Correlation Between Kip McGrath and Sayona Mining
Can any of the company-specific risk be diversified away by investing in both Kip McGrath and Sayona Mining 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 Kip McGrath and Sayona Mining into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Kip McGrath Education and Sayona Mining, you can compare the effects of market volatilities on Kip McGrath and Sayona Mining 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 Kip McGrath with a short position of Sayona Mining. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kip McGrath and Sayona Mining.
Diversification Opportunities for Kip McGrath and Sayona Mining
-0.35 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Kip and Sayona is -0.35. Overlapping area represents the amount of risk that can be diversified away by holding Kip McGrath Education and Sayona Mining in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sayona Mining and Kip McGrath 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 Kip McGrath Education are associated (or correlated) with Sayona Mining. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sayona Mining has no effect on the direction of Kip McGrath i.e., Kip McGrath and Sayona Mining go up and down completely randomly.
Pair Corralation between Kip McGrath and Sayona Mining
Assuming the 90 days trading horizon Kip McGrath Education is expected to generate 0.64 times more return on investment than Sayona Mining. However, Kip McGrath Education is 1.57 times less risky than Sayona Mining. It trades about 0.1 of its potential returns per unit of risk. Sayona Mining is currently generating about -0.02 per unit of risk. If you would invest 33.00 in Kip McGrath Education on November 1, 2024 and sell it today you would earn a total of 12.00 from holding Kip McGrath Education or generate 36.36% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Kip McGrath Education vs. Sayona Mining
Performance |
| Timeline |
| Kip McGrath Education |
| Sayona Mining |
Kip McGrath and Sayona Mining Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Kip McGrath and Sayona Mining
The main advantage of trading using opposite Kip McGrath and Sayona Mining positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kip McGrath position performs unexpectedly, Sayona Mining 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 Sayona Mining will offset losses from the drop in Sayona Mining's long position.| Kip McGrath vs. Fisher Paykel Healthcare | Kip McGrath vs. Sonic Healthcare | Kip McGrath vs. Readytech Holdings | Kip McGrath vs. Neurotech International |
| Sayona Mining vs. A1 Investments Resources | Sayona Mining vs. Djerriwarrh Investments | Sayona Mining vs. DY6 Metals | Sayona Mining vs. Hotel Property Investments |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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