Correlation Between GM and SVI Public
Can any of the company-specific risk be diversified away by investing in both GM and SVI Public 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 SVI Public into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and SVI Public, you can compare the effects of market volatilities on GM and SVI Public 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 SVI Public. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and SVI Public.
Diversification Opportunities for GM and SVI Public
-0.15 | Correlation Coefficient |
Good diversification
The 3 months correlation between GM and SVI is -0.15. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and SVI Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SVI Public 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 SVI Public. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SVI Public has no effect on the direction of GM i.e., GM and SVI Public go up and down completely randomly.
Pair Corralation between GM and SVI Public
Allowing for the 90-day total investment horizon General Motors is expected to under-perform the SVI Public. In addition to that, GM is 1.2 times more volatile than SVI Public. It trades about -0.01 of its total potential returns per unit of risk. SVI Public is currently generating about 0.01 per unit of volatility. If you would invest 735.00 in SVI Public on December 25, 2024 and sell it today you would earn a total of 0.00 from holding SVI Public or generate 0.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 96.77% |
Values | Daily Returns |
General Motors vs. SVI Public
Performance |
Timeline |
General Motors |
SVI Public |
GM and SVI Public Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GM and SVI Public
The main advantage of trading using opposite GM and SVI Public positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, SVI Public 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 SVI Public will offset losses from the drop in SVI Public's long position.The idea behind General Motors and SVI Public 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.SVI Public vs. KCE Electronics Public | SVI Public vs. Hana Microelectronics Public | SVI Public vs. Precious Shipping Public | SVI Public vs. Siri Prime Office |
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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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