Correlation Between Regional Bank and Putnam Global
Can any of the company-specific risk be diversified away by investing in both Regional Bank and Putnam Global 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 Regional Bank and Putnam Global into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Regional Bank Fund and Putnam Global Financials, you can compare the effects of market volatilities on Regional Bank and Putnam Global 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 Regional Bank with a short position of Putnam Global. Check out your portfolio center. Please also check ongoing floating volatility patterns of Regional Bank and Putnam Global.
Diversification Opportunities for Regional Bank and Putnam Global
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Regional and Putnam is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Regional Bank Fund and Putnam Global Financials in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Putnam Global Financials and Regional Bank 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 Regional Bank Fund are associated (or correlated) with Putnam Global. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Putnam Global Financials has no effect on the direction of Regional Bank i.e., Regional Bank and Putnam Global go up and down completely randomly.
Pair Corralation between Regional Bank and Putnam Global
Assuming the 90 days horizon Regional Bank is expected to generate 1.16 times less return on investment than Putnam Global. In addition to that, Regional Bank is 3.19 times more volatile than Putnam Global Financials. It trades about 0.06 of its total potential returns per unit of risk. Putnam Global Financials is currently generating about 0.22 per unit of volatility. If you would invest 1,044 in Putnam Global Financials on March 7, 2025 and sell it today you would earn a total of 22.00 from holding Putnam Global Financials or generate 2.11% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Regional Bank Fund vs. Putnam Global Financials
Performance |
Timeline |
Regional Bank |
Putnam Global Financials |
Regional Bank and Putnam Global Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Regional Bank and Putnam Global
The main advantage of trading using opposite Regional Bank and Putnam Global positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Regional Bank position performs unexpectedly, Putnam Global 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 Global will offset losses from the drop in Putnam Global's long position.Regional Bank vs. Dreyfusstandish Global Fixed | Regional Bank vs. Ab Bond Inflation | Regional Bank vs. Pace Strategic Fixed | Regional Bank vs. Ab Bond Inflation |
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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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