Correlation Between Mastercard and SoFi Technologies
Can any of the company-specific risk be diversified away by investing in both Mastercard 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 Mastercard and SoFi Technologies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mastercard and SoFi Technologies, you can compare the effects of market volatilities on Mastercard 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 Mastercard with a short position of SoFi Technologies. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mastercard and SoFi Technologies.
Diversification Opportunities for Mastercard and SoFi Technologies
0.07 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Mastercard and SoFi is 0.07. Overlapping area represents the amount of risk that can be diversified away by holding Mastercard and SoFi Technologies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SoFi Technologies and Mastercard 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 Mastercard 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 Mastercard i.e., Mastercard and SoFi Technologies go up and down completely randomly.
Pair Corralation between Mastercard and SoFi Technologies
Allowing for the 90-day total investment horizon Mastercard is expected to generate 0.28 times more return on investment than SoFi Technologies. However, Mastercard is 3.61 times less risky than SoFi Technologies. It trades about 0.09 of its potential returns per unit of risk. SoFi Technologies is currently generating about -0.07 per unit of risk. If you would invest 52,476 in Mastercard on December 29, 2024 and sell it today you would earn a total of 3,281 from holding Mastercard or generate 6.25% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Mastercard vs. SoFi Technologies
Performance |
Timeline |
Mastercard |
SoFi Technologies |
Mastercard and SoFi Technologies Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Mastercard and SoFi Technologies
The main advantage of trading using opposite Mastercard and SoFi Technologies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mastercard 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' long position.Mastercard vs. American Express | Mastercard vs. Capital One Financial | Mastercard vs. Upstart Holdings | Mastercard vs. Ally Financial |
SoFi Technologies vs. Upstart Holdings | SoFi Technologies vs. Affirm Holdings | SoFi Technologies vs. Lucid Group | SoFi Technologies vs. Palantir Technologies Class |
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 Valuation module to check real value of public entities based on technical and fundamental data.
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