Correlation Between Amg Managers and Cref Money
Can any of the company-specific risk be diversified away by investing in both Amg Managers and Cref Money 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 Amg Managers and Cref Money into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Amg Managers Centersquare and Cref Money Market, you can compare the effects of market volatilities on Amg Managers and Cref Money 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 Amg Managers with a short position of Cref Money. Check out your portfolio center. Please also check ongoing floating volatility patterns of Amg Managers and Cref Money.
Diversification Opportunities for Amg Managers and Cref Money
-0.01 | Correlation Coefficient |
Good diversification
The 3 months correlation between Amg and Cref is -0.01. Overlapping area represents the amount of risk that can be diversified away by holding Amg Managers Centersquare and Cref Money Market in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cref Money Market and Amg Managers 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 Amg Managers Centersquare are associated (or correlated) with Cref Money. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cref Money Market has no effect on the direction of Amg Managers i.e., Amg Managers and Cref Money go up and down completely randomly.
Pair Corralation between Amg Managers and Cref Money
Assuming the 90 days horizon Amg Managers Centersquare is expected to generate 43.19 times more return on investment than Cref Money. However, Amg Managers is 43.19 times more volatile than Cref Money Market. It trades about 0.16 of its potential returns per unit of risk. Cref Money Market is currently generating about 0.97 per unit of risk. If you would invest 1,027 in Amg Managers Centersquare on August 29, 2024 and sell it today you would earn a total of 191.00 from holding Amg Managers Centersquare or generate 18.6% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Amg Managers Centersquare vs. Cref Money Market
Performance |
| Timeline |
| Amg Managers Centersquare |
| Cref Money Market |
Amg Managers and Cref Money Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Amg Managers and Cref Money
The main advantage of trading using opposite Amg Managers and Cref Money positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Amg Managers position performs unexpectedly, Cref Money 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 Cref Money will offset losses from the drop in Cref Money's long position.| Amg Managers vs. T Rowe Price | Amg Managers vs. L Abbett Fundamental | Amg Managers vs. Acm Tactical Income | Amg Managers vs. Qs Global Equity |
| Cref Money vs. Vanguard Global Credit | Cref Money vs. Ms Global Fixed | Cref Money vs. T Rowe Price | Cref Money vs. Chartwell Short Duration |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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