Correlation Between Okta and Ferrari NV
Can any of the company-specific risk be diversified away by investing in both Okta and Ferrari NV 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 Okta and Ferrari NV into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Okta Inc and Ferrari NV, you can compare the effects of market volatilities on Okta and Ferrari NV 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 Okta with a short position of Ferrari NV. Check out your portfolio center. Please also check ongoing floating volatility patterns of Okta and Ferrari NV.
Diversification Opportunities for Okta and Ferrari NV
0.25 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Okta and Ferrari is 0.25. Overlapping area represents the amount of risk that can be diversified away by holding Okta Inc and Ferrari NV in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ferrari NV and Okta 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 Okta Inc are associated (or correlated) with Ferrari NV. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ferrari NV has no effect on the direction of Okta i.e., Okta and Ferrari NV go up and down completely randomly.
Pair Corralation between Okta and Ferrari NV
Given the investment horizon of 90 days Okta is expected to generate 1.51 times less return on investment than Ferrari NV. In addition to that, Okta is 1.57 times more volatile than Ferrari NV. It trades about 0.02 of its total potential returns per unit of risk. Ferrari NV is currently generating about 0.05 per unit of volatility. If you would invest 36,593 in Ferrari NV on August 24, 2024 and sell it today you would earn a total of 6,358 from holding Ferrari NV or generate 17.37% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Okta Inc vs. Ferrari NV
Performance |
| Timeline |
| Okta Inc |
| Ferrari NV |
Okta and Ferrari NV Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Okta and Ferrari NV
The main advantage of trading using opposite Okta and Ferrari NV positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Okta position performs unexpectedly, Ferrari NV 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 Ferrari NV will offset losses from the drop in Ferrari NV's long position.The idea behind Okta Inc and Ferrari NV pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.| Ferrari NV vs. Volkswagen AG Pref | Ferrari NV vs. Volkswagen AG 110 | Ferrari NV vs. Porsche Automobil Holding | Ferrari NV vs. Bayerische Motoren Werke |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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