Correlation Between Ford and Packages
Can any of the company-specific risk be diversified away by investing in both Ford and Packages 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 Packages into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ford Motor and Packages, you can compare the effects of market volatilities on Ford and Packages 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 Packages. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ford and Packages.
Diversification Opportunities for Ford and Packages
Very weak diversification
The 3 months correlation between Ford and Packages is 0.51. Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor and Packages in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Packages 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 Packages. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Packages has no effect on the direction of Ford i.e., Ford and Packages go up and down completely randomly.
Pair Corralation between Ford and Packages
Taking into account the 90-day investment horizon Ford is expected to generate 10.84 times less return on investment than Packages. But when comparing it to its historical volatility, Ford Motor is 1.13 times less risky than Packages. It trades about 0.04 of its potential returns per unit of risk. Packages is currently generating about 0.39 of returns per unit of risk over similar time horizon. If you would invest 44,435 in Packages on August 28, 2024 and sell it today you would earn a total of 12,364 from holding Packages or generate 27.82% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Ford Motor vs. Packages
Performance |
| Timeline |
| Ford Motor |
| Packages |
Ford and Packages Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Ford and Packages
The main advantage of trading using opposite Ford and Packages positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ford position performs unexpectedly, Packages 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 Packages will offset losses from the drop in Packages' long position.The idea behind Ford Motor and Packages 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.| Packages vs. Unilever Pakistan Foods | Packages vs. Pakistan Aluminium Beverage | Packages vs. National Foods | Packages vs. Big Bird Foods |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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