Correlation Between SCI Information and Automobile
Can any of the company-specific risk be diversified away by investing in both SCI Information and Automobile 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 SCI Information and Automobile into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SCI Information Service and Automobile Pc, you can compare the effects of market volatilities on SCI Information and Automobile 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 SCI Information with a short position of Automobile. Check out your portfolio center. Please also check ongoing floating volatility patterns of SCI Information and Automobile.
Diversification Opportunities for SCI Information and Automobile
0.3 | Correlation Coefficient |
Weak diversification
The 3 months correlation between SCI and Automobile is 0.3. Overlapping area represents the amount of risk that can be diversified away by holding SCI Information Service and Automobile Pc in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Automobile Pc and SCI Information 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 SCI Information Service are associated (or correlated) with Automobile. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Automobile Pc has no effect on the direction of SCI Information i.e., SCI Information and Automobile go up and down completely randomly.
Pair Corralation between SCI Information and Automobile
Assuming the 90 days trading horizon SCI Information Service is expected to generate 0.98 times more return on investment than Automobile. However, SCI Information Service is 1.02 times less risky than Automobile. It trades about -0.07 of its potential returns per unit of risk. Automobile Pc is currently generating about -0.18 per unit of risk. If you would invest 226,000 in SCI Information Service on December 25, 2024 and sell it today you would lose (15,500) from holding SCI Information Service or give up 6.86% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 98.25% |
Values | Daily Returns |
SCI Information Service vs. Automobile Pc
Performance |
Timeline |
SCI Information Service |
Automobile Pc |
SCI Information and Automobile Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SCI Information and Automobile
The main advantage of trading using opposite SCI Information and Automobile positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SCI Information position performs unexpectedly, Automobile 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 Automobile will offset losses from the drop in Automobile's long position.SCI Information vs. Taegu Broadcasting | SCI Information vs. Daewon Media Co | SCI Information vs. Barunson Entertainment Arts | SCI Information vs. SM Entertainment Co |
Automobile vs. LG Display Co | Automobile vs. FNSTech Co | Automobile vs. Kangstem Biotech Co | Automobile vs. Spolytech Co |
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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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