Correlation Between QNB Finans and Prizma Pres
Can any of the company-specific risk be diversified away by investing in both QNB Finans and Prizma Pres 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 QNB Finans and Prizma Pres into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between QNB Finans Finansal and Prizma Pres Matbaacilik, you can compare the effects of market volatilities on QNB Finans and Prizma Pres 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 QNB Finans with a short position of Prizma Pres. Check out your portfolio center. Please also check ongoing floating volatility patterns of QNB Finans and Prizma Pres.
Diversification Opportunities for QNB Finans and Prizma Pres
0.24 | Correlation Coefficient |
Modest diversification
The 3 months correlation between QNB and Prizma is 0.24. Overlapping area represents the amount of risk that can be diversified away by holding QNB Finans Finansal and Prizma Pres Matbaacilik in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Prizma Pres Matbaacilik and QNB Finans 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 QNB Finans Finansal are associated (or correlated) with Prizma Pres. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Prizma Pres Matbaacilik has no effect on the direction of QNB Finans i.e., QNB Finans and Prizma Pres go up and down completely randomly.
Pair Corralation between QNB Finans and Prizma Pres
Assuming the 90 days trading horizon QNB Finans Finansal is expected to generate 0.77 times more return on investment than Prizma Pres. However, QNB Finans Finansal is 1.3 times less risky than Prizma Pres. It trades about -0.07 of its potential returns per unit of risk. Prizma Pres Matbaacilik is currently generating about -0.15 per unit of risk. If you would invest 9,770 in QNB Finans Finansal on August 30, 2024 and sell it today you would lose (490.00) from holding QNB Finans Finansal or give up 5.02% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
QNB Finans Finansal vs. Prizma Pres Matbaacilik
Performance |
| Timeline |
| QNB Finans Finansal |
| Prizma Pres Matbaacilik |
QNB Finans and Prizma Pres Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with QNB Finans and Prizma Pres
The main advantage of trading using opposite QNB Finans and Prizma Pres positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if QNB Finans position performs unexpectedly, Prizma Pres 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 Prizma Pres will offset losses from the drop in Prizma Pres' long position.| QNB Finans vs. Bms Birlesik Metal | QNB Finans vs. Mackolik Internet Hizmetleri | QNB Finans vs. Akcansa Cimento Sanayi | QNB Finans vs. Politeknik Metal Sanayi |
| Prizma Pres vs. Haci Omer Sabanci | Prizma Pres vs. Turkiye Petrol Rafinerileri | Prizma Pres vs. Turkiye Garanti Bankasi | Prizma Pres vs. Akbank TAS |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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