Correlation Between Pyth Network and EigenLayer

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

By analyzing existing cross correlation between Pyth Network and EigenLayer, you can compare the effects of market volatilities on Pyth Network and EigenLayer 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 EigenLayer. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pyth Network and EigenLayer.

Diversification Opportunities for Pyth Network and EigenLayer

0.21

Correlation Coefficient

Pyth Network  EigenLayer

Modest diversification

The 3 months correlation between Pyth and EigenLayer is 0.21. Overlapping area represents the amount of risk that can be diversified away by holding Pyth Network and EigenLayer in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on EigenLayer 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 EigenLayer. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of EigenLayer has no effect on the direction of Pyth Network i.e., Pyth Network and EigenLayer go up and down completely randomly.

Pair Corralation between Pyth Network and EigenLayer

Assuming the 90 days trading horizon Pyth Network is expected to generate 24.57 times less return on investment than EigenLayer. But when comparing it to its historical volatility, Pyth Network is 1.36 times less risky than EigenLayer. It trades about 0.01 of its potential returns per unit of risk. EigenLayer is currently generating about 0.09 of returns per unit of risk over similar time horizon. If you would invest  101.00  in EigenLayer on April 30, 2025 and sell it today you would earn a total of  35.00  from holding EigenLayer or generate 34.65% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Very Weak
Accuracy	100.0%
Values	Daily Returns

Pyth Network  vs.  EigenLayer

Pyth Network

Risk-Adjusted Performance

Very Weak

Over the last 90 days Pyth Network has generated negative risk-adjusted returns adding no value to investors with long positions. In spite of rather sound fundamental indicators, Pyth Network is not utilizing all of its potentials. The latest stock price tumult, may contribute to shorter-term losses for the shareholders.

EigenLayer

Risk-Adjusted Performance

OK

Compared to the overall equity markets, risk-adjusted returns on investments in EigenLayer are ranked lower than 7 (%) of all global equities and portfolios over the last 90 days. In spite of rather unsteady fundamental indicators, EigenLayer exhibited solid returns over the last few months and may actually be approaching a breakup point.

Pair Trading with Pyth Network and EigenLayer

The main advantage of trading using opposite Pyth Network and EigenLayer positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pyth Network position performs unexpectedly, EigenLayer 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 EigenLayer will offset losses from the drop in EigenLayer's long position.

The idea behind Pyth Network and EigenLayer 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 Sign In To Macroaxis module to sign in to explore Macroaxis' wealth optimization platform and fintech modules.