Correlation Between Foodlink and Lampsa Hellenic
Can any of the company-specific risk be diversified away by investing in both Foodlink and Lampsa Hellenic 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 Foodlink and Lampsa Hellenic into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Foodlink AE and Lampsa Hellenic Hotels, you can compare the effects of market volatilities on Foodlink and Lampsa Hellenic 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 Foodlink with a short position of Lampsa Hellenic. Check out your portfolio center. Please also check ongoing floating volatility patterns of Foodlink and Lampsa Hellenic.
Diversification Opportunities for Foodlink and Lampsa Hellenic
0.39 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Foodlink and Lampsa is 0.39. Overlapping area represents the amount of risk that can be diversified away by holding Foodlink AE and Lampsa Hellenic Hotels in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Lampsa Hellenic Hotels and Foodlink 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 Foodlink AE are associated (or correlated) with Lampsa Hellenic. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Lampsa Hellenic Hotels has no effect on the direction of Foodlink i.e., Foodlink and Lampsa Hellenic go up and down completely randomly.
Pair Corralation between Foodlink and Lampsa Hellenic
Assuming the 90 days trading horizon Foodlink AE is expected to generate 9.58 times more return on investment than Lampsa Hellenic. However, Foodlink is 9.58 times more volatile than Lampsa Hellenic Hotels. It trades about 0.16 of its potential returns per unit of risk. Lampsa Hellenic Hotels is currently generating about 0.18 per unit of risk. If you would invest 33.00 in Foodlink AE on August 26, 2024 and sell it today you would earn a total of 3.00 from holding Foodlink AE or generate 9.09% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Foodlink AE vs. Lampsa Hellenic Hotels
Performance |
| Timeline |
| Foodlink AE |
| Lampsa Hellenic Hotels |
Foodlink and Lampsa Hellenic Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Foodlink and Lampsa Hellenic
The main advantage of trading using opposite Foodlink and Lampsa Hellenic positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Foodlink position performs unexpectedly, Lampsa Hellenic 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 Lampsa Hellenic will offset losses from the drop in Lampsa Hellenic's long position.| Foodlink vs. Frigoglass SAIC | Foodlink vs. Autohellas SA | Foodlink vs. Public Power | Foodlink vs. Intralot SA Integrated |
| Lampsa Hellenic vs. National Bank of | Lampsa Hellenic vs. N Leventeris SA | Lampsa Hellenic vs. Eurobank Ergasias Services | Lampsa Hellenic vs. Vogiatzoglou Systems SA |
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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