Correlation Between Hochschild Mining and Monster Beverage
Can any of the company-specific risk be diversified away by investing in both Hochschild Mining and Monster Beverage 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 Hochschild Mining and Monster Beverage into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hochschild Mining plc and Monster Beverage Corp, you can compare the effects of market volatilities on Hochschild Mining and Monster Beverage 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 Hochschild Mining with a short position of Monster Beverage. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hochschild Mining and Monster Beverage.
Diversification Opportunities for Hochschild Mining and Monster Beverage
0.65 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Hochschild and Monster is 0.65. Overlapping area represents the amount of risk that can be diversified away by holding Hochschild Mining plc and Monster Beverage Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Monster Beverage Corp and Hochschild Mining 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 Hochschild Mining plc are associated (or correlated) with Monster Beverage. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Monster Beverage Corp has no effect on the direction of Hochschild Mining i.e., Hochschild Mining and Monster Beverage go up and down completely randomly.
Pair Corralation between Hochschild Mining and Monster Beverage
Assuming the 90 days trading horizon Hochschild Mining plc is expected to generate 2.04 times more return on investment than Monster Beverage. However, Hochschild Mining is 2.04 times more volatile than Monster Beverage Corp. It trades about 0.16 of its potential returns per unit of risk. Monster Beverage Corp is currently generating about 0.04 per unit of risk. If you would invest 18,240 in Hochschild Mining plc on September 13, 2024 and sell it today you would earn a total of 5,660 from holding Hochschild Mining plc or generate 31.03% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Hochschild Mining plc vs. Monster Beverage Corp
Performance |
Timeline |
Hochschild Mining plc |
Monster Beverage Corp |
Hochschild Mining and Monster Beverage Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hochschild Mining and Monster Beverage
The main advantage of trading using opposite Hochschild Mining and Monster Beverage positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hochschild Mining position performs unexpectedly, Monster Beverage 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 Monster Beverage will offset losses from the drop in Monster Beverage's long position.Hochschild Mining vs. Givaudan SA | Hochschild Mining vs. Antofagasta PLC | Hochschild Mining vs. Ferrexpo PLC | Hochschild Mining vs. Atalaya Mining |
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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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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