Correlation Between NYSE Composite and ZOOZ Power

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

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

Diversification Opportunities for NYSE Composite and ZOOZ Power

0.54

Correlation Coefficient

NYSE Composite  ZOOZ Power

Very weak diversification

The 3 months correlation between NYSE and ZOOZ is 0.54. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and ZOOZ Power Ltd in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on ZOOZ Power and NYSE Composite 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 NYSE Composite are associated (or correlated) with ZOOZ Power. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of ZOOZ Power has no effect on the direction of NYSE Composite i.e., NYSE Composite and ZOOZ Power go up and down completely randomly.

Pair Corralation between NYSE Composite and ZOOZ Power

Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the ZOOZ Power. But the index apears to be less risky and, when comparing its historical volatility, NYSE Composite is 6.36 times less risky than ZOOZ Power. The index trades about -0.04 of its potential returns per unit of risk. The ZOOZ Power Ltd is currently generating about 0.19 of returns per unit of risk over similar time horizon. If you would invest  180.00  in ZOOZ Power Ltd on September 22, 2024 and sell it today you would earn a total of  102.00  from holding ZOOZ Power Ltd or generate 56.67% return on investment over 90 days.

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

NYSE Composite  vs.  ZOOZ Power Ltd

Pair Trading with NYSE Composite and ZOOZ Power

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

The idea behind NYSE Composite and ZOOZ Power Ltd 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.