Correlation Between Biglari Holdings and Yuexiu Transport
Can any of the company-specific risk be diversified away by investing in both Biglari Holdings and Yuexiu Transport 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 Biglari Holdings and Yuexiu Transport into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Biglari Holdings and Yuexiu Transport Infrastructure, you can compare the effects of market volatilities on Biglari Holdings and Yuexiu Transport 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 Biglari Holdings with a short position of Yuexiu Transport. Check out your portfolio center. Please also check ongoing floating volatility patterns of Biglari Holdings and Yuexiu Transport.
Diversification Opportunities for Biglari Holdings and Yuexiu Transport
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Biglari and Yuexiu is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Biglari Holdings and Yuexiu Transport Infrastructur in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Yuexiu Transport Inf and Biglari Holdings 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 Biglari Holdings are associated (or correlated) with Yuexiu Transport. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Yuexiu Transport Inf has no effect on the direction of Biglari Holdings i.e., Biglari Holdings and Yuexiu Transport go up and down completely randomly.
Pair Corralation between Biglari Holdings and Yuexiu Transport
Allowing for the 90-day total investment horizon Biglari Holdings is expected to generate 0.65 times more return on investment than Yuexiu Transport. However, Biglari Holdings is 1.54 times less risky than Yuexiu Transport. It trades about 0.21 of its potential returns per unit of risk. Yuexiu Transport Infrastructure is currently generating about 0.13 per unit of risk. If you would invest 17,525 in Biglari Holdings on September 17, 2024 and sell it today you would earn a total of 5,558 from holding Biglari Holdings or generate 31.71% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 98.46% |
Values | Daily Returns |
Biglari Holdings vs. Yuexiu Transport Infrastructur
Performance |
Timeline |
Biglari Holdings |
Yuexiu Transport Inf |
Biglari Holdings and Yuexiu Transport Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Biglari Holdings and Yuexiu Transport
The main advantage of trading using opposite Biglari Holdings and Yuexiu Transport positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Biglari Holdings position performs unexpectedly, Yuexiu Transport 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 Yuexiu Transport will offset losses from the drop in Yuexiu Transport's long position.Biglari Holdings vs. Cannae Holdings | Biglari Holdings vs. BJs Restaurants | Biglari Holdings vs. Ark Restaurants Corp | Biglari Holdings vs. Noble Romans |
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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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