Correlation Between Power Dividend and Power Momentum
Can any of the company-specific risk be diversified away by investing in both Power Dividend and Power Momentum 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 Power Dividend and Power Momentum into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Power Dividend Index and Power Momentum Index, you can compare the effects of market volatilities on Power Dividend and Power Momentum 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 Power Dividend with a short position of Power Momentum. Check out your portfolio center. Please also check ongoing floating volatility patterns of Power Dividend and Power Momentum.
Diversification Opportunities for Power Dividend and Power Momentum
0.71 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Power and Power is 0.71. Overlapping area represents the amount of risk that can be diversified away by holding Power Dividend Index and Power Momentum Index in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Power Momentum Index and Power Dividend 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 Power Dividend Index are associated (or correlated) with Power Momentum. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Power Momentum Index has no effect on the direction of Power Dividend i.e., Power Dividend and Power Momentum go up and down completely randomly.
Pair Corralation between Power Dividend and Power Momentum
Assuming the 90 days horizon Power Dividend Index is expected to under-perform the Power Momentum. But the mutual fund apears to be less risky and, when comparing its historical volatility, Power Dividend Index is 1.43 times less risky than Power Momentum. The mutual fund trades about -0.35 of its potential returns per unit of risk. The Power Momentum Index is currently generating about -0.17 of returns per unit of risk over similar time horizon. If you would invest 1,409 in Power Momentum Index on September 22, 2024 and sell it today you would lose (55.00) from holding Power Momentum Index or give up 3.9% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Power Dividend Index vs. Power Momentum Index
Performance |
Timeline |
Power Dividend Index |
Power Momentum Index |
Power Dividend and Power Momentum Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Power Dividend and Power Momentum
The main advantage of trading using opposite Power Dividend and Power Momentum positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Power Dividend position performs unexpectedly, Power Momentum 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 Power Momentum will offset losses from the drop in Power Momentum's long position.Power Dividend vs. Power Income Fund | Power Dividend vs. Power Income Fund | Power Dividend vs. Power Income Fund | Power Dividend vs. Power Momentum Index |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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