Correlation Between Polygon and Hive

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

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

Diversification Opportunities for Polygon and Hive

0.78

Correlation Coefficient

Polygon  Hive

Poor diversification

The 3 months correlation between Polygon and Hive is 0.78. Overlapping area represents the amount of risk that can be diversified away by holding Polygon and Hive in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hive and Polygon 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 Polygon are associated (or correlated) with Hive. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hive has no effect on the direction of Polygon i.e., Polygon and Hive go up and down completely randomly.

Pair Corralation between Polygon and Hive

Assuming the 90 days trading horizon Polygon is expected to generate 1.02 times more return on investment than Hive. However, Polygon is 1.02 times more volatile than Hive. It trades about 0.02 of its potential returns per unit of risk. Hive is currently generating about 0.01 per unit of risk. If you would invest  89.00  in Polygon on January 27, 2024 and sell it today you would lose (17.00) from holding Polygon or give up 19.1% of portfolio value over 90 days.

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

Polygon  vs.  Hive

Polygon

Risk-Adjusted Performance

0 of 100

Very Weak

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

Hive

Risk-Adjusted Performance

3 of 100

Insignificant

Compared to the overall equity markets, risk-adjusted returns on investments in Hive are ranked lower than 3 (%) of all global equities and portfolios over the last 90 days. In spite of rather unsteady basic indicators, Hive exhibited solid returns over the last few months and may actually be approaching a breakup point.

Pair Trading with Polygon and Hive

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

The idea behind Polygon and Hive 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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.