Correlation Between GM and Netlist
Can any of the company-specific risk be diversified away by investing in both GM and Netlist 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 Netlist into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and Netlist, you can compare the effects of market volatilities on GM and Netlist 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 Netlist. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Netlist.
Diversification Opportunities for GM and Netlist
Pay attention - limited upside
The 3 months correlation between GM and Netlist is -0.83. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Netlist in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Netlist 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 Netlist. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Netlist has no effect on the direction of GM i.e., GM and Netlist go up and down completely randomly.
Pair Corralation between GM and Netlist
Allowing for the 90-day total investment horizon GM is expected to generate 8.52 times less return on investment than Netlist. But when comparing it to its historical volatility, General Motors is 3.62 times less risky than Netlist. It trades about 0.04 of its potential returns per unit of risk. Netlist is currently generating about 0.1 of returns per unit of risk over similar time horizon. If you would invest 197.00 in Netlist on September 4, 2024 and sell it today you would earn a total of 130.00 from holding Netlist or generate 65.99% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Significant |
| Accuracy | 24.62% |
| Values | Daily Returns |
General Motors vs. Netlist
Performance |
| Timeline |
| General Motors |
| Netlist |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
GM and Netlist Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GM and Netlist
The main advantage of trading using opposite GM and Netlist positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Netlist 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 Netlist will offset losses from the drop in Netlist's long position.The idea behind General Motors and Netlist 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.| Netlist vs. Wisekey International Holding | Netlist vs. Nano Labs | Netlist vs. SemiLEDS | Netlist vs. WiSA Technologies |
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 Equity Valuation module to check real value of public entities based on technical and fundamental data.
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