Correlation Between Ferrari NV and Aston Martin
Can any of the company-specific risk be diversified away by investing in both Ferrari NV and Aston Martin 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 Ferrari NV and Aston Martin into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ferrari NV and Aston Martin Lagonda, you can compare the effects of market volatilities on Ferrari NV and Aston Martin 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 Ferrari NV with a short position of Aston Martin. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ferrari NV and Aston Martin.
Diversification Opportunities for Ferrari NV and Aston Martin
0.35 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Ferrari and Aston is 0.35. Overlapping area represents the amount of risk that can be diversified away by holding Ferrari NV and Aston Martin Lagonda in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Aston Martin Lagonda and Ferrari NV 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 Ferrari NV are associated (or correlated) with Aston Martin. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Aston Martin Lagonda has no effect on the direction of Ferrari NV i.e., Ferrari NV and Aston Martin go up and down completely randomly.
Pair Corralation between Ferrari NV and Aston Martin
Given the investment horizon of 90 days Ferrari NV is expected to generate 0.33 times more return on investment than Aston Martin. However, Ferrari NV is 3.05 times less risky than Aston Martin. It trades about 0.05 of its potential returns per unit of risk. Aston Martin Lagonda is currently generating about -0.03 per unit of risk. If you would invest 36,143 in Ferrari NV on September 2, 2024 and sell it today you would earn a total of 7,273 from holding Ferrari NV or generate 20.12% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Ferrari NV vs. Aston Martin Lagonda
Performance |
| Timeline |
| Ferrari NV |
| Aston Martin Lagonda |
Ferrari NV and Aston Martin Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Ferrari NV and Aston Martin
The main advantage of trading using opposite Ferrari NV and Aston Martin positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ferrari NV position performs unexpectedly, Aston Martin 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 Aston Martin will offset losses from the drop in Aston Martin's long position.| Ferrari NV vs. Volkswagen AG Pref | Ferrari NV vs. Volkswagen AG 110 | Ferrari NV vs. Porsche Automobil Holding | Ferrari NV vs. Bayerische Motoren Werke |
| Aston Martin vs. Polestar Automotive Holding | Aston Martin vs. Geely Automobile Holdings | Aston Martin vs. Mercedes Benz Group AG | Aston Martin vs. Porsche Automobile Holding |
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 Volatility Analysis module to get historical volatility and risk analysis based on latest market data.
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