Correlation Between Birikim Varlik and Federal Mogul
Can any of the company-specific risk be diversified away by investing in both Birikim Varlik and Federal Mogul 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 Birikim Varlik and Federal Mogul into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Birikim Varlik Yonetim and Federal Mogul Izmit, you can compare the effects of market volatilities on Birikim Varlik and Federal Mogul 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 Birikim Varlik with a short position of Federal Mogul. Check out your portfolio center. Please also check ongoing floating volatility patterns of Birikim Varlik and Federal Mogul.
Diversification Opportunities for Birikim Varlik and Federal Mogul
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Birikim and Federal is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Birikim Varlik Yonetim and Federal Mogul Izmit in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Federal Mogul Izmit and Birikim Varlik 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 Birikim Varlik Yonetim are associated (or correlated) with Federal Mogul. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Federal Mogul Izmit has no effect on the direction of Birikim Varlik i.e., Birikim Varlik and Federal Mogul go up and down completely randomly.
Pair Corralation between Birikim Varlik and Federal Mogul
Assuming the 90 days trading horizon Birikim Varlik Yonetim is expected to generate 1.59 times more return on investment than Federal Mogul. However, Birikim Varlik is 1.59 times more volatile than Federal Mogul Izmit. It trades about 0.05 of its potential returns per unit of risk. Federal Mogul Izmit is currently generating about -0.01 per unit of risk. If you would invest 4,810 in Birikim Varlik Yonetim on August 31, 2024 and sell it today you would earn a total of 100.00 from holding Birikim Varlik Yonetim or generate 2.08% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 95.65% |
Values | Daily Returns |
Birikim Varlik Yonetim vs. Federal Mogul Izmit
Performance |
Timeline |
Birikim Varlik Yonetim |
Federal Mogul Izmit |
Birikim Varlik and Federal Mogul Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Birikim Varlik and Federal Mogul
The main advantage of trading using opposite Birikim Varlik and Federal Mogul positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Birikim Varlik position performs unexpectedly, Federal Mogul 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 Federal Mogul will offset losses from the drop in Federal Mogul's long position.Birikim Varlik vs. Verusa Holding AS | Birikim Varlik vs. Guler Yatirim Holding | Birikim Varlik vs. Verusaturk Girisim Sermayesi | Birikim Varlik vs. Yesil Yatirim Holding |
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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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