Correlation Between World Energy and Cavanal Hill
Can any of the company-specific risk be diversified away by investing in both World Energy and Cavanal Hill 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 World Energy and Cavanal Hill into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between World Energy Fund and Cavanal Hill Hedged, you can compare the effects of market volatilities on World Energy and Cavanal Hill 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 World Energy with a short position of Cavanal Hill. Check out your portfolio center. Please also check ongoing floating volatility patterns of World Energy and Cavanal Hill.
Diversification Opportunities for World Energy and Cavanal Hill
0.9 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between World and Cavanal is 0.9. Overlapping area represents the amount of risk that can be diversified away by holding World Energy Fund and Cavanal Hill Hedged in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cavanal Hill Hedged and World Energy 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 World Energy Fund are associated (or correlated) with Cavanal Hill. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cavanal Hill Hedged has no effect on the direction of World Energy i.e., World Energy and Cavanal Hill go up and down completely randomly.
Pair Corralation between World Energy and Cavanal Hill
Assuming the 90 days horizon World Energy Fund is expected to generate 2.39 times more return on investment than Cavanal Hill. However, World Energy is 2.39 times more volatile than Cavanal Hill Hedged. It trades about 0.07 of its potential returns per unit of risk. Cavanal Hill Hedged is currently generating about 0.09 per unit of risk. If you would invest 1,170 in World Energy Fund on August 31, 2024 and sell it today you would earn a total of 355.00 from holding World Energy Fund or generate 30.34% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
World Energy Fund vs. Cavanal Hill Hedged
Performance |
| Timeline |
| World Energy |
| Cavanal Hill Hedged |
World Energy and Cavanal Hill Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with World Energy and Cavanal Hill
The main advantage of trading using opposite World Energy and Cavanal Hill positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if World Energy position performs unexpectedly, Cavanal Hill 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 Cavanal Hill will offset losses from the drop in Cavanal Hill's long position.| World Energy vs. Shelton Funds | World Energy vs. Qs Growth Fund | World Energy vs. Balanced Fund Investor | World Energy vs. Nasdaq 100 Index Fund |
| Cavanal Hill vs. Aam Select Income | Cavanal Hill vs. Arrow Managed Futures | Cavanal Hill vs. Scharf Global Opportunity | Cavanal Hill vs. Qs Large Cap |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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