1. Top Down Investing Bottom-up Approach Top-Down Approach
Choose under-valued securities
Buying performance cheaply
Top-Down Approach
Build the ideal portfolio
Structure portfolio to investor needs
May have “expensive” & “cheap” stocks
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2. The Ideal Portfolio High return Low risk Inexpensive to manage
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3. Measures of Return Total return Geometric mean = growth
After tax income and appreciation
Geometric mean = growth
[V(T) / V(o) ](1/T) - 1
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4. Measures of Risk Volatility Downside Risk Value-at-Risk
2 = variance: average squared deviation from historical mean
Downside Risk
semi-variance: average squared negative deviation from historical mean
Value-at-Risk
Minimum expected loss for a given horizon and probability level
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5. Measures of Expense Transactions Information acquisition
Monitoring costs
Probability of losing client
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6. Technology of Return and Risk
Harry Markowitz , 1959
Reduced investment to two dimensions
Showed that portfolio mix matters most
Turned investing into statistics
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7. Mean and Standard Deviation
Mean measures expected return
Standard deviation measures investor risk
Example: six asset classes
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8. Correlation: the Third Statistic
Correlation and co-movement
One asset “hedges” the other
Two assets are better than one
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9. Gold and the Stock Market
Correlation of -.3 since 1970
Hedged 70’s crash
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10. Gold in the Portfolio? 25% risk reduction 3/4 stocks, 1/4 gold
Is gold dominated?
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11. The Efficient Frontier
More assets move frontier
Frontier is a continuous set of efficient portfolios
Highest return for each level of risk
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12. The First Frontier Markowitz took stocks from the NYSE
Mixed them with cash
Created the first frontier
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13. Risk Reduction by Adding Assets
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14. International Equity Groups
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15. Risk and Return Inputs N Periods Geometric Arithmetic Standard
Mean (%) Mean (%) Deviation (%)
MSCI Automobiles Cap App
MSCI Banking Cap App
MSCI Chemicals Cap App
MSCI Energy Sources Cap App
MSCI Gold Mines Cap App
MSCI Telecomm Cap App
MSCI Textiles & Apparel Cap App
MSCI Transport - Airlines Cap App
MSCI Utilities - Elec&Gas Cap Ap
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16. Capital Appreciation Indices
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17. Results
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18. Minimum Variance Portfolio
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19. Value at Risk How much do I expect to lose 1 in 20 times?
E.G. VAR for a $100 million portfolio with a std. of 12% at the 1/20 confidence level is:
VAR = $100m * 1.64 * 12% - 8% = 12%
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20. Value at Risk Minimum expected annual loss at a 95% confidence
level for the lowest risk
portfolio = -12%
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21. New Risk Technology VAR with simulations VAR with non-normal returns
VAR with derivatives
VAR with chaotic systems
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22. Systematic Risk
Non-diversifiable risk
Market risk
Beta risk
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23. Portfolio Investors Diversify away most risk
Demand return only for residual
Have advantage over non-diversified investors
Can bid more for risky assets
Have less volatile portfolios
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24. Portfolio Investors’ Expected Returns
Only market exposure matters
Higher  means higher expected return
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25. Factor Models Assume price-setters are diversified
Ignore diversifiable risk
Expected return must compensate remaining risk
“Factors” are risk sources
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26. Developing a Top-Down Portfolio
Assess sensitivity of client to:
inflation shocks
interest rate shifts
GDP shocks
Tilt portfolio away from stocks matching firm sensitivity
Capture factor exposure with minimum transactions costs
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27. Example Client is the pension fund of an international oil company
defined benefits
ability to contribute depends upon oil prices
Exposure to oil shocks
APT allows them to “hedge” oil shocks
Analysis lets them tilt towards risks they care less about
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28. Measuring Beta Linear “Response” to Factor Returns
Example: MSCI is about a 50% “hedge” of the S&P 500.
Better Fit = Better Hedge
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29. Multi-Factor Models APT = Macro-economic risk factors
BARRA = Security-specific risk factors
Fama-French = Size and book to market ratio as risk
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30. Arbitrage Pricing Theory
Chen, Roll and Ross factors are:
Production risk
Inflation shocks
term structure shifts
investor confidence
Explanatory factors
Fundamental economic forces drive stocks
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31. BARRA factor models Stock characteristics: No common factors Earnings
Leverage
Growth
Sales
No common factors
model works to explain returns
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32. Fama and French Factors
Size
Small stocks have a premium
Book to market
A “distress” premium?
These beat S&P beta
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33. Fma & French (1992) Results
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34. New Directions in Asset Pricing
Statistical methods for identifying factors
Style analysis
Economic modeling of risk
International Factors
Diversity of markets
Diversity of environments
Diversity of historical experiences
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