Correlation Between MEDIANA CoLtd and Shinhan Financial
Can any of the company-specific risk be diversified away by investing in both MEDIANA CoLtd and Shinhan Financial 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 MEDIANA CoLtd and Shinhan Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between MEDIANA CoLtd and Shinhan Financial Group, you can compare the effects of market volatilities on MEDIANA CoLtd and Shinhan Financial 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 MEDIANA CoLtd with a short position of Shinhan Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of MEDIANA CoLtd and Shinhan Financial.
Diversification Opportunities for MEDIANA CoLtd and Shinhan Financial
-0.56 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between MEDIANA and Shinhan is -0.56. Overlapping area represents the amount of risk that can be diversified away by holding MEDIANA CoLtd and Shinhan Financial Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shinhan Financial and MEDIANA CoLtd 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 MEDIANA CoLtd are associated (or correlated) with Shinhan Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shinhan Financial has no effect on the direction of MEDIANA CoLtd i.e., MEDIANA CoLtd and Shinhan Financial go up and down completely randomly.
Pair Corralation between MEDIANA CoLtd and Shinhan Financial
Assuming the 90 days trading horizon MEDIANA CoLtd is expected to generate 1.15 times more return on investment than Shinhan Financial. However, MEDIANA CoLtd is 1.15 times more volatile than Shinhan Financial Group. It trades about -0.01 of its potential returns per unit of risk. Shinhan Financial Group is currently generating about -0.02 per unit of risk. If you would invest 488,500 in MEDIANA CoLtd on December 24, 2024 and sell it today you would lose (9,000) from holding MEDIANA CoLtd or give up 1.84% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
MEDIANA CoLtd vs. Shinhan Financial Group
Performance |
Timeline |
MEDIANA CoLtd |
Shinhan Financial |
MEDIANA CoLtd and Shinhan Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with MEDIANA CoLtd and Shinhan Financial
The main advantage of trading using opposite MEDIANA CoLtd and Shinhan Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if MEDIANA CoLtd position performs unexpectedly, Shinhan Financial 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 Shinhan Financial will offset losses from the drop in Shinhan Financial's long position.MEDIANA CoLtd vs. Daejung Chemicals Metals | MEDIANA CoLtd vs. Ssangyong Information Communication | MEDIANA CoLtd vs. Vitzro Tech Co | MEDIANA CoLtd vs. KMH Hitech Co |
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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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