Correlation Between Oxford Square and WhiteHorse Finance
Can any of the company-specific risk be diversified away by investing in both Oxford Square and WhiteHorse Finance 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 Oxford Square and WhiteHorse Finance into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Oxford Square Capital and WhiteHorse Finance, you can compare the effects of market volatilities on Oxford Square and WhiteHorse Finance 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 Oxford Square with a short position of WhiteHorse Finance. Check out your portfolio center. Please also check ongoing floating volatility patterns of Oxford Square and WhiteHorse Finance.
Diversification Opportunities for Oxford Square and WhiteHorse Finance
0.72 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Oxford and WhiteHorse is 0.72. Overlapping area represents the amount of risk that can be diversified away by holding Oxford Square Capital and WhiteHorse Finance in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on WhiteHorse Finance and Oxford Square 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 Oxford Square Capital are associated (or correlated) with WhiteHorse Finance. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of WhiteHorse Finance has no effect on the direction of Oxford Square i.e., Oxford Square and WhiteHorse Finance go up and down completely randomly.
Pair Corralation between Oxford Square and WhiteHorse Finance
Given the investment horizon of 90 days Oxford Square Capital is expected to generate 0.85 times more return on investment than WhiteHorse Finance. However, Oxford Square Capital is 1.18 times less risky than WhiteHorse Finance. It trades about -0.09 of its potential returns per unit of risk. WhiteHorse Finance is currently generating about -0.09 per unit of risk. If you would invest 283.00 in Oxford Square Capital on September 4, 2024 and sell it today you would lose (16.00) from holding Oxford Square Capital or give up 5.65% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Oxford Square Capital vs. WhiteHorse Finance
Performance |
Timeline |
Oxford Square Capital |
WhiteHorse Finance |
Oxford Square and WhiteHorse Finance Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Oxford Square and WhiteHorse Finance
The main advantage of trading using opposite Oxford Square and WhiteHorse Finance positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Oxford Square position performs unexpectedly, WhiteHorse Finance 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 WhiteHorse Finance will offset losses from the drop in WhiteHorse Finance's long position.Oxford Square vs. Eagle Point Credit | Oxford Square vs. Cornerstone Strategic Return | Oxford Square vs. Cornerstone Strategic Value | Oxford Square vs. Guggenheim Strategic Opportunities |
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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 Competition Analyzer module to analyze and compare many basic indicators for a group of related or unrelated entities.
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