Correlation Between Bridgford Foods and FactSet Research
Can any of the company-specific risk be diversified away by investing in both Bridgford Foods and FactSet Research 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 Bridgford Foods and FactSet Research into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bridgford Foods and FactSet Research Systems, you can compare the effects of market volatilities on Bridgford Foods and FactSet Research 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 Bridgford Foods with a short position of FactSet Research. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bridgford Foods and FactSet Research.
Diversification Opportunities for Bridgford Foods and FactSet Research
0.48 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Bridgford and FactSet is 0.48. Overlapping area represents the amount of risk that can be diversified away by holding Bridgford Foods and FactSet Research Systems in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FactSet Research Systems and Bridgford Foods 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 Bridgford Foods are associated (or correlated) with FactSet Research. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FactSet Research Systems has no effect on the direction of Bridgford Foods i.e., Bridgford Foods and FactSet Research go up and down completely randomly.
Pair Corralation between Bridgford Foods and FactSet Research
Given the investment horizon of 90 days Bridgford Foods is expected to generate 1.52 times more return on investment than FactSet Research. However, Bridgford Foods is 1.52 times more volatile than FactSet Research Systems. It trades about 0.25 of its potential returns per unit of risk. FactSet Research Systems is currently generating about -0.12 per unit of risk. If you would invest 976.00 in Bridgford Foods on October 10, 2024 and sell it today you would earn a total of 104.00 from holding Bridgford Foods or generate 10.66% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Bridgford Foods vs. FactSet Research Systems
Performance |
Timeline |
Bridgford Foods |
FactSet Research Systems |
Bridgford Foods and FactSet Research Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bridgford Foods and FactSet Research
The main advantage of trading using opposite Bridgford Foods and FactSet Research positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bridgford Foods position performs unexpectedly, FactSet Research 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 FactSet Research will offset losses from the drop in FactSet Research's long position.Bridgford Foods vs. J J Snack | Bridgford Foods vs. Central Garden Pet | Bridgford Foods vs. Central Garden Pet | Bridgford Foods vs. Lancaster Colony |
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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 Portfolio Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.
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