Correlation Between Pan Asia and Central Pattana
Can any of the company-specific risk be diversified away by investing in both Pan Asia and Central Pattana 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 Pan Asia and Central Pattana into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Pan Asia Footwear and Central Pattana Public, you can compare the effects of market volatilities on Pan Asia and Central Pattana 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 Pan Asia with a short position of Central Pattana. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pan Asia and Central Pattana.
Diversification Opportunities for Pan Asia and Central Pattana
0.76 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Pan and Central is 0.76. Overlapping area represents the amount of risk that can be diversified away by holding Pan Asia Footwear and Central Pattana Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Central Pattana Public and Pan Asia 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 Pan Asia Footwear are associated (or correlated) with Central Pattana. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Central Pattana Public has no effect on the direction of Pan Asia i.e., Pan Asia and Central Pattana go up and down completely randomly.
Pair Corralation between Pan Asia and Central Pattana
Assuming the 90 days trading horizon Pan Asia Footwear is expected to under-perform the Central Pattana. In addition to that, Pan Asia is 1.61 times more volatile than Central Pattana Public. It trades about -0.06 of its total potential returns per unit of risk. Central Pattana Public is currently generating about -0.06 per unit of volatility. If you would invest 6,025 in Central Pattana Public on September 13, 2024 and sell it today you would lose (75.00) from holding Central Pattana Public or give up 1.24% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Pan Asia Footwear vs. Central Pattana Public
Performance |
| Timeline |
| Pan Asia Footwear |
| Central Pattana Public |
Pan Asia and Central Pattana Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Pan Asia and Central Pattana
The main advantage of trading using opposite Pan Asia and Central Pattana positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pan Asia position performs unexpectedly, Central Pattana 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 Central Pattana will offset losses from the drop in Central Pattana's long position.| Pan Asia vs. Peoples Garment Public | Pan Asia vs. Nawarat Patanakarn Public | Pan Asia vs. KGI Securities Public | Pan Asia vs. Pato Chemical Industry |
| Central Pattana vs. CP ALL Public | Central Pattana vs. Bangkok Dusit Medical | Central Pattana vs. Airports of Thailand | Central Pattana vs. Advanced Info Service |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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