial Market Equilibrium Models
• ICAPM
• Degree of integration
• Developed equity and bond markets 80
• US real estate 70
• Emerging market equities and bonds 65

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Tools for Formulating CME

• Multifactor models
• Multifactor models explain the returns to an
  asset in terms of a set of factors and are based
  on the Arbitrage Pricing Theory (APT).

F1t is the period t return to the first
designated risk factor and Rit can be measured as
either a nominal or excess return to security i.
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Tools for Formulating CME

• Multifactor models
• To determine a specific asset return and risk
  characteristics, we use the factor covariance
  matrix (which contains the covariances for the
  factors that drive the return) and factor
  sensitivities (or betas) for the asset.

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Tools for Formulating CME

• Multifactor models
• So, for a two-factor model, two-asset model (M),
  for example,
• and the asset variances and covariances are
  calculated as

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Tools for Formulating CME

• Multifactor models
• These models can be used to develop expectations
  about broader asset classes or to develop
  expectations about individual securities.
• The following two models are the three-factor and
  four-factor models used for individual
  securities.
• They can also be used to determine the style of a
  particular stock, mutual fund or ETF.

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Tools for Formulating CME

• Multifactor models
• Fama and French three-factor model

where SMB (i.e. small minus big) is the return to
a portfolio of small capitalization stocks less
the return to a portfolio of large capitalization
stocks HML (i.e. high minus low) is the return to
a portfolio of stocks with high ratios of
book-to-market values less the return to a
portfolio of low book-to-market value stocks
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Tools for Formulating CME

• Multifactor models
• Carhart (1997) extends the Fama-French three
  factor model by including a fourth common risk
  factor that accounts for the tendency for firms
  with positive past return to produce positive
  future return. This is referred to as the
  momentum factor.
• where PR1YR is the average return to a set of
  stocks with the best performance over a year
  minus that of the of a set of stocks with the
  worst performance.

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Tools for Formulating CME

• Multifactor models
• Estimating Expected Returns for Individual Stocks
• A Specific set of K common risk factors must be
  identified
• The risk premia for the factors must be estimated
• Sensitivities (or betas) of the ith stock to each
  of those K factors must be estimated
• The expected returns can be calculated by
  combining the results of the previous steps in
  the appropriate way

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Tools for Formulating CME

• Discounted Cash Flow models
• The Gordon growth model is often used to
  formulate the long-term expected return of equity
  markets
• where g can be estimated as the growth rate in
  nominal GDP (real GDP growth rate expected
  inflation rate)

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Tools for Formulating CME

• Risk Premium Approach
• The risk premium approach expresses the expected
  return on a risky asset as the risk-free rate
  plus risk premiums to compensate investors for
  the sources of risk for that asset.

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Tools for Formulating CME

• Risk Premium Approach
• For Fixed-income

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Tools for Formulating CME

• Risk Premium Approach
• Inflation premium reflects the average inflation
  rate expected over maturity of debt
• Illiquidity premium represents the risk of loss
  relative to an investment value if it needs to be
  converted to cash quickly
• Tax premium may be applicable to certain classes
  of stocks.

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Tools for Formulating CME

• Risk Premium Approach
• For equity, we can use a historical risk premium
  estimate to proxy for the equity risk premium

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Tools for Formulating CME

• Survey Method
• The survey method involves asking a group of
  experts for their CMEs.
• The Livingston Survey (managed by the Fed of
  Philadelphia) provides expectations on
  macro-economic variables

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Readings

• RB 8 (pgs. 239 246 review of CAPM)
• RB 9 (pgs. 279 291),
• RM 2 (up to section 4, we do not cover the
  Grinold-Kroner model or Time Series Estimators)