Correlation Between Paccar and Samsung Electronics
Can any of the company-specific risk be diversified away by investing in both Paccar 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 Paccar and Samsung Electronics into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Paccar Inc and Samsung Electronics Co, you can compare the effects of market volatilities on Paccar 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 Paccar with a short position of Samsung Electronics. Check out your portfolio center. Please also check ongoing floating volatility patterns of Paccar and Samsung Electronics.
Diversification Opportunities for Paccar and Samsung Electronics
-0.88 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Paccar and Samsung is -0.88. Overlapping area represents the amount of risk that can be diversified away by holding Paccar Inc and Samsung Electronics Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Samsung Electronics and Paccar 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 Paccar Inc 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 Paccar i.e., Paccar and Samsung Electronics go up and down completely randomly.
Pair Corralation between Paccar and Samsung Electronics
Assuming the 90 days trading horizon Paccar Inc is expected to generate 0.95 times more return on investment than Samsung Electronics. However, Paccar Inc is 1.06 times less risky than Samsung Electronics. It trades about 0.09 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 6,217 in Paccar Inc on August 30, 2024 and sell it today you would earn a total of 5,343 from holding Paccar Inc or generate 85.94% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Significant |
| Accuracy | 96.78% |
| Values | Daily Returns |
Paccar Inc vs. Samsung Electronics Co
Performance |
| Timeline |
| Paccar Inc |
| Samsung Electronics |
Paccar and Samsung Electronics Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Paccar and Samsung Electronics
The main advantage of trading using opposite Paccar and Samsung Electronics positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Paccar 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.| Paccar vs. Infrastrutture Wireless Italiane | Paccar vs. Sabien Technology Group | Paccar vs. Tatton Asset Management | Paccar vs. Charter Communications Cl |
| Samsung Electronics vs. Ondine Biomedical | Samsung Electronics vs. Europa Metals | Samsung Electronics vs. Lendinvest PLC | Samsung Electronics vs. Neometals |
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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