Correlation Between Science Technology and Loomis Sayles
Can any of the company-specific risk be diversified away by investing in both Science Technology and Loomis Sayles 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 Science Technology and Loomis Sayles into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Science Technology Fund and Loomis Sayles High, you can compare the effects of market volatilities on Science Technology and Loomis Sayles 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 Science Technology with a short position of Loomis Sayles. Check out your portfolio center. Please also check ongoing floating volatility patterns of Science Technology and Loomis Sayles.
Diversification Opportunities for Science Technology and Loomis Sayles
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Science and Loomis is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Science Technology Fund and Loomis Sayles High in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Loomis Sayles High and Science Technology 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 Science Technology Fund are associated (or correlated) with Loomis Sayles. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Loomis Sayles High has no effect on the direction of Science Technology i.e., Science Technology and Loomis Sayles go up and down completely randomly.
Pair Corralation between Science Technology and Loomis Sayles
Assuming the 90 days horizon Science Technology Fund is expected to generate 5.07 times more return on investment than Loomis Sayles. However, Science Technology is 5.07 times more volatile than Loomis Sayles High. It trades about 0.07 of its potential returns per unit of risk. Loomis Sayles High is currently generating about -0.3 per unit of risk. If you would invest 2,892 in Science Technology Fund on September 27, 2024 and sell it today you would earn a total of 54.00 from holding Science Technology Fund or generate 1.87% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 95.45% |
Values | Daily Returns |
Science Technology Fund vs. Loomis Sayles High
Performance |
Timeline |
Science Technology |
Loomis Sayles High |
Science Technology and Loomis Sayles Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Science Technology and Loomis Sayles
The main advantage of trading using opposite Science Technology and Loomis Sayles positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Science Technology position performs unexpectedly, Loomis Sayles 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 Loomis Sayles will offset losses from the drop in Loomis Sayles' long position.Science Technology vs. Siit Emerging Markets | Science Technology vs. Pnc Emerging Markets | Science Technology vs. Rbc Emerging Markets | Science Technology vs. Ep Emerging Markets |
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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 Fundamental Analysis module to view fundamental data based on most recent published financial statements.
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