Correlation Between Hennessy Technology and Loomis Sayles
Can any of the company-specific risk be diversified away by investing in both Hennessy 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 Hennessy 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 Hennessy Technology Fund and Loomis Sayles High, you can compare the effects of market volatilities on Hennessy 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 Hennessy Technology with a short position of Loomis Sayles. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hennessy Technology and Loomis Sayles.
Diversification Opportunities for Hennessy Technology and Loomis Sayles
0.73 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Hennessy and Loomis is 0.73. Overlapping area represents the amount of risk that can be diversified away by holding Hennessy 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 Hennessy 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 Hennessy 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 Hennessy Technology i.e., Hennessy Technology and Loomis Sayles go up and down completely randomly.
Pair Corralation between Hennessy Technology and Loomis Sayles
Assuming the 90 days horizon Hennessy Technology Fund is expected to under-perform the Loomis Sayles. In addition to that, Hennessy Technology is 5.95 times more volatile than Loomis Sayles High. It trades about -0.09 of its total potential returns per unit of risk. Loomis Sayles High is currently generating about -0.3 per unit of volatility. If you would invest 902.00 in Loomis Sayles High on September 27, 2024 and sell it today you would lose (16.00) from holding Loomis Sayles High or give up 1.77% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Hennessy Technology Fund vs. Loomis Sayles High
Performance |
Timeline |
Hennessy Technology |
Loomis Sayles High |
Hennessy Technology and Loomis Sayles Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hennessy Technology and Loomis Sayles
The main advantage of trading using opposite Hennessy Technology and Loomis Sayles positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hennessy 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.Hennessy Technology vs. Black Oak Emerging | Hennessy Technology vs. Hennessy Large Cap | Hennessy Technology vs. Hennessy Japan Fund | Hennessy Technology vs. Hennessy Small Cap |
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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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