Correlation Between Step One and Energy Resources
Can any of the company-specific risk be diversified away by investing in both Step One and Energy Resources 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 Step One and Energy Resources into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Step One Clothing and Energy Resources, you can compare the effects of market volatilities on Step One and Energy Resources 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 Step One with a short position of Energy Resources. Check out your portfolio center. Please also check ongoing floating volatility patterns of Step One and Energy Resources.
Diversification Opportunities for Step One and Energy Resources
0.2 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Step and Energy is 0.2. Overlapping area represents the amount of risk that can be diversified away by holding Step One Clothing and Energy Resources in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Energy Resources and Step One 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 Step One Clothing are associated (or correlated) with Energy Resources. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Energy Resources has no effect on the direction of Step One i.e., Step One and Energy Resources go up and down completely randomly.
Pair Corralation between Step One and Energy Resources
Assuming the 90 days trading horizon Step One Clothing is expected to generate 0.35 times more return on investment than Energy Resources. However, Step One Clothing is 2.89 times less risky than Energy Resources. It trades about 0.08 of its potential returns per unit of risk. Energy Resources is currently generating about 0.02 per unit of risk. If you would invest 27.00 in Step One Clothing on December 2, 2024 and sell it today you would earn a total of 70.00 from holding Step One Clothing or generate 259.26% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Step One Clothing vs. Energy Resources
Performance |
Timeline |
Step One Clothing |
Energy Resources |
Step One and Energy Resources Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Step One and Energy Resources
The main advantage of trading using opposite Step One and Energy Resources positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Step One position performs unexpectedly, Energy Resources 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 Energy Resources will offset losses from the drop in Energy Resources' long position.Step One vs. Super Retail Group | Step One vs. A1 Investments Resources | Step One vs. Dexus Convenience Retail | Step One vs. Regal Investment |
Energy Resources vs. Australian Strategic Materials | Energy Resources vs. Queste Communications | Energy Resources vs. Carlton Investments | Energy Resources vs. Auctus Alternative Investments |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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