Correlation Between GM and OFS Credit
Can any of the company-specific risk be diversified away by investing in both GM and OFS Credit 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 GM and OFS Credit into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and OFS Credit, you can compare the effects of market volatilities on GM and OFS Credit 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 GM with a short position of OFS Credit. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and OFS Credit.
Diversification Opportunities for GM and OFS Credit
-0.52 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between GM and OFS is -0.52. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and OFS Credit in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on OFS Credit and GM 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 General Motors are associated (or correlated) with OFS Credit. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of OFS Credit has no effect on the direction of GM i.e., GM and OFS Credit go up and down completely randomly.
Pair Corralation between GM and OFS Credit
Allowing for the 90-day total investment horizon General Motors is expected to under-perform the OFS Credit. In addition to that, GM is 5.77 times more volatile than OFS Credit. It trades about -0.01 of its total potential returns per unit of risk. OFS Credit is currently generating about 0.1 per unit of volatility. If you would invest 2,417 in OFS Credit on December 26, 2024 and sell it today you would earn a total of 64.00 from holding OFS Credit or generate 2.65% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
General Motors vs. OFS Credit
Performance |
Timeline |
General Motors |
OFS Credit |
GM and OFS Credit Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GM and OFS Credit
The main advantage of trading using opposite GM and OFS Credit positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, OFS Credit 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 OFS Credit will offset losses from the drop in OFS Credit's long position.The idea behind General Motors and OFS Credit 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.OFS Credit vs. Oxford Lane Capital | OFS Credit vs. OFS Credit | OFS Credit vs. XOMA Corporation | OFS Credit vs. Oxford Lane Capital |
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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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