Correlation Between Automobile and Green Cross
Can any of the company-specific risk be diversified away by investing in both Automobile and Green Cross 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 Automobile and Green Cross into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Automobile Pc and Green Cross Medical, you can compare the effects of market volatilities on Automobile and Green Cross 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 Automobile with a short position of Green Cross. Check out your portfolio center. Please also check ongoing floating volatility patterns of Automobile and Green Cross.
Diversification Opportunities for Automobile and Green Cross
0.21 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Automobile and Green is 0.21. Overlapping area represents the amount of risk that can be diversified away by holding Automobile Pc and Green Cross Medical in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Green Cross Medical and Automobile 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 Automobile Pc are associated (or correlated) with Green Cross. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Green Cross Medical has no effect on the direction of Automobile i.e., Automobile and Green Cross go up and down completely randomly.
Pair Corralation between Automobile and Green Cross
Assuming the 90 days trading horizon Automobile Pc is expected to under-perform the Green Cross. But the stock apears to be less risky and, when comparing its historical volatility, Automobile Pc is 2.09 times less risky than Green Cross. The stock trades about -0.16 of its potential returns per unit of risk. The Green Cross Medical is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 367,000 in Green Cross Medical on November 3, 2024 and sell it today you would earn a total of 10,000 from holding Green Cross Medical or generate 2.72% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Automobile Pc vs. Green Cross Medical
Performance |
| Timeline |
| Automobile Pc |
| Green Cross Medical |
Automobile and Green Cross Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Automobile and Green Cross
The main advantage of trading using opposite Automobile and Green Cross positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Automobile position performs unexpectedly, Green Cross 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 Green Cross will offset losses from the drop in Green Cross' long position.| Automobile vs. Duksan Hi Metal | Automobile vs. Daejung Chemicals Metals | Automobile vs. iNtRON Biotechnology | Automobile vs. Eagon Industrial Co |
| Green Cross vs. Solus Advanced Materials | Green Cross vs. WONIK Materials CoLtd | Green Cross vs. LS Materials | Green Cross vs. SK Chemicals 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 Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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