Correlation Between Autosports and Global Data
Can any of the company-specific risk be diversified away by investing in both Autosports and Global Data 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 Autosports and Global Data into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Autosports Group and Global Data Centre, you can compare the effects of market volatilities on Autosports and Global Data 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 Autosports with a short position of Global Data. Check out your portfolio center. Please also check ongoing floating volatility patterns of Autosports and Global Data.
Diversification Opportunities for Autosports and Global Data
0.74 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Autosports and Global is 0.74. Overlapping area represents the amount of risk that can be diversified away by holding Autosports Group and Global Data Centre in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Global Data Centre and Autosports 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 Autosports Group are associated (or correlated) with Global Data. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Global Data Centre has no effect on the direction of Autosports i.e., Autosports and Global Data go up and down completely randomly.
Pair Corralation between Autosports and Global Data
Assuming the 90 days trading horizon Autosports Group is expected to under-perform the Global Data. In addition to that, Autosports is 10.54 times more volatile than Global Data Centre. It trades about -0.32 of its total potential returns per unit of risk. Global Data Centre is currently generating about 0.21 per unit of volatility. If you would invest 142.00 in Global Data Centre on September 20, 2024 and sell it today you would earn a total of 1.00 from holding Global Data Centre or generate 0.7% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Autosports Group vs. Global Data Centre
Performance |
| Timeline |
| Autosports Group |
| Global Data Centre |
Autosports and Global Data Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Autosports and Global Data
The main advantage of trading using opposite Autosports and Global Data positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Autosports position performs unexpectedly, Global Data 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 Global Data will offset losses from the drop in Global Data's long position.| Autosports vs. Westpac Banking | Autosports vs. National Australia Bank | Autosports vs. National Australia Bank | Autosports vs. National Australia Bank |
| Global Data vs. Bailador Technology Invest | Global Data vs. Regis Healthcare | Global Data vs. Viva Leisure | Global Data vs. Autosports Group |
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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