Correlation Between BHP Group and Pampa Metals
Can any of the company-specific risk be diversified away by investing in both BHP Group and Pampa Metals 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 BHP Group and Pampa Metals into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between BHP Group Limited and Pampa Metals, you can compare the effects of market volatilities on BHP Group and Pampa Metals 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 BHP Group with a short position of Pampa Metals. Check out your portfolio center. Please also check ongoing floating volatility patterns of BHP Group and Pampa Metals.
Diversification Opportunities for BHP Group and Pampa Metals
-0.41 | Correlation Coefficient |
Very good diversification
The 3 months correlation between BHP and Pampa is -0.41. Overlapping area represents the amount of risk that can be diversified away by holding BHP Group Limited and Pampa Metals in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pampa Metals and BHP Group 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 BHP Group Limited are associated (or correlated) with Pampa Metals. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pampa Metals has no effect on the direction of BHP Group i.e., BHP Group and Pampa Metals go up and down completely randomly.
Pair Corralation between BHP Group and Pampa Metals
Assuming the 90 days horizon BHP Group is expected to generate 62.38 times less return on investment than Pampa Metals. But when comparing it to its historical volatility, BHP Group Limited is 16.52 times less risky than Pampa Metals. It trades about 0.02 of its potential returns per unit of risk. Pampa Metals is currently generating about 0.07 of returns per unit of risk over similar time horizon. If you would invest 20.00 in Pampa Metals on September 1, 2024 and sell it today you would lose (5.00) from holding Pampa Metals or give up 25.0% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
BHP Group Limited vs. Pampa Metals
Performance |
| Timeline |
| BHP Group Limited |
| Pampa Metals |
BHP Group and Pampa Metals Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with BHP Group and Pampa Metals
The main advantage of trading using opposite BHP Group and Pampa Metals positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if BHP Group position performs unexpectedly, Pampa Metals 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 Pampa Metals will offset losses from the drop in Pampa Metals' long position.| BHP Group vs. Anglo American PLC | BHP Group vs. Avarone Metals | BHP Group vs. Huntsman Exploration | BHP Group vs. Aurelia Metals Limited |
| Pampa Metals vs. Surge Battery Metals | Pampa Metals vs. United Royale Holdings | Pampa Metals vs. Hillcrest Energy Technologies | Pampa Metals vs. Star Alliance International |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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