Correlation Between Ford and Neonode
Can any of the company-specific risk be diversified away by investing in both Ford and Neonode 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 Neonode into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ford Motor and Neonode, you can compare the effects of market volatilities on Ford and Neonode 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 Neonode. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ford and Neonode.
Diversification Opportunities for Ford and Neonode
Good diversification
The 3 months correlation between Ford and Neonode is -0.14. Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor and Neonode in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Neonode 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 Neonode. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Neonode has no effect on the direction of Ford i.e., Ford and Neonode go up and down completely randomly.
Pair Corralation between Ford and Neonode
Taking into account the 90-day investment horizon Ford is expected to generate 9.67 times less return on investment than Neonode. But when comparing it to its historical volatility, Ford Motor is 4.16 times less risky than Neonode. It trades about 0.03 of its potential returns per unit of risk. Neonode is currently generating about 0.07 of returns per unit of risk over similar time horizon. If you would invest 685.00 in Neonode on September 1, 2024 and sell it today you would earn a total of 135.00 from holding Neonode or generate 19.71% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Ford Motor vs. Neonode
Performance |
Timeline |
Ford Motor |
Neonode |
Ford and Neonode Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ford and Neonode
The main advantage of trading using opposite Ford and Neonode positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ford position performs unexpectedly, Neonode 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 Neonode will offset losses from the drop in Neonode's long position.The idea behind Ford Motor and Neonode 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.Neonode vs. LightPath Technologies | Neonode vs. Methode Electronics | Neonode vs. OSI Systems | Neonode vs. Plexus Corp |
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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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