Correlation Between KT Submarine and SHINWON STRUCTION
Can any of the company-specific risk be diversified away by investing in both KT Submarine and SHINWON STRUCTION 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 KT Submarine and SHINWON STRUCTION into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between KT Submarine Co and SHINWON STRUCTION COMPANY, you can compare the effects of market volatilities on KT Submarine and SHINWON STRUCTION 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 KT Submarine with a short position of SHINWON STRUCTION. Check out your portfolio center. Please also check ongoing floating volatility patterns of KT Submarine and SHINWON STRUCTION.
Diversification Opportunities for KT Submarine and SHINWON STRUCTION
0.73 | Correlation Coefficient |
Poor diversification
The 3 months correlation between 060370 and SHINWON is 0.73. Overlapping area represents the amount of risk that can be diversified away by holding KT Submarine Co and SHINWON STRUCTION COMPANY in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SHINWON STRUCTION PANY and KT Submarine 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 KT Submarine Co are associated (or correlated) with SHINWON STRUCTION. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SHINWON STRUCTION PANY has no effect on the direction of KT Submarine i.e., KT Submarine and SHINWON STRUCTION go up and down completely randomly.
Pair Corralation between KT Submarine and SHINWON STRUCTION
Assuming the 90 days trading horizon KT Submarine Co is expected to under-perform the SHINWON STRUCTION. But the stock apears to be less risky and, when comparing its historical volatility, KT Submarine Co is 1.28 times less risky than SHINWON STRUCTION. The stock trades about -0.1 of its potential returns per unit of risk. The SHINWON STRUCTION COMPANY is currently generating about -0.06 of returns per unit of risk over similar time horizon. If you would invest 376,000 in SHINWON STRUCTION COMPANY on September 12, 2024 and sell it today you would lose (81,000) from holding SHINWON STRUCTION COMPANY or give up 21.54% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 98.31% |
Values | Daily Returns |
KT Submarine Co vs. SHINWON STRUCTION COMPANY
Performance |
Timeline |
KT Submarine |
SHINWON STRUCTION PANY |
KT Submarine and SHINWON STRUCTION Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with KT Submarine and SHINWON STRUCTION
The main advantage of trading using opposite KT Submarine and SHINWON STRUCTION positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if KT Submarine position performs unexpectedly, SHINWON STRUCTION 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 SHINWON STRUCTION will offset losses from the drop in SHINWON STRUCTION's long position.KT Submarine vs. Hanyang ENG Co | KT Submarine vs. Nam Hwa Construction | KT Submarine vs. Green Plus Co | KT Submarine vs. SEOHAN Const EngcoLtd |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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