Correlation Between KCC Engineering and DB Financial
Can any of the company-specific risk be diversified away by investing in both KCC Engineering and DB Financial 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 KCC Engineering and DB Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between KCC Engineering Construction and DB Financial Investment, you can compare the effects of market volatilities on KCC Engineering and DB Financial 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 KCC Engineering with a short position of DB Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of KCC Engineering and DB Financial.
Diversification Opportunities for KCC Engineering and DB Financial
0.75 | Correlation Coefficient |
Poor diversification
The 3 months correlation between KCC and 016610 is 0.75. Overlapping area represents the amount of risk that can be diversified away by holding KCC Engineering Construction and DB Financial Investment in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DB Financial Investment and KCC Engineering 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 KCC Engineering Construction are associated (or correlated) with DB Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DB Financial Investment has no effect on the direction of KCC Engineering i.e., KCC Engineering and DB Financial go up and down completely randomly.
Pair Corralation between KCC Engineering and DB Financial
Assuming the 90 days trading horizon KCC Engineering Construction is expected to generate 0.76 times more return on investment than DB Financial. However, KCC Engineering Construction is 1.31 times less risky than DB Financial. It trades about -0.01 of its potential returns per unit of risk. DB Financial Investment is currently generating about -0.05 per unit of risk. If you would invest 410,297 in KCC Engineering Construction on October 6, 2024 and sell it today you would lose (4,797) from holding KCC Engineering Construction or give up 1.17% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
KCC Engineering Construction vs. DB Financial Investment
Performance |
Timeline |
KCC Engineering Cons |
DB Financial Investment |
KCC Engineering and DB Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with KCC Engineering and DB Financial
The main advantage of trading using opposite KCC Engineering and DB Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if KCC Engineering position performs unexpectedly, DB Financial 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 DB Financial will offset losses from the drop in DB Financial's long position.KCC Engineering vs. Pureun Mutual Savings | KCC Engineering vs. Lotte Non Life Insurance | KCC Engineering vs. Aprogen Healthcare Games | KCC Engineering vs. Industrial Bank |
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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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