Correlation Between AMCON Distributing and Goldman Sachs
Can any of the company-specific risk be diversified away by investing in both AMCON Distributing and Goldman Sachs 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 Goldman Sachs into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between AMCON Distributing and Goldman Sachs Capital, you can compare the effects of market volatilities on AMCON Distributing and Goldman Sachs 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 Goldman Sachs. Check out your portfolio center. Please also check ongoing floating volatility patterns of AMCON Distributing and Goldman Sachs.
Diversification Opportunities for AMCON Distributing and Goldman Sachs
0.13 | Correlation Coefficient |
Average diversification
The 3 months correlation between AMCON and Goldman is 0.13. Overlapping area represents the amount of risk that can be diversified away by holding AMCON Distributing and Goldman Sachs Capital in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Goldman Sachs Capital 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 Goldman Sachs. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Goldman Sachs Capital has no effect on the direction of AMCON Distributing i.e., AMCON Distributing and Goldman Sachs go up and down completely randomly.
Pair Corralation between AMCON Distributing and Goldman Sachs
Considering the 90-day investment horizon AMCON Distributing is expected to under-perform the Goldman Sachs. In addition to that, AMCON Distributing is 2.12 times more volatile than Goldman Sachs Capital. It trades about -0.16 of its total potential returns per unit of risk. Goldman Sachs Capital is currently generating about 0.03 per unit of volatility. If you would invest 2,646 in Goldman Sachs Capital on October 8, 2024 and sell it today you would earn a total of 12.00 from holding Goldman Sachs Capital or generate 0.45% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
AMCON Distributing vs. Goldman Sachs Capital
Performance |
Timeline |
AMCON Distributing |
Goldman Sachs Capital |
AMCON Distributing and Goldman Sachs Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with AMCON Distributing and Goldman Sachs
The main advantage of trading using opposite AMCON Distributing and Goldman Sachs positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if AMCON Distributing position performs unexpectedly, Goldman Sachs 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 Goldman Sachs will offset losses from the drop in Goldman Sachs' long position.AMCON Distributing vs. The Chefs Warehouse | AMCON Distributing vs. G Willi Food International | AMCON Distributing vs. SpartanNash Co | AMCON Distributing vs. Calavo Growers |
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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 Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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