Correlation Between Global Menkul and Guler Yatirim
Can any of the company-specific risk be diversified away by investing in both Global Menkul and Guler Yatirim 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 Global Menkul and Guler Yatirim into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Global Menkul Degerler and Guler Yatirim Holding, you can compare the effects of market volatilities on Global Menkul and Guler Yatirim 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 Global Menkul with a short position of Guler Yatirim. Check out your portfolio center. Please also check ongoing floating volatility patterns of Global Menkul and Guler Yatirim.
Diversification Opportunities for Global Menkul and Guler Yatirim
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Global and Guler is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Global Menkul Degerler and Guler Yatirim Holding in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Guler Yatirim Holding and Global Menkul 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 Global Menkul Degerler are associated (or correlated) with Guler Yatirim. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Guler Yatirim Holding has no effect on the direction of Global Menkul i.e., Global Menkul and Guler Yatirim go up and down completely randomly.
Pair Corralation between Global Menkul and Guler Yatirim
Assuming the 90 days trading horizon Global Menkul Degerler is expected to generate 1.75 times more return on investment than Guler Yatirim. However, Global Menkul is 1.75 times more volatile than Guler Yatirim Holding. It trades about 0.09 of its potential returns per unit of risk. Guler Yatirim Holding is currently generating about 0.08 per unit of risk. If you would invest 1,058 in Global Menkul Degerler on September 24, 2024 and sell it today you would earn a total of 201.00 from holding Global Menkul Degerler or generate 19.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Global Menkul Degerler vs. Guler Yatirim Holding
Performance |
Timeline |
Global Menkul Degerler |
Guler Yatirim Holding |
Global Menkul and Guler Yatirim Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Global Menkul and Guler Yatirim
The main advantage of trading using opposite Global Menkul and Guler Yatirim positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Global Menkul position performs unexpectedly, Guler Yatirim 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 Guler Yatirim will offset losses from the drop in Guler Yatirim's long position.Global Menkul vs. Koza Anadolu Metal | Global Menkul vs. Silverline Endustri ve | Global Menkul vs. Cuhadaroglu Metal Sanayi | Global Menkul vs. Qnb Finansbank AS |
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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 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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