Correlation Between Oxford Lane and Alliancebernstein
Can any of the company-specific risk be diversified away by investing in both Oxford Lane and Alliancebernstein 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 Lane and Alliancebernstein into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Oxford Lane Capital and Alliancebernstein Global High, you can compare the effects of market volatilities on Oxford Lane and Alliancebernstein 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 Lane with a short position of Alliancebernstein. Check out your portfolio center. Please also check ongoing floating volatility patterns of Oxford Lane and Alliancebernstein.
Diversification Opportunities for Oxford Lane and Alliancebernstein
-0.42 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Oxford and Alliancebernstein is -0.42. Overlapping area represents the amount of risk that can be diversified away by holding Oxford Lane Capital and Alliancebernstein Global High in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Alliancebernstein and Oxford Lane 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 Lane Capital are associated (or correlated) with Alliancebernstein. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Alliancebernstein has no effect on the direction of Oxford Lane i.e., Oxford Lane and Alliancebernstein go up and down completely randomly.
Pair Corralation between Oxford Lane and Alliancebernstein
Given the investment horizon of 90 days Oxford Lane Capital is expected to under-perform the Alliancebernstein. In addition to that, Oxford Lane is 1.31 times more volatile than Alliancebernstein Global High. It trades about -0.02 of its total potential returns per unit of risk. Alliancebernstein Global High is currently generating about 0.07 per unit of volatility. If you would invest 1,083 in Alliancebernstein Global High on September 13, 2024 and sell it today you would earn a total of 6.00 from holding Alliancebernstein Global High or generate 0.55% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 95.45% |
Values | Daily Returns |
Oxford Lane Capital vs. Alliancebernstein Global High
Performance |
Timeline |
Oxford Lane Capital |
Alliancebernstein |
Oxford Lane and Alliancebernstein Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Oxford Lane and Alliancebernstein
The main advantage of trading using opposite Oxford Lane and Alliancebernstein positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Oxford Lane position performs unexpectedly, Alliancebernstein 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 Alliancebernstein will offset losses from the drop in Alliancebernstein's long position.Oxford Lane vs. Capital Southwest | Oxford Lane vs. XAI Octagon Floating | Oxford Lane vs. Cornerstone Strategic Return | Oxford Lane vs. Cornerstone Strategic Value |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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