Correlation Between Energy Fund and Commodities Strategy
Can any of the company-specific risk be diversified away by investing in both Energy Fund and Commodities Strategy 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 Energy Fund and Commodities Strategy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Energy Fund Investor and Commodities Strategy Fund, you can compare the effects of market volatilities on Energy Fund and Commodities Strategy 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 Energy Fund with a short position of Commodities Strategy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Energy Fund and Commodities Strategy.
Diversification Opportunities for Energy Fund and Commodities Strategy
-0.01 | Correlation Coefficient |
Good diversification
The 3 months correlation between Energy and Commodities is -0.01. Overlapping area represents the amount of risk that can be diversified away by holding Energy Fund Investor and Commodities Strategy Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Commodities Strategy and Energy Fund 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 Energy Fund Investor are associated (or correlated) with Commodities Strategy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Commodities Strategy has no effect on the direction of Energy Fund i.e., Energy Fund and Commodities Strategy go up and down completely randomly.
Pair Corralation between Energy Fund and Commodities Strategy
Assuming the 90 days horizon Energy Fund is expected to generate 1.0 times less return on investment than Commodities Strategy. In addition to that, Energy Fund is 1.31 times more volatile than Commodities Strategy Fund. It trades about 0.29 of its total potential returns per unit of risk. Commodities Strategy Fund is currently generating about 0.39 per unit of volatility. If you would invest 1,641 in Commodities Strategy Fund on October 17, 2024 and sell it today you would earn a total of 103.00 from holding Commodities Strategy Fund or generate 6.28% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Energy Fund Investor vs. Commodities Strategy Fund
Performance |
Timeline |
Energy Fund Investor |
Commodities Strategy |
Energy Fund and Commodities Strategy Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Energy Fund and Commodities Strategy
The main advantage of trading using opposite Energy Fund and Commodities Strategy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Energy Fund position performs unexpectedly, Commodities Strategy 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 Commodities Strategy will offset losses from the drop in Commodities Strategy's long position.Energy Fund vs. Energy Services Fund | Energy Fund vs. Basic Materials Fund | Energy Fund vs. Health Care Fund | Energy Fund vs. Precious Metals 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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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