Correlation Between Banco Santander and Powszechna Kasa
Can any of the company-specific risk be diversified away by investing in both Banco Santander and Powszechna Kasa 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 Banco Santander and Powszechna Kasa into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Banco Santander SA and Powszechna Kasa Oszczednosci, you can compare the effects of market volatilities on Banco Santander and Powszechna Kasa 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 Banco Santander with a short position of Powszechna Kasa. Check out your portfolio center. Please also check ongoing floating volatility patterns of Banco Santander and Powszechna Kasa.
Diversification Opportunities for Banco Santander and Powszechna Kasa
0.29 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Banco and Powszechna is 0.29. Overlapping area represents the amount of risk that can be diversified away by holding Banco Santander SA and Powszechna Kasa Oszczednosci in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Powszechna Kasa Oszc and Banco Santander 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 Banco Santander SA are associated (or correlated) with Powszechna Kasa. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Powszechna Kasa Oszc has no effect on the direction of Banco Santander i.e., Banco Santander and Powszechna Kasa go up and down completely randomly.
Pair Corralation between Banco Santander and Powszechna Kasa
Assuming the 90 days trading horizon Banco Santander SA is expected to under-perform the Powszechna Kasa. In addition to that, Banco Santander is 1.31 times more volatile than Powszechna Kasa Oszczednosci. It trades about -0.02 of its total potential returns per unit of risk. Powszechna Kasa Oszczednosci is currently generating about 0.17 per unit of volatility. If you would invest 5,784 in Powszechna Kasa Oszczednosci on October 2, 2024 and sell it today you would earn a total of 196.00 from holding Powszechna Kasa Oszczednosci or generate 3.39% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Banco Santander SA vs. Powszechna Kasa Oszczednosci
Performance |
Timeline |
Banco Santander SA |
Powszechna Kasa Oszc |
Banco Santander and Powszechna Kasa Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Banco Santander and Powszechna Kasa
The main advantage of trading using opposite Banco Santander and Powszechna Kasa positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Banco Santander position performs unexpectedly, Powszechna Kasa 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 Powszechna Kasa will offset losses from the drop in Powszechna Kasa's long position.Banco Santander vs. UniCredit SpA | Banco Santander vs. LSI Software SA | Banco Santander vs. Medicalg | Banco Santander vs. ING Bank lski |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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