Correlation Between BMO Aggregate and Solaris Resources
Can any of the company-specific risk be diversified away by investing in both BMO Aggregate and Solaris Resources 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 BMO Aggregate and Solaris Resources into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between BMO Aggregate Bond and Solaris Resources, you can compare the effects of market volatilities on BMO Aggregate and Solaris Resources 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 BMO Aggregate with a short position of Solaris Resources. Check out your portfolio center. Please also check ongoing floating volatility patterns of BMO Aggregate and Solaris Resources.
Diversification Opportunities for BMO Aggregate and Solaris Resources
-0.3 | Correlation Coefficient |
Very good diversification
The 3 months correlation between BMO and Solaris is -0.3. Overlapping area represents the amount of risk that can be diversified away by holding BMO Aggregate Bond and Solaris Resources in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Solaris Resources and BMO Aggregate 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 BMO Aggregate Bond are associated (or correlated) with Solaris Resources. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Solaris Resources has no effect on the direction of BMO Aggregate i.e., BMO Aggregate and Solaris Resources go up and down completely randomly.
Pair Corralation between BMO Aggregate and Solaris Resources
Assuming the 90 days trading horizon BMO Aggregate Bond is expected to under-perform the Solaris Resources. But the etf apears to be less risky and, when comparing its historical volatility, BMO Aggregate Bond is 10.5 times less risky than Solaris Resources. The etf trades about -0.38 of its potential returns per unit of risk. The Solaris Resources is currently generating about 0.18 of returns per unit of risk over similar time horizon. If you would invest 436.00 in Solaris Resources on October 5, 2024 and sell it today you would earn a total of 42.00 from holding Solaris Resources or generate 9.63% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 95.0% |
Values | Daily Returns |
BMO Aggregate Bond vs. Solaris Resources
Performance |
Timeline |
BMO Aggregate Bond |
Solaris Resources |
BMO Aggregate and Solaris Resources Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with BMO Aggregate and Solaris Resources
The main advantage of trading using opposite BMO Aggregate and Solaris Resources positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if BMO Aggregate position performs unexpectedly, Solaris Resources 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 Solaris Resources will offset losses from the drop in Solaris Resources' long position.BMO Aggregate vs. BMO Short Term Bond | BMO Aggregate vs. BMO Canadian Bank | BMO Aggregate vs. BMO Aggregate Bond | BMO Aggregate vs. BMO MSCI EAFE |
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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 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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