US and Worlds Industries: An Analysis of Returns

1
US and Worlds IndustriesAn Analysis of Returns
Correlations

• Presented by New Millenium Capital
• Julie Bowser
• Tommy Kriengprarthana
• Klao Sanasen
• Courtenay Sturdivant
• February 26, 1999

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Agenda

• Project Overview
• Methodology
• Data Analysis
• Return Sharpe Ratio
• Volatility Correlation
• Regression Results
• Conclusion

3
Project Overview

• Task
• To examine equity returns of the worlds
  industries when the US market performs well and
  badly.

4
Methodology
5
Data Analysis
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Data Analysis
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Data Analysis
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Data Analysis
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Data Analysis
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Data Analysis
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Regression Results
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Regression Results
LOW Fractile (BEST)
Coefficients
T-stat
Constant
(0.04956)
-9.5
Diff(T-Bond Yield, 1)
(0.02031)
-1.7
Electricity, Lag 2
(0.14774)
-1.4
Defensive industry
ConsumerGoods, Lag 2
(0.44514)
-2.9
Defensive industry
3.6
Leisure, Lag 1
0.44555
Moves with market
Metals, Lag 1
(0.32460)
-2.6
Defensive industry
R-squared
40.32
Standard Error Est.
0.0266311
HIGH Fractile (BEST)
Coefficients
T-stat
Constant
0.06979
22.3
Diff(U.S. Inflation, 1)
(0.01736)
-2.0
Airlines, Lag 1
0.19020
2.8
High Beta, Moves with market
Chemicals, Lag 2
(0.17363)
-2.3
Health, Lag 2
0.16303
2.4
High Beta, Moves with market
Metals, Lag 1
(0.21803)
-3.0
R-squared
27.63
Standard Error Est.
0.0173702
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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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Questions?