Correlation Between Shinhan Inverse and Seoul Food
Can any of the company-specific risk be diversified away by investing in both Shinhan Inverse 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 Shinhan Inverse and Seoul Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shinhan Inverse Silver and Seoul Food Industrial, you can compare the effects of market volatilities on Shinhan Inverse 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 Shinhan Inverse with a short position of Seoul Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shinhan Inverse and Seoul Food.
Diversification Opportunities for Shinhan Inverse and Seoul Food
-0.4 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Shinhan and Seoul is -0.4. Overlapping area represents the amount of risk that can be diversified away by holding Shinhan Inverse Silver 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 Shinhan Inverse 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 Inverse Silver 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 Shinhan Inverse i.e., Shinhan Inverse and Seoul Food go up and down completely randomly.
Pair Corralation between Shinhan Inverse and Seoul Food
Assuming the 90 days trading horizon Shinhan Inverse Silver is expected to generate 1.64 times more return on investment than Seoul Food. However, Shinhan Inverse is 1.64 times more volatile than Seoul Food Industrial. It trades about -0.04 of its potential returns per unit of risk. Seoul Food Industrial is currently generating about -0.08 per unit of risk. If you would invest 446,500 in Shinhan Inverse Silver on October 16, 2024 and sell it today you would lose (92,000) from holding Shinhan Inverse Silver or give up 20.6% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 98.83% |
| Values | Daily Returns |
Shinhan Inverse Silver vs. Seoul Food Industrial
Performance |
| Timeline |
| Shinhan Inverse Silver |
| Seoul Food Industrial |
Shinhan Inverse and Seoul Food Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Shinhan Inverse and Seoul Food
The main advantage of trading using opposite Shinhan Inverse and Seoul Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shinhan Inverse 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.| Shinhan Inverse vs. Insung Information Co | Shinhan Inverse vs. Koryo Credit Information | Shinhan Inverse vs. Narae Nanotech Corp | Shinhan Inverse vs. Amogreentech Co |
| Seoul Food vs. Seoul Electronics Telecom | Seoul Food vs. Jeju Air Co | Seoul Food vs. KT Submarine Telecom | Seoul Food vs. Shinhan Inverse Silver |
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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