Correlation Between AMCON Distributing and MCF Energy
Can any of the company-specific risk be diversified away by investing in both AMCON Distributing and MCF Energy 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 AMCON Distributing and MCF Energy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between AMCON Distributing and MCF Energy, you can compare the effects of market volatilities on AMCON Distributing and MCF Energy 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 AMCON Distributing with a short position of MCF Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of AMCON Distributing and MCF Energy.
Diversification Opportunities for AMCON Distributing and MCF Energy
0.12 | Correlation Coefficient |
Average diversification
The 3 months correlation between AMCON and MCF is 0.12. Overlapping area represents the amount of risk that can be diversified away by holding AMCON Distributing and MCF Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on MCF Energy and AMCON Distributing 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 AMCON Distributing are associated (or correlated) with MCF Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of MCF Energy has no effect on the direction of AMCON Distributing i.e., AMCON Distributing and MCF Energy go up and down completely randomly.
Pair Corralation between AMCON Distributing and MCF Energy
Considering the 90-day investment horizon AMCON Distributing is expected to generate 0.54 times more return on investment than MCF Energy. However, AMCON Distributing is 1.85 times less risky than MCF Energy. It trades about 0.0 of its potential returns per unit of risk. MCF Energy is currently generating about -0.04 per unit of risk. If you would invest 17,557 in AMCON Distributing on September 13, 2024 and sell it today you would lose (3,944) from holding AMCON Distributing or give up 22.46% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 98.78% |
Values | Daily Returns |
AMCON Distributing vs. MCF Energy
Performance |
Timeline |
AMCON Distributing |
MCF Energy |
AMCON Distributing and MCF Energy Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with AMCON Distributing and MCF Energy
The main advantage of trading using opposite AMCON Distributing and MCF Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if AMCON Distributing position performs unexpectedly, MCF Energy 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 MCF Energy will offset losses from the drop in MCF Energy's long position.AMCON Distributing vs. Steven Madden | AMCON Distributing vs. Vera Bradley | AMCON Distributing vs. Caleres | AMCON Distributing vs. Wolverine World Wide |
MCF Energy vs. The Joint Corp | MCF Energy vs. NH Foods Ltd | MCF Energy vs. SunOpta | MCF Energy vs. AMCON Distributing |
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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