Correlation Between NYSE Composite and Oxford Square

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

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

Diversification Opportunities for NYSE Composite and Oxford Square

0.49

Correlation Coefficient

NYSE Composite  Oxford Square

Very weak diversification

The 3 months correlation between NYSE and Oxford is 0.49. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Oxford Square Capital in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Oxford Square Capital 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 Oxford Square. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Oxford Square Capital has no effect on the direction of NYSE Composite i.e., NYSE Composite and Oxford Square go up and down completely randomly.

Pair Corralation between NYSE Composite and Oxford Square

Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the Oxford Square. In addition to that, NYSE Composite is 1.07 times more volatile than Oxford Square Capital. It trades about -0.11 of its total potential returns per unit of risk. Oxford Square Capital is currently generating about -0.02 per unit of volatility. If you would invest  2,321  in Oxford Square Capital on September 19, 2024 and sell it today you would lose (4.00) from holding Oxford Square Capital or give up 0.17% of portfolio value over 90 days.

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

NYSE Composite  vs.  Oxford Square Capital

Pair Trading with NYSE Composite and Oxford Square

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

The idea behind NYSE Composite and Oxford Square Capital 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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.