Correlation Between Ford and Avicanna
Can any of the company-specific risk be diversified away by investing in both Ford and Avicanna 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 Ford and Avicanna into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ford Motor and Avicanna, you can compare the effects of market volatilities on Ford and Avicanna 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 Ford with a short position of Avicanna. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ford and Avicanna.
Diversification Opportunities for Ford and Avicanna
Very good diversification
The 3 months correlation between Ford and Avicanna is -0.48. Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor and Avicanna in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Avicanna and Ford 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 Ford Motor are associated (or correlated) with Avicanna. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Avicanna has no effect on the direction of Ford i.e., Ford and Avicanna go up and down completely randomly.
Pair Corralation between Ford and Avicanna
Taking into account the 90-day investment horizon Ford is expected to generate 2.28 times less return on investment than Avicanna. But when comparing it to its historical volatility, Ford Motor is 4.09 times less risky than Avicanna. It trades about 0.03 of its potential returns per unit of risk. Avicanna is currently generating about 0.02 of returns per unit of risk over similar time horizon. If you would invest 26.00 in Avicanna on September 11, 2024 and sell it today you would lose (3.00) from holding Avicanna or give up 11.54% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Ford Motor vs. Avicanna
Performance |
Timeline |
Ford Motor |
Avicanna |
Ford and Avicanna Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ford and Avicanna
The main advantage of trading using opposite Ford and Avicanna positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ford position performs unexpectedly, Avicanna 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 Avicanna will offset losses from the drop in Avicanna's long position.The idea behind Ford Motor and Avicanna 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.Avicanna vs. Pharmacielo | Avicanna vs. Khiron Life Sciences | Avicanna vs. Flower One Holdings | Avicanna vs. Cansortium |
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 Suggestion module to get suggestions outside of your existing asset allocation including your own model portfolios.
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