Correlation Between Sempio Foods and Seoul Food
Can any of the company-specific risk be diversified away by investing in both Sempio Foods 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 Sempio Foods and Seoul Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sempio Foods Co and Seoul Food Industrial, you can compare the effects of market volatilities on Sempio Foods 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 Sempio Foods with a short position of Seoul Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sempio Foods and Seoul Food.
Diversification Opportunities for Sempio Foods and Seoul Food
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Sempio and Seoul is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Sempio Foods Co 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 Sempio Foods 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 Sempio Foods Co 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 Sempio Foods i.e., Sempio Foods and Seoul Food go up and down completely randomly.
Pair Corralation between Sempio Foods and Seoul Food
Assuming the 90 days trading horizon Sempio Foods Co is expected to generate 2.21 times more return on investment than Seoul Food. However, Sempio Foods is 2.21 times more volatile than Seoul Food Industrial. It trades about -0.01 of its potential returns per unit of risk. Seoul Food Industrial is currently generating about -0.05 per unit of risk. If you would invest 3,470,000 in Sempio Foods Co on October 26, 2024 and sell it today you would lose (1,075,000) from holding Sempio Foods Co or give up 30.98% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Sempio Foods Co vs. Seoul Food Industrial
Performance |
| Timeline |
| Sempio Foods |
| Seoul Food Industrial |
Sempio Foods and Seoul Food Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Sempio Foods and Seoul Food
The main advantage of trading using opposite Sempio Foods and Seoul Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sempio Foods 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.| Sempio Foods vs. DB Financial Investment | Sempio Foods vs. Seoam Machinery Industry | Sempio Foods vs. Keyang Electric Machinery | Sempio Foods vs. Korea Investment Holdings |
| Seoul Food vs. Youngchang Chemical Co | Seoul Food vs. Formetal Co | Seoul Food vs. Kukil Metal Co | Seoul Food vs. Youngsin Metal Industrial |
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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