Correlation Between Metall Zug and Banque Cantonale
Can any of the company-specific risk be diversified away by investing in both Metall Zug and Banque Cantonale 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 Metall Zug and Banque Cantonale into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Metall Zug AG and Banque Cantonale du, you can compare the effects of market volatilities on Metall Zug and Banque Cantonale 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 Metall Zug with a short position of Banque Cantonale. Check out your portfolio center. Please also check ongoing floating volatility patterns of Metall Zug and Banque Cantonale.
Diversification Opportunities for Metall Zug and Banque Cantonale
0.76 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Metall and Banque is 0.76. Overlapping area represents the amount of risk that can be diversified away by holding Metall Zug AG and Banque Cantonale du in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Banque Cantonale and Metall Zug 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 Metall Zug AG are associated (or correlated) with Banque Cantonale. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Banque Cantonale has no effect on the direction of Metall Zug i.e., Metall Zug and Banque Cantonale go up and down completely randomly.
Pair Corralation between Metall Zug and Banque Cantonale
Assuming the 90 days trading horizon Metall Zug AG is expected to under-perform the Banque Cantonale. In addition to that, Metall Zug is 4.49 times more volatile than Banque Cantonale du. It trades about -0.16 of its total potential returns per unit of risk. Banque Cantonale du is currently generating about -0.19 per unit of volatility. If you would invest 11,300 in Banque Cantonale du on August 31, 2024 and sell it today you would lose (200.00) from holding Banque Cantonale du or give up 1.77% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 95.65% |
| Values | Daily Returns |
Metall Zug AG vs. Banque Cantonale du
Performance |
| Timeline |
| Metall Zug AG |
| Banque Cantonale |
Metall Zug and Banque Cantonale Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Metall Zug and Banque Cantonale
The main advantage of trading using opposite Metall Zug and Banque Cantonale positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Metall Zug position performs unexpectedly, Banque Cantonale 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 Banque Cantonale will offset losses from the drop in Banque Cantonale's long position.| Metall Zug vs. Bucher Industries AG | Metall Zug vs. Burckhardt Compression | Metall Zug vs. Also Holding AG | Metall Zug vs. Emmi AG |
| Banque Cantonale vs. BB Biotech AG | Banque Cantonale vs. St Galler Kantonalbank | Banque Cantonale vs. Metall Zug AG | Banque Cantonale vs. mobilezone ag |
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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