Correlation Between Bats Series and Sterling Capital
Can any of the company-specific risk be diversified away by investing in both Bats Series and Sterling Capital 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 Bats Series and Sterling Capital into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bats Series M and Sterling Capital Total, you can compare the effects of market volatilities on Bats Series and Sterling Capital 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 Bats Series with a short position of Sterling Capital. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bats Series and Sterling Capital.
Diversification Opportunities for Bats Series and Sterling Capital
0.96 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Bats and Sterling is 0.96. Overlapping area represents the amount of risk that can be diversified away by holding Bats Series M and Sterling Capital Total in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sterling Capital Total and Bats Series 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 Bats Series M are associated (or correlated) with Sterling Capital. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sterling Capital Total has no effect on the direction of Bats Series i.e., Bats Series and Sterling Capital go up and down completely randomly.
Pair Corralation between Bats Series and Sterling Capital
Assuming the 90 days horizon Bats Series is expected to generate 1.25 times less return on investment than Sterling Capital. In addition to that, Bats Series is 1.54 times more volatile than Sterling Capital Total. It trades about 0.04 of its total potential returns per unit of risk. Sterling Capital Total is currently generating about 0.08 per unit of volatility. If you would invest 952.00 in Sterling Capital Total on September 14, 2024 and sell it today you would earn a total of 51.00 from holding Sterling Capital Total or generate 5.36% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 99.6% |
Values | Daily Returns |
Bats Series M vs. Sterling Capital Total
Performance |
Timeline |
Bats Series M |
Sterling Capital Total |
Bats Series and Sterling Capital Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bats Series and Sterling Capital
The main advantage of trading using opposite Bats Series and Sterling Capital positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bats Series position performs unexpectedly, Sterling Capital 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 Sterling Capital will offset losses from the drop in Sterling Capital's long position.Bats Series vs. Huber Capital Diversified | Bats Series vs. Fidelity Advisor Diversified | Bats Series vs. Pgim Jennison Diversified | Bats Series vs. Sentinel Small Pany |
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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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