Correlation Between Airports and Ekachai Medical
Can any of the company-specific risk be diversified away by investing in both Airports and Ekachai Medical 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 Airports and Ekachai Medical into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Airports of Thailand and Ekachai Medical Care, you can compare the effects of market volatilities on Airports and Ekachai Medical 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 Airports with a short position of Ekachai Medical. Check out your portfolio center. Please also check ongoing floating volatility patterns of Airports and Ekachai Medical.
Diversification Opportunities for Airports and Ekachai Medical
0.27 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Airports and Ekachai is 0.27. Overlapping area represents the amount of risk that can be diversified away by holding Airports of Thailand and Ekachai Medical Care in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Ekachai Medical Care and Airports 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 Airports of Thailand are associated (or correlated) with Ekachai Medical. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Ekachai Medical Care has no effect on the direction of Airports i.e., Airports and Ekachai Medical go up and down completely randomly.
Pair Corralation between Airports and Ekachai Medical
Assuming the 90 days trading horizon Airports of Thailand is expected to generate 1.35 times more return on investment than Ekachai Medical. However, Airports is 1.35 times more volatile than Ekachai Medical Care. It trades about -0.09 of its potential returns per unit of risk. Ekachai Medical Care is currently generating about -0.2 per unit of risk. If you would invest 6,225 in Airports of Thailand on September 3, 2024 and sell it today you would lose (150.00) from holding Airports of Thailand or give up 2.41% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Airports of Thailand vs. Ekachai Medical Care
Performance |
| Timeline |
| Airports of Thailand |
| Ekachai Medical Care |
Airports and Ekachai Medical Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Airports and Ekachai Medical
The main advantage of trading using opposite Airports and Ekachai Medical positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Airports position performs unexpectedly, Ekachai Medical 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 Ekachai Medical will offset losses from the drop in Ekachai Medical's long position.| Airports vs. CP ALL Public | Airports vs. PTT Public | Airports vs. Kasikornbank Public | Airports vs. Bangkok Dusit Medical |
| Ekachai Medical vs. Bangkok Dusit Medical | Ekachai Medical vs. CP ALL Public | Ekachai Medical vs. Central Pattana Public | Ekachai Medical vs. Airports of Thailand |
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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