Correlation Between Pyth Network and Frax Share
Can any of the company-specific risk be diversified away by investing in both Pyth Network and Frax Share 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 Pyth Network and Frax Share into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Pyth Network and Frax Share, you can compare the effects of market volatilities on Pyth Network and Frax Share 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 Pyth Network with a short position of Frax Share. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pyth Network and Frax Share.
Diversification Opportunities for Pyth Network and Frax Share
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Pyth and Frax is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Pyth Network and Frax Share in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Frax Share and Pyth Network 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 Pyth Network are associated (or correlated) with Frax Share. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Frax Share has no effect on the direction of Pyth Network i.e., Pyth Network and Frax Share go up and down completely randomly.
Pair Corralation between Pyth Network and Frax Share
Assuming the 90 days trading horizon Pyth Network is expected to generate 2.6 times less return on investment than Frax Share. But when comparing it to its historical volatility, Pyth Network is 1.11 times less risky than Frax Share. It trades about 0.12 of its potential returns per unit of risk. Frax Share is currently generating about 0.29 of returns per unit of risk over similar time horizon. If you would invest 198.00 in Frax Share on August 27, 2024 and sell it today you would earn a total of 66.00 from holding Frax Share or generate 33.33% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Pyth Network vs. Frax Share
Performance |
| Timeline |
| Pyth Network |
| Frax Share |
Pyth Network and Frax Share Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Pyth Network and Frax Share
The main advantage of trading using opposite Pyth Network and Frax Share positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pyth Network position performs unexpectedly, Frax Share 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 Frax Share will offset losses from the drop in Frax Share's long position.The idea behind Pyth Network and Frax Share 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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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