Correlation Between Super Retail and Kip McGrath
Can any of the company-specific risk be diversified away by investing in both Super Retail and Kip McGrath 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 Super Retail and Kip McGrath into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Super Retail Group and Kip McGrath Education, you can compare the effects of market volatilities on Super Retail and Kip McGrath 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 Super Retail with a short position of Kip McGrath. Check out your portfolio center. Please also check ongoing floating volatility patterns of Super Retail and Kip McGrath.
Diversification Opportunities for Super Retail and Kip McGrath
-0.47 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Super and Kip is -0.47. Overlapping area represents the amount of risk that can be diversified away by holding Super Retail Group and Kip McGrath Education in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kip McGrath Education and Super Retail 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 Super Retail Group are associated (or correlated) with Kip McGrath. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kip McGrath Education has no effect on the direction of Super Retail i.e., Super Retail and Kip McGrath go up and down completely randomly.
Pair Corralation between Super Retail and Kip McGrath
Assuming the 90 days trading horizon Super Retail Group is expected to under-perform the Kip McGrath. But the stock apears to be less risky and, when comparing its historical volatility, Super Retail Group is 1.09 times less risky than Kip McGrath. The stock trades about -0.14 of its potential returns per unit of risk. The Kip McGrath Education is currently generating about 0.09 of returns per unit of risk over similar time horizon. If you would invest 43.00 in Kip McGrath Education on October 1, 2024 and sell it today you would earn a total of 4.00 from holding Kip McGrath Education or generate 9.3% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Super Retail Group vs. Kip McGrath Education
Performance |
Timeline |
Super Retail Group |
Kip McGrath Education |
Super Retail and Kip McGrath Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Super Retail and Kip McGrath
The main advantage of trading using opposite Super Retail and Kip McGrath positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Super Retail position performs unexpectedly, Kip McGrath 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 Kip McGrath will offset losses from the drop in Kip McGrath's long position.Super Retail vs. Ras Technology Holdings | Super Retail vs. Macquarie Technology Group | Super Retail vs. Falcon Metals | Super Retail vs. Centuria Industrial Reit |
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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 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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