Correlation Between Levinski Ofer and Clal Industries
Can any of the company-specific risk be diversified away by investing in both Levinski Ofer and Clal Industries 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 Levinski Ofer and Clal Industries into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Levinski Ofer and Clal Industries and, you can compare the effects of market volatilities on Levinski Ofer and Clal Industries 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 Levinski Ofer with a short position of Clal Industries. Check out your portfolio center. Please also check ongoing floating volatility patterns of Levinski Ofer and Clal Industries.
Diversification Opportunities for Levinski Ofer and Clal Industries
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Levinski and Clal is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Levinski Ofer and Clal Industries and in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Clal Industries and Levinski Ofer 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 Levinski Ofer are associated (or correlated) with Clal Industries. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Clal Industries has no effect on the direction of Levinski Ofer i.e., Levinski Ofer and Clal Industries go up and down completely randomly.
Pair Corralation between Levinski Ofer and Clal Industries
Assuming the 90 days trading horizon Levinski Ofer is expected to generate 1.44 times more return on investment than Clal Industries. However, Levinski Ofer is 1.44 times more volatile than Clal Industries and. It trades about 0.23 of its potential returns per unit of risk. Clal Industries and is currently generating about 0.2 per unit of risk. If you would invest 22,580 in Levinski Ofer on September 5, 2024 and sell it today you would earn a total of 9,420 from holding Levinski Ofer or generate 41.72% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Levinski Ofer vs. Clal Industries and
Performance |
Timeline |
Levinski Ofer |
Clal Industries |
Levinski Ofer and Clal Industries Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Levinski Ofer and Clal Industries
The main advantage of trading using opposite Levinski Ofer and Clal Industries positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Levinski Ofer position performs unexpectedly, Clal Industries 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 Clal Industries will offset losses from the drop in Clal Industries' long position.Levinski Ofer vs. Augwind Energy Tech | Levinski Ofer vs. Enlight Renewable Energy | Levinski Ofer vs. Maytronics | Levinski Ofer vs. Fattal 1998 Holdings |
Clal Industries vs. Levinski Ofer | Clal Industries vs. Shufersal | Clal Industries vs. Tower Semiconductor | Clal Industries vs. Gan Shmuel |
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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