Correlation Between Seoam Machinery and Nam Hwa
Can any of the company-specific risk be diversified away by investing in both Seoam Machinery and Nam Hwa 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 Seoam Machinery and Nam Hwa into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Seoam Machinery Industry and Nam Hwa Construction, you can compare the effects of market volatilities on Seoam Machinery and Nam Hwa 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 Seoam Machinery with a short position of Nam Hwa. Check out your portfolio center. Please also check ongoing floating volatility patterns of Seoam Machinery and Nam Hwa.
Diversification Opportunities for Seoam Machinery and Nam Hwa
0.28 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Seoam and Nam is 0.28. Overlapping area represents the amount of risk that can be diversified away by holding Seoam Machinery Industry and Nam Hwa Construction in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Nam Hwa Construction and Seoam 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 Seoam Machinery Industry are associated (or correlated) with Nam Hwa. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Nam Hwa Construction has no effect on the direction of Seoam Machinery i.e., Seoam Machinery and Nam Hwa go up and down completely randomly.
Pair Corralation between Seoam Machinery and Nam Hwa
Assuming the 90 days trading horizon Seoam Machinery Industry is expected to generate 0.96 times more return on investment than Nam Hwa. However, Seoam Machinery Industry is 1.04 times less risky than Nam Hwa. It trades about -0.03 of its potential returns per unit of risk. Nam Hwa Construction is currently generating about -0.06 per unit of risk. If you would invest 606,305 in Seoam Machinery Industry on September 18, 2024 and sell it today you would lose (242,805) from holding Seoam Machinery Industry or give up 40.05% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Seoam Machinery Industry vs. Nam Hwa Construction
Performance |
Timeline |
Seoam Machinery Industry |
Nam Hwa Construction |
Seoam Machinery and Nam Hwa Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Seoam Machinery and Nam Hwa
The main advantage of trading using opposite Seoam Machinery and Nam Hwa positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Seoam Machinery position performs unexpectedly, Nam Hwa 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 Nam Hwa will offset losses from the drop in Nam Hwa's long position.Seoam Machinery vs. Rainbow Robotics | Seoam Machinery vs. COWINTECH Co | Seoam Machinery vs. CS BEARING CoLtd | Seoam Machinery vs. Young Poong Precision |
Nam Hwa vs. Korea New Network | Nam Hwa vs. Solution Advanced Technology | Nam Hwa vs. Busan Industrial Co | Nam Hwa vs. Busan Ind |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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