Correlation Between Choil Aluminum and Hanwha Life
Can any of the company-specific risk be diversified away by investing in both Choil Aluminum and Hanwha Life 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 Choil Aluminum and Hanwha Life into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Choil Aluminum and Hanwha Life Insurance, you can compare the effects of market volatilities on Choil Aluminum and Hanwha Life 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 Choil Aluminum with a short position of Hanwha Life. Check out your portfolio center. Please also check ongoing floating volatility patterns of Choil Aluminum and Hanwha Life.
Diversification Opportunities for Choil Aluminum and Hanwha Life
0.89 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Choil and Hanwha is 0.89. Overlapping area represents the amount of risk that can be diversified away by holding Choil Aluminum and Hanwha Life Insurance in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hanwha Life Insurance and Choil Aluminum 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 Choil Aluminum are associated (or correlated) with Hanwha Life. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hanwha Life Insurance has no effect on the direction of Choil Aluminum i.e., Choil Aluminum and Hanwha Life go up and down completely randomly.
Pair Corralation between Choil Aluminum and Hanwha Life
Assuming the 90 days trading horizon Choil Aluminum is expected to under-perform the Hanwha Life. In addition to that, Choil Aluminum is 1.41 times more volatile than Hanwha Life Insurance. It trades about -0.01 of its total potential returns per unit of risk. Hanwha Life Insurance is currently generating about 0.0 per unit of volatility. If you would invest 274,147 in Hanwha Life Insurance on October 16, 2024 and sell it today you would lose (24,147) from holding Hanwha Life Insurance or give up 8.81% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Choil Aluminum vs. Hanwha Life Insurance
Performance |
| Timeline |
| Choil Aluminum |
| Hanwha Life Insurance |
Choil Aluminum and Hanwha Life Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Choil Aluminum and Hanwha Life
The main advantage of trading using opposite Choil Aluminum and Hanwha Life positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Choil Aluminum position performs unexpectedly, Hanwha Life 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 Hanwha Life will offset losses from the drop in Hanwha Life's long position.| Choil Aluminum vs. ABCO Electronics Co | Choil Aluminum vs. Narae Nanotech Corp | Choil Aluminum vs. Shinil Electronics Co | Choil Aluminum vs. Raontech |
| Hanwha Life vs. DC Media Co | Hanwha Life vs. SAMG Entertainment Co | Hanwha Life vs. Choil Aluminum | Hanwha Life vs. Next Entertainment World |
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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