Correlation Between DISTRICT METALS and Penn National
Can any of the company-specific risk be diversified away by investing in both DISTRICT METALS and Penn National 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 DISTRICT METALS and Penn National into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DISTRICT METALS and Penn National Gaming, you can compare the effects of market volatilities on DISTRICT METALS and Penn National 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 DISTRICT METALS with a short position of Penn National. Check out your portfolio center. Please also check ongoing floating volatility patterns of DISTRICT METALS and Penn National.
Diversification Opportunities for DISTRICT METALS and Penn National
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between DISTRICT and Penn is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding DISTRICT METALS and Penn National Gaming in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Penn National Gaming and DISTRICT METALS 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 DISTRICT METALS are associated (or correlated) with Penn National. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Penn National Gaming has no effect on the direction of DISTRICT METALS i.e., DISTRICT METALS and Penn National go up and down completely randomly.
Pair Corralation between DISTRICT METALS and Penn National
Assuming the 90 days trading horizon DISTRICT METALS is expected to under-perform the Penn National. In addition to that, DISTRICT METALS is 1.21 times more volatile than Penn National Gaming. It trades about -0.05 of its total potential returns per unit of risk. Penn National Gaming is currently generating about -0.04 per unit of volatility. If you would invest 1,775 in Penn National Gaming on December 28, 2024 and sell it today you would lose (175.00) from holding Penn National Gaming or give up 9.86% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
DISTRICT METALS vs. Penn National Gaming
Performance |
Timeline |
DISTRICT METALS |
Penn National Gaming |
DISTRICT METALS and Penn National Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with DISTRICT METALS and Penn National
The main advantage of trading using opposite DISTRICT METALS and Penn National positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DISTRICT METALS position performs unexpectedly, Penn National 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 Penn National will offset losses from the drop in Penn National's long position.DISTRICT METALS vs. CompuGroup Medical SE | DISTRICT METALS vs. Medical Properties Trust | DISTRICT METALS vs. G8 EDUCATION | DISTRICT METALS vs. CAREER EDUCATION |
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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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