Correlation Between Globalfoundries and Monolithic Power
Can any of the company-specific risk be diversified away by investing in both Globalfoundries and Monolithic Power 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 Globalfoundries and Monolithic Power into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Globalfoundries and Monolithic Power Systems, you can compare the effects of market volatilities on Globalfoundries and Monolithic Power 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 Globalfoundries with a short position of Monolithic Power. Check out your portfolio center. Please also check ongoing floating volatility patterns of Globalfoundries and Monolithic Power.
Diversification Opportunities for Globalfoundries and Monolithic Power
-0.36 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Globalfoundries and Monolithic is -0.36. Overlapping area represents the amount of risk that can be diversified away by holding Globalfoundries and Monolithic Power Systems in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Monolithic Power Systems and Globalfoundries 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 Globalfoundries are associated (or correlated) with Monolithic Power. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Monolithic Power Systems has no effect on the direction of Globalfoundries i.e., Globalfoundries and Monolithic Power go up and down completely randomly.
Pair Corralation between Globalfoundries and Monolithic Power
Considering the 90-day investment horizon Globalfoundries is expected to generate 0.89 times more return on investment than Monolithic Power. However, Globalfoundries is 1.13 times less risky than Monolithic Power. It trades about 0.02 of its potential returns per unit of risk. Monolithic Power Systems is currently generating about -0.14 per unit of risk. If you would invest 4,268 in Globalfoundries on September 2, 2024 and sell it today you would earn a total of 57.00 from holding Globalfoundries or generate 1.34% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Globalfoundries vs. Monolithic Power Systems
Performance |
Timeline |
Globalfoundries |
Monolithic Power Systems |
Globalfoundries and Monolithic Power Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Globalfoundries and Monolithic Power
The main advantage of trading using opposite Globalfoundries and Monolithic Power positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Globalfoundries position performs unexpectedly, Monolithic Power 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 Monolithic Power will offset losses from the drop in Monolithic Power's long position.Globalfoundries vs. NXP Semiconductors NV | Globalfoundries vs. Analog Devices | Globalfoundries vs. ON Semiconductor | Globalfoundries vs. Lattice Semiconductor |
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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