Correlation Between Ssangyong Materials and Seoul Food
Can any of the company-specific risk be diversified away by investing in both Ssangyong Materials and Seoul Food 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 Ssangyong Materials and Seoul Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ssangyong Materials Corp and Seoul Food Industrial, you can compare the effects of market volatilities on Ssangyong Materials and Seoul Food 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 Ssangyong Materials with a short position of Seoul Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ssangyong Materials and Seoul Food.
Diversification Opportunities for Ssangyong Materials and Seoul Food
0.41 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Ssangyong and Seoul is 0.41. Overlapping area represents the amount of risk that can be diversified away by holding Ssangyong Materials Corp and Seoul Food Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Seoul Food Industrial and Ssangyong Materials 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 Ssangyong Materials Corp are associated (or correlated) with Seoul Food. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Seoul Food Industrial has no effect on the direction of Ssangyong Materials i.e., Ssangyong Materials and Seoul Food go up and down completely randomly.
Pair Corralation between Ssangyong Materials and Seoul Food
Assuming the 90 days trading horizon Ssangyong Materials Corp is expected to under-perform the Seoul Food. In addition to that, Ssangyong Materials is 2.88 times more volatile than Seoul Food Industrial. It trades about -0.07 of its total potential returns per unit of risk. Seoul Food Industrial is currently generating about -0.07 per unit of volatility. If you would invest 15,100 in Seoul Food Industrial on December 26, 2024 and sell it today you would lose (600.00) from holding Seoul Food Industrial or give up 3.97% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Ssangyong Materials Corp vs. Seoul Food Industrial
Performance |
Timeline |
Ssangyong Materials Corp |
Seoul Food Industrial |
Ssangyong Materials and Seoul Food Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ssangyong Materials and Seoul Food
The main advantage of trading using opposite Ssangyong Materials and Seoul Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ssangyong Materials position performs unexpectedly, Seoul Food 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 Seoul Food will offset losses from the drop in Seoul Food's long position.Ssangyong Materials vs. SBI Investment KOREA | Ssangyong Materials vs. DoubleU Games Co | Ssangyong Materials vs. Eugene Investment Securities | Ssangyong Materials vs. KTB Investment Securities |
Seoul Food vs. E Investment Development | Seoul Food vs. Husteel | Seoul Food vs. Lindeman Asia Investment | Seoul Food vs. LB Investment |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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