Correlation Between Dicker Data and Magellan Financial
Can any of the company-specific risk be diversified away by investing in both Dicker Data and Magellan Financial 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 Dicker Data and Magellan Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dicker Data and Magellan Financial Group, you can compare the effects of market volatilities on Dicker Data and Magellan Financial 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 Dicker Data with a short position of Magellan Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dicker Data and Magellan Financial.
Diversification Opportunities for Dicker Data and Magellan Financial
-0.38 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Dicker and Magellan is -0.38. Overlapping area represents the amount of risk that can be diversified away by holding Dicker Data and Magellan Financial Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Magellan Financial and Dicker Data 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 Dicker Data are associated (or correlated) with Magellan Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Magellan Financial has no effect on the direction of Dicker Data i.e., Dicker Data and Magellan Financial go up and down completely randomly.
Pair Corralation between Dicker Data and Magellan Financial
Assuming the 90 days trading horizon Dicker Data is expected to under-perform the Magellan Financial. But the stock apears to be less risky and, when comparing its historical volatility, Dicker Data is 1.51 times less risky than Magellan Financial. The stock trades about -0.05 of its potential returns per unit of risk. The Magellan Financial Group is currently generating about 0.13 of returns per unit of risk over similar time horizon. If you would invest 922.00 in Magellan Financial Group on September 4, 2024 and sell it today you would earn a total of 149.00 from holding Magellan Financial Group or generate 16.16% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Dicker Data vs. Magellan Financial Group
Performance |
Timeline |
Dicker Data |
Magellan Financial |
Dicker Data and Magellan Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Dicker Data and Magellan Financial
The main advantage of trading using opposite Dicker Data and Magellan Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dicker Data position performs unexpectedly, Magellan Financial 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 Magellan Financial will offset losses from the drop in Magellan Financial's long position.Dicker Data vs. Renascor Resources | Dicker Data vs. Venus Metals | Dicker Data vs. Havilah Resources | Dicker Data vs. Asara Resources |
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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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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