Correlation Between Carnegie Clean and SOFI TECHNOLOGIES
Can any of the company-specific risk be diversified away by investing in both Carnegie Clean and SOFI 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 Carnegie Clean and SOFI TECHNOLOGIES into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Carnegie Clean Energy and SOFI TECHNOLOGIES, you can compare the effects of market volatilities on Carnegie Clean and SOFI 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 Carnegie Clean with a short position of SOFI TECHNOLOGIES. Check out your portfolio center. Please also check ongoing floating volatility patterns of Carnegie Clean and SOFI TECHNOLOGIES.
Diversification Opportunities for Carnegie Clean and SOFI TECHNOLOGIES
-0.03 | Correlation Coefficient |
Good diversification
The 3 months correlation between Carnegie and SOFI is -0.03. Overlapping area represents the amount of risk that can be diversified away by holding Carnegie Clean Energy and SOFI TECHNOLOGIES in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SOFI TECHNOLOGIES and Carnegie Clean 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 Carnegie Clean Energy are associated (or correlated) with SOFI TECHNOLOGIES. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SOFI TECHNOLOGIES has no effect on the direction of Carnegie Clean i.e., Carnegie Clean and SOFI TECHNOLOGIES go up and down completely randomly.
Pair Corralation between Carnegie Clean and SOFI TECHNOLOGIES
Assuming the 90 days trading horizon Carnegie Clean is expected to generate 26.7 times less return on investment than SOFI TECHNOLOGIES. In addition to that, Carnegie Clean is 1.11 times more volatile than SOFI TECHNOLOGIES. It trades about 0.01 of its total potential returns per unit of risk. SOFI TECHNOLOGIES is currently generating about 0.24 per unit of volatility. If you would invest 1,032 in SOFI TECHNOLOGIES on October 25, 2024 and sell it today you would earn a total of 640.00 from holding SOFI TECHNOLOGIES or generate 62.02% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Carnegie Clean Energy vs. SOFI TECHNOLOGIES
Performance |
Timeline |
Carnegie Clean Energy |
SOFI TECHNOLOGIES |
Carnegie Clean and SOFI TECHNOLOGIES Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Carnegie Clean and SOFI TECHNOLOGIES
The main advantage of trading using opposite Carnegie Clean and SOFI TECHNOLOGIES positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Carnegie Clean position performs unexpectedly, SOFI 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 SOFI TECHNOLOGIES will offset losses from the drop in SOFI TECHNOLOGIES's long position.Carnegie Clean vs. CARSALESCOM | Carnegie Clean vs. YATRA ONLINE DL 0001 | Carnegie Clean vs. Lamar Advertising | Carnegie Clean vs. Tencent Music Entertainment |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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