Correlation Between Sungdo Engineering and Shinsegae Food
Can any of the company-specific risk be diversified away by investing in both Sungdo Engineering and Shinsegae 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 Sungdo Engineering and Shinsegae Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sungdo Engineering Construction and Shinsegae Food, you can compare the effects of market volatilities on Sungdo Engineering and Shinsegae 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 Sungdo Engineering with a short position of Shinsegae Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sungdo Engineering and Shinsegae Food.
Diversification Opportunities for Sungdo Engineering and Shinsegae Food
0.57 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Sungdo and Shinsegae is 0.57. Overlapping area represents the amount of risk that can be diversified away by holding Sungdo Engineering Constructio and Shinsegae Food in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shinsegae Food and Sungdo Engineering 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 Sungdo Engineering Construction are associated (or correlated) with Shinsegae Food. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shinsegae Food has no effect on the direction of Sungdo Engineering i.e., Sungdo Engineering and Shinsegae Food go up and down completely randomly.
Pair Corralation between Sungdo Engineering and Shinsegae Food
Assuming the 90 days trading horizon Sungdo Engineering Construction is expected to generate 1.35 times more return on investment than Shinsegae Food. However, Sungdo Engineering is 1.35 times more volatile than Shinsegae Food. It trades about 0.0 of its potential returns per unit of risk. Shinsegae Food is currently generating about -0.03 per unit of risk. If you would invest 457,491 in Sungdo Engineering Construction on October 23, 2024 and sell it today you would lose (47,491) from holding Sungdo Engineering Construction or give up 10.38% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Sungdo Engineering Constructio vs. Shinsegae Food
Performance |
Timeline |
Sungdo Engineering |
Shinsegae Food |
Sungdo Engineering and Shinsegae Food Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Sungdo Engineering and Shinsegae Food
The main advantage of trading using opposite Sungdo Engineering and Shinsegae Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sungdo Engineering position performs unexpectedly, Shinsegae 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 Shinsegae Food will offset losses from the drop in Shinsegae Food's long position.Sungdo Engineering vs. InnoTherapy | Sungdo Engineering vs. FoodNamoo | Sungdo Engineering vs. Sajo Seafood | Sungdo Engineering vs. Organic Special Pet |
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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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