Correlation Between Power Financial and Diamond Estates
Can any of the company-specific risk be diversified away by investing in both Power Financial and Diamond Estates 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 Financial and Diamond Estates into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Power Financial Corp and Diamond Estates Wines, you can compare the effects of market volatilities on Power Financial and Diamond Estates 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 Financial with a short position of Diamond Estates. Check out your portfolio center. Please also check ongoing floating volatility patterns of Power Financial and Diamond Estates.
Diversification Opportunities for Power Financial and Diamond Estates
-0.45 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Power and Diamond is -0.45. Overlapping area represents the amount of risk that can be diversified away by holding Power Financial Corp and Diamond Estates Wines in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Diamond Estates Wines and Power Financial 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 Financial Corp are associated (or correlated) with Diamond Estates. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Diamond Estates Wines has no effect on the direction of Power Financial i.e., Power Financial and Diamond Estates go up and down completely randomly.
Pair Corralation between Power Financial and Diamond Estates
Assuming the 90 days trading horizon Power Financial Corp is expected to generate 0.5 times more return on investment than Diamond Estates. However, Power Financial Corp is 2.0 times less risky than Diamond Estates. It trades about 0.19 of its potential returns per unit of risk. Diamond Estates Wines is currently generating about -0.06 per unit of risk. If you would invest 1,621 in Power Financial Corp on October 24, 2024 and sell it today you would earn a total of 103.00 from holding Power Financial Corp or generate 6.35% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 95.0% |
Values | Daily Returns |
Power Financial Corp vs. Diamond Estates Wines
Performance |
Timeline |
Power Financial Corp |
Diamond Estates Wines |
Power Financial and Diamond Estates Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Power Financial and Diamond Estates
The main advantage of trading using opposite Power Financial and Diamond Estates positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Power Financial position performs unexpectedly, Diamond Estates 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 Diamond Estates will offset losses from the drop in Diamond Estates' long position.Power Financial vs. NeXGold Mining Corp | Power Financial vs. CVS HEALTH CDR | Power Financial vs. UnitedHealth Group CDR | Power Financial vs. Thunderbird Entertainment Group |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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