1. US and World’s Industries: An Analysis of Returns & Correlations
Presented by New Millenium Capital
Julie Bowser
Tommy Kriengprarthana
Klao Sanasen
Courtenay Sturdivant
February 26, 1999

2. Agenda Project Overview Methodology Data Analysis Regression Results
Return – Sharpe Ratio
Volatility – Correlation
Regression Results
Conclusion
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3. Project Overview Task:
To examine equity returns of the world’s industries when the US market performs well and badly.
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4. Methodology
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5. Data Analysis
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6. Data Analysis
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7. Data Analysis
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8. Data Analysis
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9. Data Analysis
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10. Data Analysis
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11. Regression Results
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12. Regression Results LOW Fractile (BEST) Coefficients T-stat Constant
( )
-9.5
Diff(T-Bond Yield, 1)
( )
-1.7
Electricity, Lag 2
( )
-1.4
Defensive industry
ConsumerGoods, Lag 2
( )
-2.9
Defensive industry
Leisure, Lag 1
3.6
Moves with market
Metals, Lag 1
( )
-2.6
Defensive industry
R-squared
40.32%
Standard Error Est.
HIGH Fractile (BEST)
Coefficients
T-stat
Constant
22.3
Diff(U.S. Inflation, 1)
( )
-2.0
Airlines, Lag 1
2.8
High Beta, Moves with market
Chemicals, Lag 2
( )
-2.3
Health, Lag 2
2.4
High Beta, Moves with market
Metals, Lag 1
( )
-3.0
R-squared
27.63%
Standard Error Est.
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13. Conclusion Betas/coefficients change over time and market.
HIGH fractile - inflation more significant
LOW fractile - Bond yields
Most important variable for predicting industry returns is the bond yields.
Strategy for investing in different market situations.
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14. Questions?
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