Correlation Between Balanced Fund and Putnam Floating
Can any of the company-specific risk be diversified away by investing in both Balanced Fund and Putnam Floating 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 Balanced Fund and Putnam Floating into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Balanced Fund Retail and Putnam Floating Rate, you can compare the effects of market volatilities on Balanced Fund and Putnam Floating 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 Balanced Fund with a short position of Putnam Floating. Check out your portfolio center. Please also check ongoing floating volatility patterns of Balanced Fund and Putnam Floating.
Diversification Opportunities for Balanced Fund and Putnam Floating
-0.55 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Balanced and Putnam is -0.55. Overlapping area represents the amount of risk that can be diversified away by holding Balanced Fund Retail and Putnam Floating Rate in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Putnam Floating Rate and Balanced Fund 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 Balanced Fund Retail are associated (or correlated) with Putnam Floating. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Putnam Floating Rate has no effect on the direction of Balanced Fund i.e., Balanced Fund and Putnam Floating go up and down completely randomly.
Pair Corralation between Balanced Fund and Putnam Floating
Assuming the 90 days horizon Balanced Fund Retail is expected to generate 14.64 times more return on investment than Putnam Floating. However, Balanced Fund is 14.64 times more volatile than Putnam Floating Rate. It trades about 0.04 of its potential returns per unit of risk. Putnam Floating Rate is currently generating about -0.41 per unit of risk. If you would invest 1,271 in Balanced Fund Retail on December 3, 2024 and sell it today you would earn a total of 6.00 from holding Balanced Fund Retail or generate 0.47% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 95.24% |
Values | Daily Returns |
Balanced Fund Retail vs. Putnam Floating Rate
Performance |
Timeline |
Balanced Fund Retail |
Putnam Floating Rate |
Balanced Fund and Putnam Floating Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Balanced Fund and Putnam Floating
The main advantage of trading using opposite Balanced Fund and Putnam Floating positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Balanced Fund position performs unexpectedly, Putnam Floating 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 Putnam Floating will offset losses from the drop in Putnam Floating's long position.Balanced Fund vs. Muirfield Fund Retail | Balanced Fund vs. Dynamic Growth Fund | Balanced Fund vs. Infrastructure Fund Retail | Balanced Fund vs. Quantex Fund Retail |
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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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