Correlation Between Woodside Energy and Exela Technologies
Can any of the company-specific risk be diversified away by investing in both Woodside Energy and Exela Technologies 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 Woodside Energy and Exela Technologies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Woodside Energy Group and Exela Technologies, you can compare the effects of market volatilities on Woodside Energy and Exela Technologies 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 Woodside Energy with a short position of Exela Technologies. Check out your portfolio center. Please also check ongoing floating volatility patterns of Woodside Energy and Exela Technologies.
Diversification Opportunities for Woodside Energy and Exela Technologies
0.26 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Woodside and Exela is 0.26. Overlapping area represents the amount of risk that can be diversified away by holding Woodside Energy Group and Exela Technologies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Exela Technologies and Woodside 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 Woodside Energy Group are associated (or correlated) with Exela Technologies. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Exela Technologies has no effect on the direction of Woodside Energy i.e., Woodside Energy and Exela Technologies go up and down completely randomly.
Pair Corralation between Woodside Energy and Exela Technologies
Considering the 90-day investment horizon Woodside Energy Group is expected to generate 0.18 times more return on investment than Exela Technologies. However, Woodside Energy Group is 5.54 times less risky than Exela Technologies. It trades about -0.01 of its potential returns per unit of risk. Exela Technologies is currently generating about -0.01 per unit of risk. If you would invest 1,954 in Woodside Energy Group on March 9, 2024 and sell it today you would lose (122.00) from holding Woodside Energy Group or give up 6.24% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Woodside Energy Group vs. Exela Technologies
Performance |
Timeline |
Woodside Energy Group |
Exela Technologies |
Woodside Energy and Exela Technologies Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Woodside Energy and Exela Technologies
The main advantage of trading using opposite Woodside Energy and Exela Technologies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Woodside Energy position performs unexpectedly, Exela Technologies 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 Exela Technologies will offset losses from the drop in Exela Technologies' long position.Woodside Energy vs. SM Energy Co | Woodside Energy vs. Civitas Resources | Woodside Energy vs. Matador Resources | Woodside Energy vs. Chord Energy Corp |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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