Correlation Between Pyth Network and Tensor

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

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

Diversification Opportunities for Pyth Network and Tensor

0.47

Correlation Coefficient

Pyth Network  Tensor

Very weak diversification

The 3 months correlation between Pyth and Tensor is 0.47. Overlapping area represents the amount of risk that can be diversified away by holding Pyth Network and Tensor in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Tensor 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 Tensor. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Tensor has no effect on the direction of Pyth Network i.e., Pyth Network and Tensor go up and down completely randomly.

Pair Corralation between Pyth Network and Tensor

Assuming the 90 days trading horizon Pyth Network is expected to under-perform the Tensor. But the crypto coin apears to be less risky and, when comparing its historical volatility, Pyth Network is 1.18 times less risky than Tensor. The crypto coin trades about -0.16 of its potential returns per unit of risk. The Tensor is currently generating about -0.14 of returns per unit of risk over similar time horizon. If you would invest  43.00  in Tensor on December 30, 2024 and sell it today you would lose (27.00) from holding Tensor or give up 62.79% of portfolio value over 90 days.

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

Pyth Network  vs.  Tensor

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 unsteady performance in the last few months, the Crypto's fundamental indicators remain rather sound which may send shares a bit higher in April 2025. The latest tumult may also be a sign of longer-term up-swing for Pyth Network shareholders.

Tensor

Risk-Adjusted Performance

Very Weak

Over the last 90 days Tensor 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 April 2025. The latest tumult may also be a sign of longer-term up-swing for Tensor shareholders.

Pair Trading with Pyth Network and Tensor

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

The idea behind Pyth Network and Tensor 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.