Correlation Between Sumitomo Rubber and Gildan Activewear
Can any of the company-specific risk be diversified away by investing in both Sumitomo Rubber and Gildan Activewear 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 Sumitomo Rubber and Gildan Activewear into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sumitomo Rubber Industries and Gildan Activewear, you can compare the effects of market volatilities on Sumitomo Rubber and Gildan Activewear 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 Sumitomo Rubber with a short position of Gildan Activewear. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sumitomo Rubber and Gildan Activewear.
Diversification Opportunities for Sumitomo Rubber and Gildan Activewear
0.63 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Sumitomo and Gildan is 0.63. Overlapping area represents the amount of risk that can be diversified away by holding Sumitomo Rubber Industries and Gildan Activewear in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Gildan Activewear and Sumitomo Rubber 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 Sumitomo Rubber Industries are associated (or correlated) with Gildan Activewear. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Gildan Activewear has no effect on the direction of Sumitomo Rubber i.e., Sumitomo Rubber and Gildan Activewear go up and down completely randomly.
Pair Corralation between Sumitomo Rubber and Gildan Activewear
Assuming the 90 days horizon Sumitomo Rubber Industries is expected to generate 2.15 times more return on investment than Gildan Activewear. However, Sumitomo Rubber is 2.15 times more volatile than Gildan Activewear. It trades about 0.11 of its potential returns per unit of risk. Gildan Activewear is currently generating about 0.21 per unit of risk. If you would invest 910.00 in Sumitomo Rubber Industries on September 14, 2024 and sell it today you would earn a total of 140.00 from holding Sumitomo Rubber Industries or generate 15.38% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 98.46% |
Values | Daily Returns |
Sumitomo Rubber Industries vs. Gildan Activewear
Performance |
Timeline |
Sumitomo Rubber Indu |
Gildan Activewear |
Sumitomo Rubber and Gildan Activewear Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Sumitomo Rubber and Gildan Activewear
The main advantage of trading using opposite Sumitomo Rubber and Gildan Activewear positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sumitomo Rubber position performs unexpectedly, Gildan Activewear 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 Gildan Activewear will offset losses from the drop in Gildan Activewear's long position.Sumitomo Rubber vs. Superior Plus Corp | Sumitomo Rubber vs. NMI Holdings | Sumitomo Rubber vs. SIVERS SEMICONDUCTORS AB | Sumitomo Rubber vs. NorAm Drilling AS |
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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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