Correlation Between GM and Pyth Network
Can any of the company-specific risk be diversified away by investing in both GM and Pyth Network 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 Pyth Network into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and Pyth Network, you can compare the effects of market volatilities on GM and Pyth Network 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 Pyth Network. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Pyth Network.
Diversification Opportunities for GM and Pyth Network
0.79 | Correlation Coefficient |
Poor diversification
The 3 months correlation between GM and Pyth is 0.79. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Pyth Network in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pyth Network 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 Pyth Network. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pyth Network has no effect on the direction of GM i.e., GM and Pyth Network go up and down completely randomly.
Pair Corralation between GM and Pyth Network
Allowing for the 90-day total investment horizon GM is expected to generate 3.54 times less return on investment than Pyth Network. But when comparing it to its historical volatility, General Motors is 1.69 times less risky than Pyth Network. It trades about 0.12 of its potential returns per unit of risk. Pyth Network is currently generating about 0.25 of returns per unit of risk over similar time horizon. If you would invest 37.00 in Pyth Network on August 31, 2024 and sell it today you would earn a total of 10.00 from holding Pyth Network or generate 27.03% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
General Motors vs. Pyth Network
Performance |
| Timeline |
| General Motors |
| Pyth Network |
GM and Pyth Network Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GM and Pyth Network
The main advantage of trading using opposite GM and Pyth Network positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Pyth Network 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 Pyth Network will offset losses from the drop in Pyth Network's long position.The idea behind General Motors and Pyth Network 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.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 Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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