Correlation Between Marti Gayrimenkul and Dow Jones
Can any of the company-specific risk be diversified away by investing in both Marti Gayrimenkul and Dow Jones 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 Marti Gayrimenkul and Dow Jones into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Marti Gayrimenkul Yatirim and Dow Jones Industrial, you can compare the effects of market volatilities on Marti Gayrimenkul and Dow Jones 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 Marti Gayrimenkul with a short position of Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of Marti Gayrimenkul and Dow Jones.
Diversification Opportunities for Marti Gayrimenkul and Dow Jones
0.49 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Marti and Dow is 0.49. Overlapping area represents the amount of risk that can be diversified away by holding Marti Gayrimenkul Yatirim and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones Industrial and Marti Gayrimenkul 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 Marti Gayrimenkul Yatirim are associated (or correlated) with Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of Marti Gayrimenkul i.e., Marti Gayrimenkul and Dow Jones go up and down completely randomly.
Pair Corralation between Marti Gayrimenkul and Dow Jones
Assuming the 90 days trading horizon Marti Gayrimenkul Yatirim is expected to generate 3.67 times more return on investment than Dow Jones. However, Marti Gayrimenkul is 3.67 times more volatile than Dow Jones Industrial. It trades about -0.02 of its potential returns per unit of risk. Dow Jones Industrial is currently generating about -0.22 per unit of risk. If you would invest 183.00 in Marti Gayrimenkul Yatirim on October 11, 2024 and sell it today you would lose (4.00) from holding Marti Gayrimenkul Yatirim or give up 2.19% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Marti Gayrimenkul Yatirim vs. Dow Jones Industrial
Performance |
Timeline |
Marti Gayrimenkul and Dow Jones Volatility Contrast
Predicted Return Density |
Returns |
Marti Gayrimenkul Yatirim
Pair trading matchups for Marti Gayrimenkul
Dow Jones Industrial
Pair trading matchups for Dow Jones
Pair Trading with Marti Gayrimenkul and Dow Jones
The main advantage of trading using opposite Marti Gayrimenkul and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Marti Gayrimenkul position performs unexpectedly, Dow Jones 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 Dow Jones will offset losses from the drop in Dow Jones' long position.Marti Gayrimenkul vs. Akcansa Cimento Sanayi | Marti Gayrimenkul vs. Turkiye Kalkinma Bankasi | Marti Gayrimenkul vs. KOC METALURJI | Marti Gayrimenkul vs. Koza Anadolu Metal |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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