Correlation Between SK TELECOM and Cars
Can any of the company-specific risk be diversified away by investing in both SK TELECOM and Cars 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 SK TELECOM and Cars into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SK TELECOM TDADR and Cars Inc, you can compare the effects of market volatilities on SK TELECOM and Cars 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 SK TELECOM with a short position of Cars. Check out your portfolio center. Please also check ongoing floating volatility patterns of SK TELECOM and Cars.
Diversification Opportunities for SK TELECOM and Cars
0.5 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between KMBA and Cars is 0.5. Overlapping area represents the amount of risk that can be diversified away by holding SK TELECOM TDADR and Cars Inc in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cars Inc and SK TELECOM 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 SK TELECOM TDADR are associated (or correlated) with Cars. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cars Inc has no effect on the direction of SK TELECOM i.e., SK TELECOM and Cars go up and down completely randomly.
Pair Corralation between SK TELECOM and Cars
Assuming the 90 days trading horizon SK TELECOM TDADR is expected to generate 1.6 times more return on investment than Cars. However, SK TELECOM is 1.6 times more volatile than Cars Inc. It trades about 0.0 of its potential returns per unit of risk. Cars Inc is currently generating about -0.01 per unit of risk. If you would invest 2,080 in SK TELECOM TDADR on September 13, 2024 and sell it today you would lose (20.00) from holding SK TELECOM TDADR or give up 0.96% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
SK TELECOM TDADR vs. Cars Inc
Performance |
Timeline |
SK TELECOM TDADR |
Cars Inc |
SK TELECOM and Cars Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SK TELECOM and Cars
The main advantage of trading using opposite SK TELECOM and Cars positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SK TELECOM position performs unexpectedly, Cars 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 Cars will offset losses from the drop in Cars' long position.The idea behind SK TELECOM TDADR and Cars Inc pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.Cars vs. Superior Plus Corp | Cars vs. SIVERS SEMICONDUCTORS AB | Cars vs. Norsk Hydro ASA | Cars vs. Reliance Steel Aluminum |
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 Valuation module to check real value of public entities based on technical and fundamental data.
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