Correlation Between Lotte Chilsung and DRB Industrial
Can any of the company-specific risk be diversified away by investing in both Lotte Chilsung and DRB Industrial 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 Lotte Chilsung and DRB Industrial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lotte Chilsung Beverage and DRB Industrial Co, you can compare the effects of market volatilities on Lotte Chilsung and DRB Industrial 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 Lotte Chilsung with a short position of DRB Industrial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lotte Chilsung and DRB Industrial.
Diversification Opportunities for Lotte Chilsung and DRB Industrial
0.05 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Lotte and DRB is 0.05. Overlapping area represents the amount of risk that can be diversified away by holding Lotte Chilsung Beverage and DRB Industrial Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DRB Industrial and Lotte Chilsung 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 Lotte Chilsung Beverage are associated (or correlated) with DRB Industrial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DRB Industrial has no effect on the direction of Lotte Chilsung i.e., Lotte Chilsung and DRB Industrial go up and down completely randomly.
Pair Corralation between Lotte Chilsung and DRB Industrial
Assuming the 90 days trading horizon Lotte Chilsung Beverage is expected to under-perform the DRB Industrial. But the stock apears to be less risky and, when comparing its historical volatility, Lotte Chilsung Beverage is 1.19 times less risky than DRB Industrial. The stock trades about -0.12 of its potential returns per unit of risk. The DRB Industrial Co is currently generating about 0.24 of returns per unit of risk over similar time horizon. If you would invest 647,609 in DRB Industrial Co on October 11, 2024 and sell it today you would earn a total of 62,391 from holding DRB Industrial Co or generate 9.63% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Lotte Chilsung Beverage vs. DRB Industrial Co
Performance |
Timeline |
Lotte Chilsung Beverage |
DRB Industrial |
Lotte Chilsung and DRB Industrial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Lotte Chilsung and DRB Industrial
The main advantage of trading using opposite Lotte Chilsung and DRB Industrial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lotte Chilsung position performs unexpectedly, DRB Industrial 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 DRB Industrial will offset losses from the drop in DRB Industrial's long position.Lotte Chilsung vs. Daelim Trading Co | Lotte Chilsung vs. Taegu Broadcasting | Lotte Chilsung vs. ENF Technology Co | Lotte Chilsung vs. Hanjin Transportation Co |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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