Correlation Between Shelton Emerging and Baillie Gifford
Can any of the company-specific risk be diversified away by investing in both Shelton Emerging and Baillie Gifford at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Shelton Emerging and Baillie Gifford into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shelton Emerging Markets and Baillie Gifford Eafe, you can compare the effects of market volatilities on Shelton Emerging and Baillie Gifford and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Shelton Emerging with a short position of Baillie Gifford. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shelton Emerging and Baillie Gifford.
Diversification Opportunities for Shelton Emerging and Baillie Gifford
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Shelton and Baillie is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding Shelton Emerging Markets and Baillie Gifford Eafe in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Baillie Gifford Eafe and Shelton Emerging is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Shelton Emerging Markets are associated (or correlated) with Baillie Gifford. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Baillie Gifford Eafe has no effect on the direction of Shelton Emerging i.e., Shelton Emerging and Baillie Gifford go up and down completely randomly.
Pair Corralation between Shelton Emerging and Baillie Gifford
Assuming the 90 days horizon Shelton Emerging Markets is expected to generate 0.96 times more return on investment than Baillie Gifford. However, Shelton Emerging Markets is 1.04 times less risky than Baillie Gifford. It trades about -0.01 of its potential returns per unit of risk. Baillie Gifford Eafe is currently generating about -0.01 per unit of risk. If you would invest 1,770 in Shelton Emerging Markets on September 14, 2024 and sell it today you would lose (14.00) from holding Shelton Emerging Markets or give up 0.79% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Shelton Emerging Markets vs. Baillie Gifford Eafe
Performance |
Timeline |
Shelton Emerging Markets |
Baillie Gifford Eafe |
Shelton Emerging and Baillie Gifford Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Shelton Emerging and Baillie Gifford
The main advantage of trading using opposite Shelton Emerging and Baillie Gifford positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shelton Emerging position performs unexpectedly, Baillie Gifford can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Baillie Gifford will offset losses from the drop in Baillie Gifford's long position.Shelton Emerging vs. Shelton Emerging Markets | Shelton Emerging vs. California Tax Free Income | Shelton Emerging vs. Shelton Funds | Shelton Emerging vs. Nasdaq 100 Index Fund |
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Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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