Correlation Between DATA MODUL and CAL-MAINE FOODS
Can any of the company-specific risk be diversified away by investing in both DATA MODUL and CAL-MAINE FOODS 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 DATA MODUL and CAL-MAINE FOODS into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DATA MODUL and CAL MAINE FOODS, you can compare the effects of market volatilities on DATA MODUL and CAL-MAINE FOODS 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 DATA MODUL with a short position of CAL-MAINE FOODS. Check out your portfolio center. Please also check ongoing floating volatility patterns of DATA MODUL and CAL-MAINE FOODS.
Diversification Opportunities for DATA MODUL and CAL-MAINE FOODS
0.22 | Correlation Coefficient |
Modest diversification
The 3 months correlation between DATA and CAL-MAINE is 0.22. Overlapping area represents the amount of risk that can be diversified away by holding DATA MODUL and CAL MAINE FOODS in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CAL MAINE FOODS and DATA MODUL 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 DATA MODUL are associated (or correlated) with CAL-MAINE FOODS. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CAL MAINE FOODS has no effect on the direction of DATA MODUL i.e., DATA MODUL and CAL-MAINE FOODS go up and down completely randomly.
Pair Corralation between DATA MODUL and CAL-MAINE FOODS
Assuming the 90 days trading horizon DATA MODUL is expected to generate 2.79 times less return on investment than CAL-MAINE FOODS. In addition to that, DATA MODUL is 1.47 times more volatile than CAL MAINE FOODS. It trades about 0.07 of its total potential returns per unit of risk. CAL MAINE FOODS is currently generating about 0.27 per unit of volatility. If you would invest 7,700 in CAL MAINE FOODS on October 8, 2024 and sell it today you would earn a total of 2,510 from holding CAL MAINE FOODS or generate 32.6% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
DATA MODUL vs. CAL MAINE FOODS
Performance |
Timeline |
DATA MODUL |
CAL MAINE FOODS |
DATA MODUL and CAL-MAINE FOODS Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with DATA MODUL and CAL-MAINE FOODS
The main advantage of trading using opposite DATA MODUL and CAL-MAINE FOODS positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DATA MODUL position performs unexpectedly, CAL-MAINE FOODS 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 CAL-MAINE FOODS will offset losses from the drop in CAL-MAINE FOODS's long position.DATA MODUL vs. Aedas Homes SA | DATA MODUL vs. ADDUS HOMECARE | DATA MODUL vs. MAG SILVER | DATA MODUL vs. 24SEVENOFFICE GROUP AB |
CAL-MAINE FOODS vs. GigaMedia | CAL-MAINE FOODS vs. RCS MediaGroup SpA | CAL-MAINE FOODS vs. Applied Materials | CAL-MAINE FOODS vs. Goodyear Tire Rubber |
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 File Import module to quickly import all of your third-party portfolios from your local drive in csv format.
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