Correlation Between Mitsubishi UFJ and Barclays PLC
Can any of the company-specific risk be diversified away by investing in both Mitsubishi UFJ and Barclays PLC 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 Mitsubishi UFJ and Barclays PLC into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mitsubishi UFJ Financial and Barclays PLC, you can compare the effects of market volatilities on Mitsubishi UFJ and Barclays PLC 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 Mitsubishi UFJ with a short position of Barclays PLC. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mitsubishi UFJ and Barclays PLC.
Diversification Opportunities for Mitsubishi UFJ and Barclays PLC
0.71 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Mitsubishi and Barclays is 0.71. Overlapping area represents the amount of risk that can be diversified away by holding Mitsubishi UFJ Financial and Barclays PLC in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Barclays PLC and Mitsubishi UFJ 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 Mitsubishi UFJ Financial are associated (or correlated) with Barclays PLC. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Barclays PLC has no effect on the direction of Mitsubishi UFJ i.e., Mitsubishi UFJ and Barclays PLC go up and down completely randomly.
Pair Corralation between Mitsubishi UFJ and Barclays PLC
Assuming the 90 days horizon Mitsubishi UFJ is expected to generate 1.37 times less return on investment than Barclays PLC. In addition to that, Mitsubishi UFJ is 1.01 times more volatile than Barclays PLC. It trades about 0.08 of its total potential returns per unit of risk. Barclays PLC is currently generating about 0.11 per unit of volatility. If you would invest 290.00 in Barclays PLC on September 7, 2024 and sell it today you would earn a total of 58.00 from holding Barclays PLC or generate 20.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Mitsubishi UFJ Financial vs. Barclays PLC
Performance |
Timeline |
Mitsubishi UFJ Financial |
Barclays PLC |
Mitsubishi UFJ and Barclays PLC Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Mitsubishi UFJ and Barclays PLC
The main advantage of trading using opposite Mitsubishi UFJ and Barclays PLC positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mitsubishi UFJ position performs unexpectedly, Barclays PLC 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 Barclays PLC will offset losses from the drop in Barclays PLC's long position.Mitsubishi UFJ vs. Banco Bilbao Vizcaya | Mitsubishi UFJ vs. ABN AMRO Bank | Mitsubishi UFJ vs. ING Groep NV | Mitsubishi UFJ vs. Banco de Sabadell |
Barclays PLC vs. ABN AMRO Bank | Barclays PLC vs. Bank of America | Barclays PLC vs. Bank of America | Barclays PLC vs. Banco Bilbao Vizcaya |
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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