Correlation Between SoFi Technologies and Eneos Holdings
Can any of the company-specific risk be diversified away by investing in both SoFi Technologies and Eneos Holdings 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 SoFi Technologies and Eneos Holdings into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SoFi Technologies and Eneos Holdings ADR, you can compare the effects of market volatilities on SoFi Technologies and Eneos Holdings 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 SoFi Technologies with a short position of Eneos Holdings. Check out your portfolio center. Please also check ongoing floating volatility patterns of SoFi Technologies and Eneos Holdings.
Diversification Opportunities for SoFi Technologies and Eneos Holdings
-0.35 | Correlation Coefficient |
Very good diversification
The 3 months correlation between SoFi and Eneos is -0.35. Overlapping area represents the amount of risk that can be diversified away by holding SoFi Technologies and Eneos Holdings ADR in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Eneos Holdings ADR and SoFi Technologies 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 SoFi Technologies are associated (or correlated) with Eneos Holdings. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Eneos Holdings ADR has no effect on the direction of SoFi Technologies i.e., SoFi Technologies and Eneos Holdings go up and down completely randomly.
Pair Corralation between SoFi Technologies and Eneos Holdings
Given the investment horizon of 90 days SoFi Technologies is expected to generate 0.52 times more return on investment than Eneos Holdings. However, SoFi Technologies is 1.93 times less risky than Eneos Holdings. It trades about 0.36 of its potential returns per unit of risk. Eneos Holdings ADR is currently generating about 0.02 per unit of risk. If you would invest 806.00 in SoFi Technologies on September 16, 2024 and sell it today you would earn a total of 819.00 from holding SoFi Technologies or generate 101.61% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
SoFi Technologies vs. Eneos Holdings ADR
Performance |
Timeline |
SoFi Technologies |
Eneos Holdings ADR |
SoFi Technologies and Eneos Holdings Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SoFi Technologies and Eneos Holdings
The main advantage of trading using opposite SoFi Technologies and Eneos Holdings positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SoFi Technologies position performs unexpectedly, Eneos Holdings 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 Eneos Holdings will offset losses from the drop in Eneos Holdings' long position.SoFi Technologies vs. Visa Class A | SoFi Technologies vs. PayPal Holdings | SoFi Technologies vs. Upstart Holdings | SoFi Technologies vs. Mastercard |
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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