Correlation Between E Data and Atlas Menkul
Can any of the company-specific risk be diversified away by investing in both E Data and Atlas Menkul 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 E Data and Atlas Menkul into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between E Data Teknoloji Pazarlama and Atlas Menkul Kiymetler, you can compare the effects of market volatilities on E Data and Atlas Menkul 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 E Data with a short position of Atlas Menkul. Check out your portfolio center. Please also check ongoing floating volatility patterns of E Data and Atlas Menkul.
Diversification Opportunities for E Data and Atlas Menkul
0.04 | Correlation Coefficient |
Significant diversification
The 3 months correlation between EDATA and Atlas is 0.04. Overlapping area represents the amount of risk that can be diversified away by holding E Data Teknoloji Pazarlama and Atlas Menkul Kiymetler in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Atlas Menkul Kiymetler and E Data 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 E Data Teknoloji Pazarlama are associated (or correlated) with Atlas Menkul. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Atlas Menkul Kiymetler has no effect on the direction of E Data i.e., E Data and Atlas Menkul go up and down completely randomly.
Pair Corralation between E Data and Atlas Menkul
Assuming the 90 days trading horizon E Data is expected to generate 1.05 times less return on investment than Atlas Menkul. In addition to that, E Data is 1.09 times more volatile than Atlas Menkul Kiymetler. It trades about 0.06 of its total potential returns per unit of risk. Atlas Menkul Kiymetler is currently generating about 0.07 per unit of volatility. If you would invest 285.00 in Atlas Menkul Kiymetler on September 2, 2024 and sell it today you would earn a total of 304.00 from holding Atlas Menkul Kiymetler or generate 106.67% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
E Data Teknoloji Pazarlama vs. Atlas Menkul Kiymetler
Performance |
| Timeline |
| E Data Teknoloji |
| Atlas Menkul Kiymetler |
E Data and Atlas Menkul Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with E Data and Atlas Menkul
The main advantage of trading using opposite E Data and Atlas Menkul positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if E Data position performs unexpectedly, Atlas Menkul 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 Atlas Menkul will offset losses from the drop in Atlas Menkul's long position.| E Data vs. MEGA METAL | E Data vs. Koza Anadolu Metal | E Data vs. Turkiye Kalkinma Bankasi | E Data vs. Akcansa Cimento Sanayi |
| Atlas Menkul vs. Borlease Otomotiv AS | Atlas Menkul vs. MEGA METAL | Atlas Menkul vs. Politeknik Metal Sanayi | Atlas Menkul vs. Galatasaray Sportif Sinai |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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