Correlation Between TD Canadian and CIBC Clean
Can any of the company-specific risk be diversified away by investing in both TD Canadian and CIBC Clean 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 TD Canadian and CIBC Clean into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between TD Canadian Long and CIBC Clean Energy, you can compare the effects of market volatilities on TD Canadian and CIBC Clean 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 TD Canadian with a short position of CIBC Clean. Check out your portfolio center. Please also check ongoing floating volatility patterns of TD Canadian and CIBC Clean.
Diversification Opportunities for TD Canadian and CIBC Clean
-0.6 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between TCLB and CIBC is -0.6. Overlapping area represents the amount of risk that can be diversified away by holding TD Canadian Long and CIBC Clean Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CIBC Clean Energy and TD Canadian 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 TD Canadian Long are associated (or correlated) with CIBC Clean. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CIBC Clean Energy has no effect on the direction of TD Canadian i.e., TD Canadian and CIBC Clean go up and down completely randomly.
Pair Corralation between TD Canadian and CIBC Clean
Assuming the 90 days trading horizon TD Canadian Long is expected to under-perform the CIBC Clean. But the etf apears to be less risky and, when comparing its historical volatility, TD Canadian Long is 3.17 times less risky than CIBC Clean. The etf trades about -0.09 of its potential returns per unit of risk. The CIBC Clean Energy is currently generating about 0.25 of returns per unit of risk over similar time horizon. If you would invest 607.00 in CIBC Clean Energy on April 23, 2025 and sell it today you would earn a total of 223.00 from holding CIBC Clean Energy or generate 36.74% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
TD Canadian Long vs. CIBC Clean Energy
Performance |
Timeline |
TD Canadian Long |
CIBC Clean Energy |
TD Canadian and CIBC Clean Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with TD Canadian and CIBC Clean
The main advantage of trading using opposite TD Canadian and CIBC Clean positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if TD Canadian position performs unexpectedly, CIBC Clean 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 CIBC Clean will offset losses from the drop in CIBC Clean's long position.TD Canadian vs. NBI High Yield | TD Canadian vs. NBI Unconstrained Fixed | TD Canadian vs. Mackenzie Developed ex North | TD Canadian vs. BMO Short Term Bond |
CIBC Clean vs. iShares SPTSX 60 | CIBC Clean vs. iShares Core SP | CIBC Clean vs. iShares Core SPTSX | CIBC Clean vs. BMO Aggregate Bond |
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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