Correlation Between Ontology and JNT

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

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

Diversification Opportunities for Ontology and JNT

0.13

Correlation Coefficient

Ontology  JNT

Average diversification

The 3 months correlation between Ontology and JNT is 0.13. Overlapping area represents the amount of risk that can be diversified away by holding Ontology and JNT in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on JNT and Ontology 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 Ontology are associated (or correlated) with JNT. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of JNT has no effect on the direction of Ontology i.e., Ontology and JNT go up and down completely randomly.

Pair Corralation between Ontology and JNT

If you would invest  16.00  in Ontology on August 30, 2024 and sell it today you would earn a total of  11.00  from holding Ontology or generate 68.75% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Insignificant
Accuracy	1.56%
Values	Daily Returns

Ontology  vs.  JNT

Ontology

Risk-Adjusted Performance

14 of 100

Good

Compared to the overall equity markets, risk-adjusted returns on investments in Ontology are ranked lower than 14 (%) of all global equities and portfolios over the last 90 days. In spite of rather unsteady basic indicators, Ontology exhibited solid returns over the last few months and may actually be approaching a breakup point.

JNT

Risk-Adjusted Performance

0 of 100

Very Weak

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

Pair Trading with Ontology and JNT

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

The idea behind Ontology and JNT 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.