Correlation Between Airports and Eastern Polymer
Can any of the company-specific risk be diversified away by investing in both Airports and Eastern Polymer 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 Eastern Polymer 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 Eastern Polymer Group, you can compare the effects of market volatilities on Airports and Eastern Polymer 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 Eastern Polymer. Check out your portfolio center. Please also check ongoing floating volatility patterns of Airports and Eastern Polymer.
Diversification Opportunities for Airports and Eastern Polymer
0.5 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Airports and Eastern is 0.5. Overlapping area represents the amount of risk that can be diversified away by holding Airports of Thailand and Eastern Polymer Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Eastern Polymer Group 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 Eastern Polymer. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Eastern Polymer Group has no effect on the direction of Airports i.e., Airports and Eastern Polymer go up and down completely randomly.
Pair Corralation between Airports and Eastern Polymer
Assuming the 90 days trading horizon Airports of Thailand is expected to generate 0.51 times more return on investment than Eastern Polymer. However, Airports of Thailand is 1.95 times less risky than Eastern Polymer. It trades about -0.03 of its potential returns per unit of risk. Eastern Polymer Group is currently generating about -0.08 per unit of risk. If you would invest 6,771 in Airports of Thailand on September 14, 2024 and sell it today you would lose (646.00) from holding Airports of Thailand or give up 9.54% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 99.62% |
| Values | Daily Returns |
Airports of Thailand vs. Eastern Polymer Group
Performance |
| Timeline |
| Airports of Thailand |
| Eastern Polymer Group |
Airports and Eastern Polymer Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Airports and Eastern Polymer
The main advantage of trading using opposite Airports and Eastern Polymer positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Airports position performs unexpectedly, Eastern Polymer 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 Eastern Polymer will offset losses from the drop in Eastern Polymer's long position.| Airports vs. CP ALL Public | Airports vs. PTT Public | Airports vs. Kasikornbank Public | Airports vs. Bangkok Dusit Medical |
| Eastern Polymer vs. Thantawan Industry Public | Eastern Polymer vs. The Erawan Group | Eastern Polymer vs. Jay Mart Public | Eastern Polymer 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 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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