Correlation Between DC Media and Samsung Electronics
Can any of the company-specific risk be diversified away by investing in both DC Media and Samsung Electronics 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 DC Media and Samsung Electronics into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DC Media Co and Samsung Electronics Co, you can compare the effects of market volatilities on DC Media and Samsung Electronics 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 DC Media with a short position of Samsung Electronics. Check out your portfolio center. Please also check ongoing floating volatility patterns of DC Media and Samsung Electronics.
Diversification Opportunities for DC Media and Samsung Electronics
-0.19 | Correlation Coefficient |
Good diversification
The 3 months correlation between 263720 and Samsung is -0.19. Overlapping area represents the amount of risk that can be diversified away by holding DC Media Co and Samsung Electronics Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Samsung Electronics and DC Media 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 DC Media Co are associated (or correlated) with Samsung Electronics. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Samsung Electronics has no effect on the direction of DC Media i.e., DC Media and Samsung Electronics go up and down completely randomly.
Pair Corralation between DC Media and Samsung Electronics
Assuming the 90 days trading horizon DC Media Co is expected to generate 2.72 times more return on investment than Samsung Electronics. However, DC Media is 2.72 times more volatile than Samsung Electronics Co. It trades about 0.01 of its potential returns per unit of risk. Samsung Electronics Co is currently generating about -0.01 per unit of risk. If you would invest 2,370,000 in DC Media Co on September 12, 2024 and sell it today you would lose (536,000) from holding DC Media Co or give up 22.62% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
DC Media Co vs. Samsung Electronics Co
Performance |
Timeline |
DC Media |
Samsung Electronics |
DC Media and Samsung Electronics Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with DC Media and Samsung Electronics
The main advantage of trading using opposite DC Media and Samsung Electronics positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DC Media position performs unexpectedly, Samsung Electronics 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 Electronics will offset losses from the drop in Samsung Electronics' long position.DC Media vs. Samsung Special Purpose | DC Media vs. Solution Advanced Technology | DC Media vs. Busan Industrial Co | DC Media vs. Busan Ind |
Samsung Electronics vs. LG Corp | Samsung Electronics vs. Solution Advanced Technology | Samsung Electronics vs. Busan Industrial Co | Samsung Electronics vs. Busan Ind |
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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