Correlation Between Samsung Publishing and Kukdo Chemical
Can any of the company-specific risk be diversified away by investing in both Samsung Publishing and Kukdo Chemical 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 Samsung Publishing and Kukdo Chemical into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Samsung Publishing Co and Kukdo Chemical Co, you can compare the effects of market volatilities on Samsung Publishing and Kukdo Chemical 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 Samsung Publishing with a short position of Kukdo Chemical. Check out your portfolio center. Please also check ongoing floating volatility patterns of Samsung Publishing and Kukdo Chemical.
Diversification Opportunities for Samsung Publishing and Kukdo Chemical
0.51 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Samsung and Kukdo is 0.51. Overlapping area represents the amount of risk that can be diversified away by holding Samsung Publishing Co and Kukdo Chemical Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kukdo Chemical and Samsung Publishing 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 Samsung Publishing Co are associated (or correlated) with Kukdo Chemical. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kukdo Chemical has no effect on the direction of Samsung Publishing i.e., Samsung Publishing and Kukdo Chemical go up and down completely randomly.
Pair Corralation between Samsung Publishing and Kukdo Chemical
Assuming the 90 days trading horizon Samsung Publishing Co is expected to generate 1.96 times more return on investment than Kukdo Chemical. However, Samsung Publishing is 1.96 times more volatile than Kukdo Chemical Co. It trades about -0.03 of its potential returns per unit of risk. Kukdo Chemical Co is currently generating about -0.12 per unit of risk. If you would invest 1,537,000 in Samsung Publishing Co on September 3, 2024 and sell it today you would lose (147,000) from holding Samsung Publishing Co or give up 9.56% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Samsung Publishing Co vs. Kukdo Chemical Co
Performance |
Timeline |
Samsung Publishing |
Kukdo Chemical |
Samsung Publishing and Kukdo Chemical Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Samsung Publishing and Kukdo Chemical
The main advantage of trading using opposite Samsung Publishing and Kukdo Chemical positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Samsung Publishing position performs unexpectedly, Kukdo Chemical 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 Kukdo Chemical will offset losses from the drop in Kukdo Chemical's long position.Samsung Publishing vs. Sangsin Energy Display | Samsung Publishing vs. Samsung Life Insurance | Samsung Publishing vs. Dongbang Transport Logistics | Samsung Publishing vs. InfoBank |
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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