Correlation Between Dragonfly and Samsung SDI
Can any of the company-specific risk be diversified away by investing in both Dragonfly and Samsung SDI 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 Dragonfly and Samsung SDI into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dragonfly GF Co and Samsung SDI, you can compare the effects of market volatilities on Dragonfly and Samsung SDI 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 Dragonfly with a short position of Samsung SDI. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dragonfly and Samsung SDI.
Diversification Opportunities for Dragonfly and Samsung SDI
0.77 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Dragonfly and Samsung is 0.77. Overlapping area represents the amount of risk that can be diversified away by holding Dragonfly GF Co and Samsung SDI in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Samsung SDI and Dragonfly 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 Dragonfly GF Co are associated (or correlated) with Samsung SDI. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Samsung SDI has no effect on the direction of Dragonfly i.e., Dragonfly and Samsung SDI go up and down completely randomly.
Pair Corralation between Dragonfly and Samsung SDI
Assuming the 90 days trading horizon Dragonfly GF Co is expected to generate 0.64 times more return on investment than Samsung SDI. However, Dragonfly GF Co is 1.57 times less risky than Samsung SDI. It trades about -0.22 of its potential returns per unit of risk. Samsung SDI is currently generating about -0.27 per unit of risk. If you would invest 24,700 in Dragonfly GF Co on August 31, 2024 and sell it today you would lose (1,000.00) from holding Dragonfly GF Co or give up 4.05% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 36.36% |
| Values | Daily Returns |
Dragonfly GF Co vs. Samsung SDI
Performance |
| Timeline |
| Dragonfly GF |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
| Samsung SDI |
Dragonfly and Samsung SDI Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Dragonfly and Samsung SDI
The main advantage of trading using opposite Dragonfly and Samsung SDI positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dragonfly position performs unexpectedly, Samsung SDI 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 Samsung SDI will offset losses from the drop in Samsung SDI's long position.| Dragonfly vs. Samsung Electronics Co | Dragonfly vs. Samsung Electronics Co | Dragonfly vs. LG Energy Solution | Dragonfly vs. SK Hynix |
| Samsung SDI vs. Dongsin Engineering Construction | Samsung SDI vs. Doosan Fuel Cell | Samsung SDI vs. Daishin Balance 1 | Samsung SDI vs. Total Soft Bank |
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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