Correlation Between Olivers Real and Super Retail
Can any of the company-specific risk be diversified away by investing in both Olivers Real and Super Retail 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 Olivers Real and Super Retail into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Olivers Real Food and Super Retail Group, you can compare the effects of market volatilities on Olivers Real and Super Retail 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 Olivers Real with a short position of Super Retail. Check out your portfolio center. Please also check ongoing floating volatility patterns of Olivers Real and Super Retail.
Diversification Opportunities for Olivers Real and Super Retail
-0.07 | Correlation Coefficient |
Good diversification
The 3 months correlation between Olivers and Super is -0.07. Overlapping area represents the amount of risk that can be diversified away by holding Olivers Real Food and Super Retail Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Super Retail Group and Olivers Real 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 Olivers Real Food are associated (or correlated) with Super Retail. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Super Retail Group has no effect on the direction of Olivers Real i.e., Olivers Real and Super Retail go up and down completely randomly.
Pair Corralation between Olivers Real and Super Retail
Assuming the 90 days trading horizon Olivers Real Food is expected to under-perform the Super Retail. In addition to that, Olivers Real is 4.11 times more volatile than Super Retail Group. It trades about -0.18 of its total potential returns per unit of risk. Super Retail Group is currently generating about 0.08 per unit of volatility. If you would invest 1,444 in Super Retail Group on August 31, 2024 and sell it today you would earn a total of 37.00 from holding Super Retail Group or generate 2.56% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Olivers Real Food vs. Super Retail Group
Performance |
| Timeline |
| Olivers Real Food |
| Super Retail Group |
Olivers Real and Super Retail Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Olivers Real and Super Retail
The main advantage of trading using opposite Olivers Real and Super Retail positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Olivers Real position performs unexpectedly, Super Retail 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 Super Retail will offset losses from the drop in Super Retail's long position.| Olivers Real vs. Aneka Tambang Tbk | Olivers Real vs. Unibail Rodamco Westfield SE | Olivers Real vs. Macquarie Group | Olivers Real vs. Commonwealth Bank |
| Super Retail vs. Aneka Tambang Tbk | Super Retail vs. Unibail Rodamco Westfield SE | Super Retail vs. Macquarie Group | Super Retail vs. Commonwealth Bank |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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