Correlation Between Financial Industries and Bbh Partner
Can any of the company-specific risk be diversified away by investing in both Financial Industries and Bbh Partner 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 Financial Industries and Bbh Partner into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Financial Industries Fund and Bbh Partner Fund, you can compare the effects of market volatilities on Financial Industries and Bbh Partner 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 Financial Industries with a short position of Bbh Partner. Check out your portfolio center. Please also check ongoing floating volatility patterns of Financial Industries and Bbh Partner.
Diversification Opportunities for Financial Industries and Bbh Partner
-0.37 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Financial and Bbh is -0.37. Overlapping area represents the amount of risk that can be diversified away by holding Financial Industries Fund and Bbh Partner Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bbh Partner Fund and Financial Industries 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 Financial Industries Fund are associated (or correlated) with Bbh Partner. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bbh Partner Fund has no effect on the direction of Financial Industries i.e., Financial Industries and Bbh Partner go up and down completely randomly.
Pair Corralation between Financial Industries and Bbh Partner
Assuming the 90 days horizon Financial Industries Fund is expected to generate 1.15 times more return on investment than Bbh Partner. However, Financial Industries is 1.15 times more volatile than Bbh Partner Fund. It trades about 0.1 of its potential returns per unit of risk. Bbh Partner Fund is currently generating about 0.04 per unit of risk. If you would invest 1,451 in Financial Industries Fund on September 12, 2024 and sell it today you would earn a total of 597.00 from holding Financial Industries Fund or generate 41.14% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Financial Industries Fund vs. Bbh Partner Fund
Performance |
Timeline |
Financial Industries |
Bbh Partner Fund |
Financial Industries and Bbh Partner Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Financial Industries and Bbh Partner
The main advantage of trading using opposite Financial Industries and Bbh Partner positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Financial Industries position performs unexpectedly, Bbh Partner 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 Bbh Partner will offset losses from the drop in Bbh Partner's long position.Financial Industries vs. Gmo Global Equity | Financial Industries vs. Us Strategic Equity | Financial Industries vs. Dodge International Stock | Financial Industries vs. Calamos Global Equity |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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