Correlation Between NYSE Composite and Lake Shore

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

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

Diversification Opportunities for NYSE Composite and Lake Shore

0.36

Correlation Coefficient

NYSE Composite  Lake Shore

Weak diversification

The 3 months correlation between NYSE and Lake is 0.36. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Lake Shore Bancorp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Lake Shore Bancorp 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 Lake Shore. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Lake Shore Bancorp has no effect on the direction of NYSE Composite i.e., NYSE Composite and Lake Shore go up and down completely randomly.

Pair Corralation between NYSE Composite and Lake Shore

Assuming the 90 days trading horizon NYSE Composite is expected to generate 17.36 times less return on investment than Lake Shore. But when comparing it to its historical volatility, NYSE Composite is 3.67 times less risky than Lake Shore. It trades about 0.02 of its potential returns per unit of risk. Lake Shore Bancorp is currently generating about 0.11 of returns per unit of risk over similar time horizon. If you would invest  1,358  in Lake Shore Bancorp on December 28, 2024 and sell it today you would earn a total of  254.00  from holding Lake Shore Bancorp or generate 18.7% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Very Weak
Accuracy	93.44%
Values	Daily Returns

NYSE Composite  vs.  Lake Shore Bancorp

Pair Trading with NYSE Composite and Lake Shore

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

The idea behind NYSE Composite and Lake Shore Bancorp 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.