Correlation Between Federated Global and Dodge Cox
Can any of the company-specific risk be diversified away by investing in both Federated Global and Dodge Cox 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 Federated Global and Dodge Cox into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Federated Global Allocation and Dodge Global Stock, you can compare the effects of market volatilities on Federated Global and Dodge Cox 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 Federated Global with a short position of Dodge Cox. Check out your portfolio center. Please also check ongoing floating volatility patterns of Federated Global and Dodge Cox.
Diversification Opportunities for Federated Global and Dodge Cox
0.73 | Correlation Coefficient |
Poor diversification
The 3 months correlation between FEDERATED and Dodge is 0.73. Overlapping area represents the amount of risk that can be diversified away by holding Federated Global Allocation and Dodge Global Stock in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dodge Global Stock and Federated Global 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 Federated Global Allocation are associated (or correlated) with Dodge Cox. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dodge Global Stock has no effect on the direction of Federated Global i.e., Federated Global and Dodge Cox go up and down completely randomly.
Pair Corralation between Federated Global and Dodge Cox
Assuming the 90 days horizon Federated Global Allocation is expected to generate 0.68 times more return on investment than Dodge Cox. However, Federated Global Allocation is 1.47 times less risky than Dodge Cox. It trades about 0.1 of its potential returns per unit of risk. Dodge Global Stock is currently generating about 0.02 per unit of risk. If you would invest 1,943 in Federated Global Allocation on August 31, 2024 and sell it today you would earn a total of 57.00 from holding Federated Global Allocation or generate 2.93% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Federated Global Allocation vs. Dodge Global Stock
Performance |
Timeline |
Federated Global All |
Dodge Global Stock |
Federated Global and Dodge Cox Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Federated Global and Dodge Cox
The main advantage of trading using opposite Federated Global and Dodge Cox positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Federated Global position performs unexpectedly, Dodge Cox 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 Dodge Cox will offset losses from the drop in Dodge Cox's long position.Federated Global vs. Federated Max Cap Index | Federated Global vs. Federated Kaufmann Fund | Federated Global vs. Federated Strategic Income | Federated Global vs. Federated Bond Fund |
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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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