Correlation Between EigenLayer and Pyth Network

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

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

Diversification Opportunities for EigenLayer and Pyth Network

0.44

Correlation Coefficient

EigenLayer  Pyth Network

Very weak diversification

The 3 months correlation between EigenLayer and Pyth is 0.44. Overlapping area represents the amount of risk that can be diversified away by holding EigenLayer and Pyth Network in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pyth Network and EigenLayer 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 EigenLayer 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 EigenLayer i.e., EigenLayer and Pyth Network go up and down completely randomly.

Pair Corralation between EigenLayer and Pyth Network

Assuming the 90 days trading horizon EigenLayer is expected to generate 1.24 times more return on investment than Pyth Network. However, EigenLayer is 1.24 times more volatile than Pyth Network. It trades about 0.02 of its potential returns per unit of risk. Pyth Network is currently generating about -0.1 per unit of risk. If you would invest  124.00  in EigenLayer on May 8, 2025 and sell it today you would lose (13.00) from holding EigenLayer or give up 10.48% of portfolio value over 90 days.

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

EigenLayer  vs.  Pyth Network

EigenLayer

Risk-Adjusted Performance

Weak

Compared to the overall equity markets, risk-adjusted returns on investments in EigenLayer are ranked lower than 1 (%) of all global equities and portfolios over the last 90 days. In spite of rather unsteady fundamental indicators, EigenLayer may actually be approaching a critical reversion point that can send shares even higher in September 2025.

Pyth Network

Risk-Adjusted Performance

Weakest

Over the last 90 days Pyth Network has generated negative risk-adjusted returns adding no value to investors with long positions. In spite of unsteady performance in the last few months, the Crypto's fundamental indicators remain rather sound which may send shares a bit higher in September 2025. The latest tumult may also be a sign of longer-term up-swing for Pyth Network shareholders.

Pair Trading with EigenLayer and Pyth Network

The main advantage of trading using opposite EigenLayer and Pyth Network positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if EigenLayer 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 EigenLayer 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.

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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 Portfolio Comparator module to compare the composition, asset allocations and performance of any two portfolios in your account.