Correlation Between Shinhan Financial and New Ulm
Can any of the company-specific risk be diversified away by investing in both Shinhan Financial and New Ulm 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 Shinhan Financial and New Ulm into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shinhan Financial Group and New Ulm Telecom, you can compare the effects of market volatilities on Shinhan Financial and New Ulm 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 Shinhan Financial with a short position of New Ulm. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shinhan Financial and New Ulm.
Diversification Opportunities for Shinhan Financial and New Ulm
-0.64 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Shinhan and New is -0.64. Overlapping area represents the amount of risk that can be diversified away by holding Shinhan Financial Group and New Ulm Telecom in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on New Ulm Telecom and Shinhan Financial 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 Shinhan Financial Group are associated (or correlated) with New Ulm. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of New Ulm Telecom has no effect on the direction of Shinhan Financial i.e., Shinhan Financial and New Ulm go up and down completely randomly.
Pair Corralation between Shinhan Financial and New Ulm
Considering the 90-day investment horizon Shinhan Financial Group is expected to generate 0.44 times more return on investment than New Ulm. However, Shinhan Financial Group is 2.26 times less risky than New Ulm. It trades about -0.05 of its potential returns per unit of risk. New Ulm Telecom is currently generating about -0.05 per unit of risk. If you would invest 4,082 in Shinhan Financial Group on August 25, 2024 and sell it today you would lose (107.00) from holding Shinhan Financial Group or give up 2.62% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 95.65% |
| Values | Daily Returns |
Shinhan Financial Group vs. New Ulm Telecom
Performance |
| Timeline |
| Shinhan Financial |
| New Ulm Telecom |
Shinhan Financial and New Ulm Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Shinhan Financial and New Ulm
The main advantage of trading using opposite Shinhan Financial and New Ulm positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shinhan Financial position performs unexpectedly, New Ulm 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 New Ulm will offset losses from the drop in New Ulm's long position.| Shinhan Financial vs. Banco Santander Brasil | Shinhan Financial vs. CrossFirst Bankshares | Shinhan Financial vs. Banco Bradesco SA | Shinhan Financial vs. CF Bankshares |
| New Ulm vs. KORE Group Holdings | New Ulm vs. Grupo Televisa SAB | New Ulm vs. ATT Inc | New Ulm vs. Verizon Communications |
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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