Correlation Between Hennessy and Goldman Sachs
Can any of the company-specific risk be diversified away by investing in both Hennessy and Goldman Sachs 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 Goldman Sachs 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 Goldman Sachs Short, you can compare the effects of market volatilities on Hennessy and Goldman Sachs 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 Goldman Sachs. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hennessy and Goldman Sachs.
Diversification Opportunities for Hennessy and Goldman Sachs
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Hennessy and GOLDMAN is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Hennessy Bp Energy and Goldman Sachs Short in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Goldman Sachs Short 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 Goldman Sachs. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Goldman Sachs Short has no effect on the direction of Hennessy i.e., Hennessy and Goldman Sachs go up and down completely randomly.
Pair Corralation between Hennessy and Goldman Sachs
Assuming the 90 days horizon Hennessy Bp Energy is expected to generate 12.74 times more return on investment than Goldman Sachs. However, Hennessy is 12.74 times more volatile than Goldman Sachs Short. It trades about 0.03 of its potential returns per unit of risk. Goldman Sachs Short is currently generating about 0.11 per unit of risk. If you would invest 2,383 in Hennessy Bp Energy on October 11, 2024 and sell it today you would earn a total of 385.00 from holding Hennessy Bp Energy or generate 16.16% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 99.8% |
| Values | Daily Returns |
Hennessy Bp Energy vs. Goldman Sachs Short
Performance |
| Timeline |
| Hennessy Bp Energy |
| Goldman Sachs Short |
Hennessy and Goldman Sachs Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Hennessy and Goldman Sachs
The main advantage of trading using opposite Hennessy and Goldman Sachs positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hennessy position performs unexpectedly, Goldman Sachs 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 Goldman Sachs will offset losses from the drop in Goldman Sachs' long position.| Hennessy vs. World Energy Fund | Hennessy vs. Ivy Energy Fund | Hennessy vs. Blackrock All Cap Energy | Hennessy vs. Energy Fund Class |
| Goldman Sachs vs. Blackrock All Cap Energy | Goldman Sachs vs. Vanguard Energy Index | Goldman Sachs vs. Hennessy Bp Energy | Goldman Sachs vs. Icon Natural Resources |
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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