Correlation Between GM and Meta Data

Can any of the company-specific risk be diversified away by investing in both GM and Meta Data 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 Meta Data into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between General Motors and Meta Data, you can compare the effects of market volatilities on GM and Meta Data 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 Meta Data. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Meta Data.

Diversification Opportunities for GM and Meta Data

-0.01

Correlation Coefficient

GM  Meta Data

Good diversification

The 3 months correlation between GM and Meta is -0.01. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Meta Data in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Meta Data 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 Meta Data. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Meta Data has no effect on the direction of GM i.e., GM and Meta Data go up and down completely randomly.

Pair Corralation between GM and Meta Data

Allowing for the 90-day total investment horizon General Motors is expected to generate 0.24 times more return on investment than Meta Data. However, General Motors is 4.19 times less risky than Meta Data. It trades about 0.05 of its potential returns per unit of risk. Meta Data is currently generating about -0.06 per unit of risk. If you would invest  3,312  in General Motors on September 21, 2024 and sell it today you would earn a total of  1,722  from holding General Motors or generate 51.99% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Against
Strength	Insignificant
Accuracy	82.42%
Values	Daily Returns

General Motors  vs.  Meta Data

General Motors

Risk-Adjusted Performance

3 of 100

Weak

Compared to the overall equity markets, risk-adjusted returns on investments in General Motors are ranked lower than 3 (%) of all global equities and portfolios over the last 90 days. In spite of very weak primary indicators, GM may actually be approaching a critical reversion point that can send shares even higher in January 2025.

Meta Data

Risk-Adjusted Performance

0 of 100

Very Weak

Over the last 90 days Meta Data has generated negative risk-adjusted returns adding no value to investors with long positions. In spite of comparatively stable forward indicators, Meta Data is not utilizing all of its potentials. The newest stock price uproar, may contribute to short-horizon losses for the private investors.

Pair Trading with GM and Meta Data

The main advantage of trading using opposite GM and Meta Data positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Meta Data 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 Meta Data will offset losses from the drop in Meta Data's long position.

The idea behind General Motors and Meta Data 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.

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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 Correlation Analysis module to reduce portfolio risk simply by holding instruments which are not perfectly correlated.