Correlation Between Ford and Meta Data

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

By analyzing existing cross correlation between Ford Motor and Meta Data, you can compare the effects of market volatilities on Ford 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 Ford with a short position of Meta Data. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ford and Meta Data.

Diversification Opportunities for Ford and Meta Data

0.4

Correlation Coefficient

Ford  Meta Data

Very weak diversification

The 3 months correlation between Ford and Meta is 0.4. Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor and Meta Data in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Meta Data 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 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 Ford i.e., Ford and Meta Data go up and down completely randomly.

Pair Corralation between Ford and Meta Data

Taking into account the 90-day investment horizon Ford Motor is expected to generate 0.26 times more return on investment than Meta Data. However, Ford Motor is 3.83 times less risky than Meta Data. It trades about 0.01 of its potential returns per unit of risk. Meta Data is currently generating about -0.06 per unit of risk. If you would invest  988.00  in Ford Motor on September 21, 2024 and sell it today you would lose (19.00) from holding Ford Motor or give up 1.92% of portfolio value over 90 days.

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

Ford Motor  vs.  Meta Data

Ford Motor

Risk-Adjusted Performance

0 of 100

Very Weak

Over the last 90 days Ford Motor has generated negative risk-adjusted returns adding no value to investors with long positions. Despite latest unfluctuating performance, the Stock's technical and fundamental indicators remain stable and the current disturbance on Wall Street may also be a sign of long-run gains for the company stockholders.

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 Ford and Meta Data

The main advantage of trading using opposite Ford and Meta Data positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ford 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 Ford Motor 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 AI Portfolio Architect module to use AI to generate optimal portfolios and find profitable investment opportunities.