Correlation Between Hennessy and Loomis Sayles
Can any of the company-specific risk be diversified away by investing in both Hennessy 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 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 Bp Energy and Loomis Sayles Strategic, you can compare the effects of market volatilities on Hennessy 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 with a short position of Loomis Sayles. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hennessy and Loomis Sayles.
Diversification Opportunities for Hennessy and Loomis Sayles
-0.01 | Correlation Coefficient |
Good diversification
The 3 months correlation between Hennessy and Loomis is -0.01. Overlapping area represents the amount of risk that can be diversified away by holding Hennessy Bp Energy and Loomis Sayles Strategic in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Loomis Sayles Strategic and Hennessy 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 Bp Energy 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 Strategic has no effect on the direction of Hennessy i.e., Hennessy and Loomis Sayles go up and down completely randomly.
Pair Corralation between Hennessy and Loomis Sayles
Assuming the 90 days horizon Hennessy Bp Energy is expected to generate 3.74 times more return on investment than Loomis Sayles. However, Hennessy is 3.74 times more volatile than Loomis Sayles Strategic. It trades about 0.03 of its potential returns per unit of risk. Loomis Sayles Strategic is currently generating about 0.07 per unit of risk. If you would invest 2,310 in Hennessy Bp Energy on September 13, 2024 and sell it today you would earn a total of 482.00 from holding Hennessy Bp Energy or generate 20.87% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 99.8% |
| Values | Daily Returns |
Hennessy Bp Energy vs. Loomis Sayles Strategic
Performance |
| Timeline |
| Hennessy Bp Energy |
| Loomis Sayles Strategic |
Hennessy and Loomis Sayles Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Hennessy and Loomis Sayles
The main advantage of trading using opposite Hennessy and Loomis Sayles positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hennessy 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 vs. World Energy Fund | Hennessy vs. Blackrock All Cap Energy | Hennessy vs. Energy Fund Class | Hennessy vs. Fidelity Advisor Energy |
| Loomis Sayles vs. Asg Managed Futures | Loomis Sayles vs. Asg Managed Futures | Loomis Sayles vs. Natixis Oakmark | Loomis Sayles vs. Natixis Oakmark International |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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