Correlation Between VersaBank and Bank of Nova Scotia
Can any of the company-specific risk be diversified away by investing in both VersaBank and Bank of Nova Scotia 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 VersaBank and Bank of Nova Scotia into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between VersaBank and Bank of Nova, you can compare the effects of market volatilities on VersaBank and Bank of Nova Scotia 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 VersaBank with a short position of Bank of Nova Scotia. Check out your portfolio center. Please also check ongoing floating volatility patterns of VersaBank and Bank of Nova Scotia.
Diversification Opportunities for VersaBank and Bank of Nova Scotia
0.9 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between VersaBank and Bank is 0.9. Overlapping area represents the amount of risk that can be diversified away by holding VersaBank and Bank of Nova in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bank of Nova Scotia and VersaBank 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 VersaBank are associated (or correlated) with Bank of Nova Scotia. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bank of Nova Scotia has no effect on the direction of VersaBank i.e., VersaBank and Bank of Nova Scotia go up and down completely randomly.
Pair Corralation between VersaBank and Bank of Nova Scotia
Assuming the 90 days trading horizon VersaBank is expected to generate 1.97 times more return on investment than Bank of Nova Scotia. However, VersaBank is 1.97 times more volatile than Bank of Nova. It trades about 0.09 of its potential returns per unit of risk. Bank of Nova is currently generating about 0.07 per unit of risk. If you would invest 998.00 in VersaBank on September 14, 2024 and sell it today you would earn a total of 1,230 from holding VersaBank or generate 123.25% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
VersaBank vs. Bank of Nova
Performance |
Timeline |
VersaBank |
Bank of Nova Scotia |
VersaBank and Bank of Nova Scotia Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with VersaBank and Bank of Nova Scotia
The main advantage of trading using opposite VersaBank and Bank of Nova Scotia positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if VersaBank position performs unexpectedly, Bank of Nova Scotia 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 Bank of Nova Scotia will offset losses from the drop in Bank of Nova Scotia's long position.VersaBank vs. Sylogist | VersaBank vs. Sangoma Technologies Corp | VersaBank vs. Firan Technology Group | VersaBank vs. Hammond Power Solutions |
Bank of Nova Scotia vs. Toronto Dominion Bank | Bank of Nova Scotia vs. Royal Bank of | Bank of Nova Scotia vs. Bank of Montreal | Bank of Nova Scotia vs. Canadian Imperial Bank |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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