Correlation Between Franklin High and Segall Bryant
Can any of the company-specific risk be diversified away by investing in both Franklin High and Segall Bryant 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 Franklin High and Segall Bryant into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Franklin High Income and Segall Bryant Hamill, you can compare the effects of market volatilities on Franklin High and Segall Bryant 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 Franklin High with a short position of Segall Bryant. Check out your portfolio center. Please also check ongoing floating volatility patterns of Franklin High and Segall Bryant.
Diversification Opportunities for Franklin High and Segall Bryant
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Franklin and Segall is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding Franklin High Income and Segall Bryant Hamill in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Segall Bryant Hamill and Franklin High 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 Franklin High Income are associated (or correlated) with Segall Bryant. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Segall Bryant Hamill has no effect on the direction of Franklin High i.e., Franklin High and Segall Bryant go up and down completely randomly.
Pair Corralation between Franklin High and Segall Bryant
Assuming the 90 days horizon Franklin High is expected to generate 1.71 times less return on investment than Segall Bryant. But when comparing it to its historical volatility, Franklin High Income is 2.37 times less risky than Segall Bryant. It trades about 0.19 of its potential returns per unit of risk. Segall Bryant Hamill is currently generating about 0.13 of returns per unit of risk over similar time horizon. If you would invest 1,828 in Segall Bryant Hamill on August 25, 2024 and sell it today you would earn a total of 560.00 from holding Segall Bryant Hamill or generate 30.63% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Franklin High Income vs. Segall Bryant Hamill
Performance |
Timeline |
Franklin High Income |
Segall Bryant Hamill |
Franklin High and Segall Bryant Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Franklin High and Segall Bryant
The main advantage of trading using opposite Franklin High and Segall Bryant positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Franklin High position performs unexpectedly, Segall Bryant 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 Segall Bryant will offset losses from the drop in Segall Bryant's long position.Franklin High vs. Franklin Mutual Beacon | Franklin High vs. Templeton Developing Markets | Franklin High vs. Franklin Mutual Global | Franklin High vs. Franklin Mutual Global |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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