Correlation Between Ford and DY6 Metals
Can any of the company-specific risk be diversified away by investing in both Ford and DY6 Metals 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 DY6 Metals into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ford Motor and DY6 Metals, you can compare the effects of market volatilities on Ford and DY6 Metals 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 DY6 Metals. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ford and DY6 Metals.
Diversification Opportunities for Ford and DY6 Metals
0.17 | Correlation Coefficient |
Average diversification
The 3 months correlation between Ford and DY6 is 0.17. Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor and DY6 Metals in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DY6 Metals 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 DY6 Metals. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DY6 Metals has no effect on the direction of Ford i.e., Ford and DY6 Metals go up and down completely randomly.
Pair Corralation between Ford and DY6 Metals
Taking into account the 90-day investment horizon Ford Motor is expected to generate 0.62 times more return on investment than DY6 Metals. However, Ford Motor is 1.62 times less risky than DY6 Metals. It trades about 0.04 of its potential returns per unit of risk. DY6 Metals is currently generating about -0.27 per unit of risk. If you would invest 1,122 in Ford Motor on August 27, 2024 and sell it today you would earn a total of 18.00 from holding Ford Motor or generate 1.6% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Ford Motor vs. DY6 Metals
Performance |
| Timeline |
| Ford Motor |
| DY6 Metals |
Ford and DY6 Metals Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Ford and DY6 Metals
The main advantage of trading using opposite Ford and DY6 Metals positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ford position performs unexpectedly, DY6 Metals 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 DY6 Metals will offset losses from the drop in DY6 Metals' long position.The idea behind Ford Motor and DY6 Metals 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.| DY6 Metals vs. Northern Star Resources | DY6 Metals vs. Evolution Mining | DY6 Metals vs. Bluescope Steel | DY6 Metals vs. Sandfire Resources NL |
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 Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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