Correlation Between Peak Resources and COMMERCIAL VEHICLE
Can any of the company-specific risk be diversified away by investing in both Peak Resources and COMMERCIAL VEHICLE 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 Peak Resources and COMMERCIAL VEHICLE into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Peak Resources Limited and COMMERCIAL VEHICLE, you can compare the effects of market volatilities on Peak Resources and COMMERCIAL VEHICLE 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 Peak Resources with a short position of COMMERCIAL VEHICLE. Check out your portfolio center. Please also check ongoing floating volatility patterns of Peak Resources and COMMERCIAL VEHICLE.
Diversification Opportunities for Peak Resources and COMMERCIAL VEHICLE
0.89 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Peak and COMMERCIAL is 0.89. Overlapping area represents the amount of risk that can be diversified away by holding Peak Resources Limited and COMMERCIAL VEHICLE in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on COMMERCIAL VEHICLE and Peak Resources 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 Peak Resources Limited are associated (or correlated) with COMMERCIAL VEHICLE. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of COMMERCIAL VEHICLE has no effect on the direction of Peak Resources i.e., Peak Resources and COMMERCIAL VEHICLE go up and down completely randomly.
Pair Corralation between Peak Resources and COMMERCIAL VEHICLE
Assuming the 90 days horizon Peak Resources Limited is expected to generate 2.76 times more return on investment than COMMERCIAL VEHICLE. However, Peak Resources is 2.76 times more volatile than COMMERCIAL VEHICLE. It trades about 0.1 of its potential returns per unit of risk. COMMERCIAL VEHICLE is currently generating about -0.01 per unit of risk. If you would invest 5.80 in Peak Resources Limited on September 12, 2024 and sell it today you would earn a total of 0.70 from holding Peak Resources Limited or generate 12.07% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Peak Resources Limited vs. COMMERCIAL VEHICLE
Performance |
Timeline |
Peak Resources |
COMMERCIAL VEHICLE |
Peak Resources and COMMERCIAL VEHICLE Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Peak Resources and COMMERCIAL VEHICLE
The main advantage of trading using opposite Peak Resources and COMMERCIAL VEHICLE positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Peak Resources position performs unexpectedly, COMMERCIAL VEHICLE 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 COMMERCIAL VEHICLE will offset losses from the drop in COMMERCIAL VEHICLE's long position.Peak Resources vs. NEWELL RUBBERMAID | Peak Resources vs. The Yokohama Rubber | Peak Resources vs. Hyster Yale Materials Handling | Peak Resources vs. Park Hotels Resorts |
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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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