Correlation Between Daejung Chemicals and Sung Bo
Can any of the company-specific risk be diversified away by investing in both Daejung Chemicals and Sung Bo 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 Daejung Chemicals and Sung Bo into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Daejung Chemicals Metals and Sung Bo Chemicals, you can compare the effects of market volatilities on Daejung Chemicals and Sung Bo 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 Daejung Chemicals with a short position of Sung Bo. Check out your portfolio center. Please also check ongoing floating volatility patterns of Daejung Chemicals and Sung Bo.
Diversification Opportunities for Daejung Chemicals and Sung Bo
0.91 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Daejung and Sung is 0.91. Overlapping area represents the amount of risk that can be diversified away by holding Daejung Chemicals Metals and Sung Bo Chemicals in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sung Bo Chemicals and Daejung Chemicals 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 Daejung Chemicals Metals are associated (or correlated) with Sung Bo. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sung Bo Chemicals has no effect on the direction of Daejung Chemicals i.e., Daejung Chemicals and Sung Bo go up and down completely randomly.
Pair Corralation between Daejung Chemicals and Sung Bo
Assuming the 90 days trading horizon Daejung Chemicals Metals is expected to under-perform the Sung Bo. In addition to that, Daejung Chemicals is 3.17 times more volatile than Sung Bo Chemicals. It trades about -0.09 of its total potential returns per unit of risk. Sung Bo Chemicals is currently generating about -0.04 per unit of volatility. If you would invest 262,500 in Sung Bo Chemicals on September 28, 2024 and sell it today you would lose (4,000) from holding Sung Bo Chemicals or give up 1.52% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Daejung Chemicals Metals vs. Sung Bo Chemicals
Performance |
Timeline |
Daejung Chemicals Metals |
Sung Bo Chemicals |
Daejung Chemicals and Sung Bo Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Daejung Chemicals and Sung Bo
The main advantage of trading using opposite Daejung Chemicals and Sung Bo positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Daejung Chemicals position performs unexpectedly, Sung Bo 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 Sung Bo will offset losses from the drop in Sung Bo's long position.Daejung Chemicals vs. Keyang Electric Machinery | Daejung Chemicals vs. Air Busan Co | Daejung Chemicals vs. ITM Semiconductor Co | Daejung Chemicals vs. Cloud Air CoLtd |
Sung Bo vs. DataSolution | Sung Bo vs. Daishin Information Communications | Sung Bo vs. Ssangyong Information Communication | Sung Bo vs. Puloon Technology |
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 Optimization module to compute new portfolio that will generate highest expected return given your specified tolerance for risk.
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