Correlation Between Banco Santander and MARKET VECTR
Can any of the company-specific risk be diversified away by investing in both Banco Santander and MARKET VECTR 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 MARKET VECTR 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 MARKET VECTR RETAIL, you can compare the effects of market volatilities on Banco Santander and MARKET VECTR 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 MARKET VECTR. Check out your portfolio center. Please also check ongoing floating volatility patterns of Banco Santander and MARKET VECTR.
Diversification Opportunities for Banco Santander and MARKET VECTR
0.43 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Banco and MARKET is 0.43. Overlapping area represents the amount of risk that can be diversified away by holding Banco Santander SA and MARKET VECTR RETAIL in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on MARKET VECTR RETAIL 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 MARKET VECTR. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of MARKET VECTR RETAIL has no effect on the direction of Banco Santander i.e., Banco Santander and MARKET VECTR go up and down completely randomly.
Pair Corralation between Banco Santander and MARKET VECTR
Assuming the 90 days trading horizon Banco Santander SA is expected to generate 2.06 times more return on investment than MARKET VECTR. However, Banco Santander is 2.06 times more volatile than MARKET VECTR RETAIL. It trades about 0.07 of its potential returns per unit of risk. MARKET VECTR RETAIL is currently generating about 0.08 per unit of risk. If you would invest 255.00 in Banco Santander SA on September 3, 2024 and sell it today you would earn a total of 183.00 from holding Banco Santander SA or generate 71.76% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 99.41% |
| Values | Daily Returns |
Banco Santander SA vs. MARKET VECTR RETAIL
Performance |
| Timeline |
| Banco Santander SA |
| MARKET VECTR RETAIL |
Banco Santander and MARKET VECTR Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Banco Santander and MARKET VECTR
The main advantage of trading using opposite Banco Santander and MARKET VECTR positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Banco Santander position performs unexpectedly, MARKET VECTR 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 MARKET VECTR will offset losses from the drop in MARKET VECTR's long position.| Banco Santander vs. TOTAL GABON | Banco Santander vs. Walgreens Boots Alliance | Banco Santander vs. Peak Resources Limited |
| MARKET VECTR vs. TOTAL GABON | MARKET VECTR vs. Walgreens Boots Alliance | MARKET VECTR vs. Peak Resources Limited |
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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