Correlation Between NYSE Composite and John Hancock
Can any of the company-specific risk be diversified away by investing in both NYSE Composite and John Hancock 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 John Hancock into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NYSE Composite and John Hancock Income, you can compare the effects of market volatilities on NYSE Composite and John Hancock 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 John Hancock. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and John Hancock.
Diversification Opportunities for NYSE Composite and John Hancock
-0.39 | Correlation Coefficient |
Very good diversification
The 3 months correlation between NYSE and John is -0.39. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and John Hancock Income in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on John Hancock Income 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 John Hancock. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of John Hancock Income has no effect on the direction of NYSE Composite i.e., NYSE Composite and John Hancock go up and down completely randomly.
Pair Corralation between NYSE Composite and John Hancock
Assuming the 90 days trading horizon NYSE Composite is expected to generate 3.02 times more return on investment than John Hancock. However, NYSE Composite is 3.02 times more volatile than John Hancock Income. It trades about 0.14 of its potential returns per unit of risk. John Hancock Income is currently generating about 0.12 per unit of risk. If you would invest 1,804,550 in NYSE Composite on September 3, 2024 and sell it today you would earn a total of 222,654 from holding NYSE Composite or generate 12.34% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
NYSE Composite vs. John Hancock Income
Performance |
Timeline |
NYSE Composite and John Hancock Volatility Contrast
Predicted Return Density |
Returns |
NYSE Composite
Pair trading matchups for NYSE Composite
John Hancock Income
Pair trading matchups for John Hancock
Pair Trading with NYSE Composite and John Hancock
The main advantage of trading using opposite NYSE Composite and John Hancock positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, John Hancock 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 John Hancock will offset losses from the drop in John Hancock's long position.NYSE Composite vs. Lindblad Expeditions Holdings | NYSE Composite vs. LB Foster | NYSE Composite vs. HUTCHMED DRC | NYSE Composite vs. Bridgford Foods |
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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