Correlation Between Sunnova Energy and Marvell Technology
Can any of the company-specific risk be diversified away by investing in both Sunnova Energy and Marvell Technology 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 Sunnova Energy and Marvell Technology into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sunnova Energy International and Marvell Technology Group, you can compare the effects of market volatilities on Sunnova Energy and Marvell Technology 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 Sunnova Energy with a short position of Marvell Technology. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sunnova Energy and Marvell Technology.
Diversification Opportunities for Sunnova Energy and Marvell Technology
-0.03 | Correlation Coefficient |
Good diversification
The 3 months correlation between Sunnova and Marvell is -0.03. Overlapping area represents the amount of risk that can be diversified away by holding Sunnova Energy International and Marvell Technology Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Marvell Technology and Sunnova 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 Sunnova Energy International are associated (or correlated) with Marvell Technology. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Marvell Technology has no effect on the direction of Sunnova Energy i.e., Sunnova Energy and Marvell Technology go up and down completely randomly.
Pair Corralation between Sunnova Energy and Marvell Technology
Given the investment horizon of 90 days Sunnova Energy International is expected to under-perform the Marvell Technology. In addition to that, Sunnova Energy is 2.95 times more volatile than Marvell Technology Group. It trades about -0.15 of its total potential returns per unit of risk. Marvell Technology Group is currently generating about -0.12 per unit of volatility. If you would invest 7,314 in Marvell Technology Group on January 31, 2024 and sell it today you would lose (439.00) from holding Marvell Technology Group or give up 6.0% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Sunnova Energy International vs. Marvell Technology Group
Performance |
Timeline |
Sunnova Energy Inter |
Marvell Technology |
Sunnova Energy and Marvell Technology Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Sunnova Energy and Marvell Technology
The main advantage of trading using opposite Sunnova Energy and Marvell Technology positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sunnova Energy position performs unexpectedly, Marvell Technology 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 Marvell Technology will offset losses from the drop in Marvell Technology's long position.Sunnova Energy vs. iSun Inc | Sunnova Energy vs. Emeren Group | Sunnova Energy vs. Tigo Energy | Sunnova Energy vs. SunPower |
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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