Correlation Between Alcoa Corp and Kawasaki Heavy
Can any of the company-specific risk be diversified away by investing in both Alcoa Corp and Kawasaki Heavy 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 Alcoa Corp and Kawasaki Heavy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Alcoa Corp and Kawasaki Heavy Industries, you can compare the effects of market volatilities on Alcoa Corp and Kawasaki Heavy 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 Alcoa Corp with a short position of Kawasaki Heavy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Alcoa Corp and Kawasaki Heavy.
Diversification Opportunities for Alcoa Corp and Kawasaki Heavy
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Alcoa and Kawasaki is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Alcoa Corp and Kawasaki Heavy Industries in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kawasaki Heavy Industries and Alcoa Corp 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 Alcoa Corp are associated (or correlated) with Kawasaki Heavy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kawasaki Heavy Industries has no effect on the direction of Alcoa Corp i.e., Alcoa Corp and Kawasaki Heavy go up and down completely randomly.
Pair Corralation between Alcoa Corp and Kawasaki Heavy
Allowing for the 90-day total investment horizon Alcoa Corp is expected to generate 0.95 times more return on investment than Kawasaki Heavy. However, Alcoa Corp is 1.05 times less risky than Kawasaki Heavy. It trades about 0.23 of its potential returns per unit of risk. Kawasaki Heavy Industries is currently generating about 0.09 per unit of risk. If you would invest 3,020 in Alcoa Corp on September 5, 2024 and sell it today you would earn a total of 1,570 from holding Alcoa Corp or generate 51.99% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Alcoa Corp vs. Kawasaki Heavy Industries
Performance |
Timeline |
Alcoa Corp |
Kawasaki Heavy Industries |
Alcoa Corp and Kawasaki Heavy Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Alcoa Corp and Kawasaki Heavy
The main advantage of trading using opposite Alcoa Corp and Kawasaki Heavy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Alcoa Corp position performs unexpectedly, Kawasaki Heavy 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 Kawasaki Heavy will offset losses from the drop in Kawasaki Heavy's long position.Alcoa Corp vs. Constellium Nv | Alcoa Corp vs. Century Aluminum | Alcoa Corp vs. China Hongqiao Group | Alcoa Corp vs. Kaiser Aluminum |
Kawasaki Heavy vs. Dear Cashmere Holding | Kawasaki Heavy vs. Goff Corp | Kawasaki Heavy vs. Wialan Technologies | Kawasaki Heavy vs. Cgrowth Capital |
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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