Correlation Between CG Hi and Dreamus
Can any of the company-specific risk be diversified away by investing in both CG Hi and Dreamus 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 CG Hi and Dreamus into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CG Hi Tech and Dreamus Company, you can compare the effects of market volatilities on CG Hi and Dreamus 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 CG Hi with a short position of Dreamus. Check out your portfolio center. Please also check ongoing floating volatility patterns of CG Hi and Dreamus.
Diversification Opportunities for CG Hi and Dreamus
Very weak diversification
The 3 months correlation between 264660 and Dreamus is 0.56. Overlapping area represents the amount of risk that can be diversified away by holding CG Hi Tech and Dreamus Company in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dreamus Company and CG Hi 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 CG Hi Tech are associated (or correlated) with Dreamus. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dreamus Company has no effect on the direction of CG Hi i.e., CG Hi and Dreamus go up and down completely randomly.
Pair Corralation between CG Hi and Dreamus
Assuming the 90 days trading horizon CG Hi Tech is expected to generate 0.61 times more return on investment than Dreamus. However, CG Hi Tech is 1.65 times less risky than Dreamus. It trades about -0.01 of its potential returns per unit of risk. Dreamus Company is currently generating about -0.02 per unit of risk. If you would invest 1,158,717 in CG Hi Tech on September 3, 2024 and sell it today you would lose (218,717) from holding CG Hi Tech or give up 18.88% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
CG Hi Tech vs. Dreamus Company
Performance |
| Timeline |
| CG Hi Tech |
| Dreamus Company |
CG Hi and Dreamus Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with CG Hi and Dreamus
The main advantage of trading using opposite CG Hi and Dreamus positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CG Hi position performs unexpectedly, Dreamus 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 Dreamus will offset losses from the drop in Dreamus' long position.The idea behind CG Hi Tech and Dreamus Company pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.| Dreamus vs. Seoul Electronics Telecom | Dreamus vs. Lotte Chilsung Beverage | Dreamus vs. Lion Chemtech Co | Dreamus vs. PH Tech Co |
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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