Correlation Between Gyeongnam Steel and Vissem Electronics
Can any of the company-specific risk be diversified away by investing in both Gyeongnam Steel and Vissem Electronics 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 Gyeongnam Steel and Vissem Electronics into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Gyeongnam Steel Co and Vissem Electronics Co, you can compare the effects of market volatilities on Gyeongnam Steel and Vissem Electronics 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 Gyeongnam Steel with a short position of Vissem Electronics. Check out your portfolio center. Please also check ongoing floating volatility patterns of Gyeongnam Steel and Vissem Electronics.
Diversification Opportunities for Gyeongnam Steel and Vissem Electronics
-0.63 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Gyeongnam and Vissem is -0.63. Overlapping area represents the amount of risk that can be diversified away by holding Gyeongnam Steel Co and Vissem Electronics Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Vissem Electronics and Gyeongnam Steel 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 Gyeongnam Steel Co are associated (or correlated) with Vissem Electronics. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Vissem Electronics has no effect on the direction of Gyeongnam Steel i.e., Gyeongnam Steel and Vissem Electronics go up and down completely randomly.
Pair Corralation between Gyeongnam Steel and Vissem Electronics
Assuming the 90 days trading horizon Gyeongnam Steel Co is expected to generate 4.06 times more return on investment than Vissem Electronics. However, Gyeongnam Steel is 4.06 times more volatile than Vissem Electronics Co. It trades about 0.3 of its potential returns per unit of risk. Vissem Electronics Co is currently generating about -0.05 per unit of risk. If you would invest 275,423 in Gyeongnam Steel Co on October 4, 2024 and sell it today you would earn a total of 179,077 from holding Gyeongnam Steel Co or generate 65.02% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Gyeongnam Steel Co vs. Vissem Electronics Co
Performance |
Timeline |
Gyeongnam Steel |
Vissem Electronics |
Gyeongnam Steel and Vissem Electronics Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Gyeongnam Steel and Vissem Electronics
The main advantage of trading using opposite Gyeongnam Steel and Vissem Electronics positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Gyeongnam Steel position performs unexpectedly, Vissem Electronics 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 Vissem Electronics will offset losses from the drop in Vissem Electronics' long position.Gyeongnam Steel vs. Keum Kang Steel | Gyeongnam Steel vs. PJ Metal Co | Gyeongnam Steel vs. Jeil Steel Mfg | Gyeongnam Steel vs. Wonil Special Steel |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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