Correlation Between INDOFOOD AGRI and PENN Entertainment
Can any of the company-specific risk be diversified away by investing in both INDOFOOD AGRI and PENN Entertainment 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 INDOFOOD AGRI and PENN Entertainment into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between INDOFOOD AGRI RES and PENN Entertainment, you can compare the effects of market volatilities on INDOFOOD AGRI and PENN Entertainment 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 INDOFOOD AGRI with a short position of PENN Entertainment. Check out your portfolio center. Please also check ongoing floating volatility patterns of INDOFOOD AGRI and PENN Entertainment.
Diversification Opportunities for INDOFOOD AGRI and PENN Entertainment
0.35 | Correlation Coefficient |
Weak diversification
The 3 months correlation between INDOFOOD and PENN is 0.35. Overlapping area represents the amount of risk that can be diversified away by holding INDOFOOD AGRI RES and PENN Entertainment in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PENN Entertainment and INDOFOOD AGRI 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 INDOFOOD AGRI RES are associated (or correlated) with PENN Entertainment. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PENN Entertainment has no effect on the direction of INDOFOOD AGRI i.e., INDOFOOD AGRI and PENN Entertainment go up and down completely randomly.
Pair Corralation between INDOFOOD AGRI and PENN Entertainment
Assuming the 90 days trading horizon INDOFOOD AGRI RES is expected to under-perform the PENN Entertainment. But the stock apears to be less risky and, when comparing its historical volatility, INDOFOOD AGRI RES is 2.37 times less risky than PENN Entertainment. The stock trades about -0.08 of its potential returns per unit of risk. The PENN Entertainment is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest 1,896 in PENN Entertainment on October 11, 2024 and sell it today you would lose (86.00) from holding PENN Entertainment or give up 4.54% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
INDOFOOD AGRI RES vs. PENN Entertainment
Performance |
Timeline |
INDOFOOD AGRI RES |
PENN Entertainment |
INDOFOOD AGRI and PENN Entertainment Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with INDOFOOD AGRI and PENN Entertainment
The main advantage of trading using opposite INDOFOOD AGRI and PENN Entertainment positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if INDOFOOD AGRI position performs unexpectedly, PENN Entertainment 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 Entertainment will offset losses from the drop in PENN Entertainment's long position.INDOFOOD AGRI vs. Casio Computer CoLtd | INDOFOOD AGRI vs. APPLIED MATERIALS | INDOFOOD AGRI vs. Eagle Materials | INDOFOOD AGRI vs. GOODYEAR T RUBBER |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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