Correlation Between NYSE Composite and National Bank

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

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

Diversification Opportunities for NYSE Composite and National Bank

0.84

Correlation Coefficient

NYSE Composite  National Bank

Very poor diversification

The 3 months correlation between NYSE and National is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and National Bank of in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on National Bank 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 National Bank. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of National Bank has no effect on the direction of NYSE Composite i.e., NYSE Composite and National Bank go up and down completely randomly.

Pair Corralation between NYSE Composite and National Bank

Assuming the 90 days trading horizon NYSE Composite is expected to generate about the same return on investment as National Bank of. But, NYSE Composite is 1.3 times less risky than National Bank. It trades about -0.07 of its potential returns per unit of risk. National Bank of is currently generating about -0.06 per unit of risk. If you would invest  9,478  in National Bank of on October 12, 2024 and sell it today you would lose (282.00) from holding National Bank of or give up 2.98% of portfolio value over 90 days.

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

NYSE Composite  vs.  National Bank of

Pair Trading with NYSE Composite and National Bank

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

The idea behind NYSE Composite and National Bank of 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.