Correlation Between Penn National and Food Life
Can any of the company-specific risk be diversified away by investing in both Penn National and Food Life 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 Penn National and Food Life into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Penn National Gaming and Food Life Companies, you can compare the effects of market volatilities on Penn National and Food Life 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 Penn National with a short position of Food Life. Check out your portfolio center. Please also check ongoing floating volatility patterns of Penn National and Food Life.
Diversification Opportunities for Penn National and Food Life
0.72 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Penn and Food is 0.72. Overlapping area represents the amount of risk that can be diversified away by holding Penn National Gaming and Food Life Companies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Food Life Companies and Penn National 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 Penn National Gaming are associated (or correlated) with Food Life. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Food Life Companies has no effect on the direction of Penn National i.e., Penn National and Food Life go up and down completely randomly.
Pair Corralation between Penn National and Food Life
Assuming the 90 days horizon Penn National Gaming is expected to under-perform the Food Life. In addition to that, Penn National is 1.78 times more volatile than Food Life Companies. It trades about -0.05 of its total potential returns per unit of risk. Food Life Companies is currently generating about 0.31 per unit of volatility. If you would invest 1,870 in Food Life Companies on September 21, 2024 and sell it today you would earn a total of 230.00 from holding Food Life Companies or generate 12.3% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Penn National Gaming vs. Food Life Companies
Performance |
Timeline |
Penn National Gaming |
Food Life Companies |
Penn National and Food Life Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Penn National and Food Life
The main advantage of trading using opposite Penn National and Food Life positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Penn National position performs unexpectedly, Food Life 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 Food Life will offset losses from the drop in Food Life's long position.Penn National vs. AUST AGRICULTURAL | Penn National vs. ELMOS SEMICONDUCTOR | Penn National vs. AGRICULTBK HADR25 YC | Penn National vs. Nordic Semiconductor ASA |
Food Life vs. Penn National Gaming | Food Life vs. GigaMedia | Food Life vs. Aegean Airlines SA | Food Life vs. PENN NATL GAMING |
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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