Correlation Between Daekyung Machinery and Duksan Hi
Can any of the company-specific risk be diversified away by investing in both Daekyung Machinery and Duksan Hi 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 Daekyung Machinery and Duksan Hi into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Daekyung Machinery Engineering and Duksan Hi Metal, you can compare the effects of market volatilities on Daekyung Machinery and Duksan Hi 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 Daekyung Machinery with a short position of Duksan Hi. Check out your portfolio center. Please also check ongoing floating volatility patterns of Daekyung Machinery and Duksan Hi.
Diversification Opportunities for Daekyung Machinery and Duksan Hi
-0.4 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Daekyung and Duksan is -0.4. Overlapping area represents the amount of risk that can be diversified away by holding Daekyung Machinery Engineering and Duksan Hi Metal in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Duksan Hi Metal and Daekyung Machinery 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 Daekyung Machinery Engineering are associated (or correlated) with Duksan Hi. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Duksan Hi Metal has no effect on the direction of Daekyung Machinery i.e., Daekyung Machinery and Duksan Hi go up and down completely randomly.
Pair Corralation between Daekyung Machinery and Duksan Hi
Assuming the 90 days trading horizon Daekyung Machinery Engineering is expected to generate 1.07 times more return on investment than Duksan Hi. However, Daekyung Machinery is 1.07 times more volatile than Duksan Hi Metal. It trades about 0.17 of its potential returns per unit of risk. Duksan Hi Metal is currently generating about -0.13 per unit of risk. If you would invest 41,000 in Daekyung Machinery Engineering on September 16, 2024 and sell it today you would earn a total of 10,500 from holding Daekyung Machinery Engineering or generate 25.61% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 78.33% |
Values | Daily Returns |
Daekyung Machinery Engineering vs. Duksan Hi Metal
Performance |
Timeline |
Daekyung Machinery |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Good
Duksan Hi Metal |
Daekyung Machinery and Duksan Hi Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Daekyung Machinery and Duksan Hi
The main advantage of trading using opposite Daekyung Machinery and Duksan Hi positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Daekyung Machinery position performs unexpectedly, Duksan Hi 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 Duksan Hi will offset losses from the drop in Duksan Hi's long position.Daekyung Machinery vs. Duksan Hi Metal | Daekyung Machinery vs. Youngsin Metal Industrial | Daekyung Machinery vs. Kukil Metal Co | Daekyung Machinery vs. Daiyang Metal Co |
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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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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