Correlation Between Amazon and Telecom Argentina
Can any of the company-specific risk be diversified away by investing in both Amazon and Telecom Argentina 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 Amazon and Telecom Argentina into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Amazon Inc and Telecom Argentina, you can compare the effects of market volatilities on Amazon and Telecom Argentina 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 Amazon with a short position of Telecom Argentina. Check out your portfolio center. Please also check ongoing floating volatility patterns of Amazon and Telecom Argentina.
Diversification Opportunities for Amazon and Telecom Argentina
0.31 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Amazon and Telecom is 0.31. Overlapping area represents the amount of risk that can be diversified away by holding Amazon Inc and Telecom Argentina in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Telecom Argentina and Amazon 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 Amazon Inc are associated (or correlated) with Telecom Argentina. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Telecom Argentina has no effect on the direction of Amazon i.e., Amazon and Telecom Argentina go up and down completely randomly.
Pair Corralation between Amazon and Telecom Argentina
Assuming the 90 days trading horizon Amazon is expected to generate 30.64 times less return on investment than Telecom Argentina. But when comparing it to its historical volatility, Amazon Inc is 2.19 times less risky than Telecom Argentina. It trades about 0.02 of its potential returns per unit of risk. Telecom Argentina is currently generating about 0.34 of returns per unit of risk over similar time horizon. If you would invest 224,500 in Telecom Argentina on September 4, 2024 and sell it today you would earn a total of 76,500 from holding Telecom Argentina or generate 34.08% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Amazon Inc vs. Telecom Argentina
Performance |
| Timeline |
| Amazon Inc |
| Telecom Argentina |
Amazon and Telecom Argentina Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Amazon and Telecom Argentina
The main advantage of trading using opposite Amazon and Telecom Argentina positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Amazon position performs unexpectedly, Telecom Argentina 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 Telecom Argentina will offset losses from the drop in Telecom Argentina's long position.| Amazon vs. United States Steel | Amazon vs. Compania de Transporte | Amazon vs. Telecom Argentina | Amazon vs. Transportadora de Gas |
| Telecom Argentina vs. Transportadora de Gas | Telecom Argentina vs. Compania de Transporte | Telecom Argentina vs. Harmony Gold Mining | Telecom Argentina vs. Agrometal SAI |
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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