Correlation Between Dongwoo Farm and Kyeryong Construction
Can any of the company-specific risk be diversified away by investing in both Dongwoo Farm and Kyeryong Construction 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 Dongwoo Farm and Kyeryong Construction into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dongwoo Farm To and Kyeryong Construction Industrial, you can compare the effects of market volatilities on Dongwoo Farm and Kyeryong Construction 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 Dongwoo Farm with a short position of Kyeryong Construction. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dongwoo Farm and Kyeryong Construction.
Diversification Opportunities for Dongwoo Farm and Kyeryong Construction
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Dongwoo and Kyeryong is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Dongwoo Farm To and Kyeryong Construction Industri in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kyeryong Construction and Dongwoo Farm 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 Dongwoo Farm To are associated (or correlated) with Kyeryong Construction. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kyeryong Construction has no effect on the direction of Dongwoo Farm i.e., Dongwoo Farm and Kyeryong Construction go up and down completely randomly.
Pair Corralation between Dongwoo Farm and Kyeryong Construction
Assuming the 90 days trading horizon Dongwoo Farm To is expected to generate 0.91 times more return on investment than Kyeryong Construction. However, Dongwoo Farm To is 1.1 times less risky than Kyeryong Construction. It trades about -0.04 of its potential returns per unit of risk. Kyeryong Construction Industrial is currently generating about -0.09 per unit of risk. If you would invest 199,858 in Dongwoo Farm To on October 8, 2024 and sell it today you would lose (6,058) from holding Dongwoo Farm To or give up 3.03% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Dongwoo Farm To vs. Kyeryong Construction Industri
Performance |
Timeline |
Dongwoo Farm To |
Kyeryong Construction |
Dongwoo Farm and Kyeryong Construction Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Dongwoo Farm and Kyeryong Construction
The main advantage of trading using opposite Dongwoo Farm and Kyeryong Construction positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dongwoo Farm position performs unexpectedly, Kyeryong Construction 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 Kyeryong Construction will offset losses from the drop in Kyeryong Construction's long position.Dongwoo Farm vs. Busan Industrial Co | Dongwoo Farm vs. UNISEM Co | Dongwoo Farm vs. RPBio Inc | Dongwoo Farm vs. Finebesteel |
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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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