Correlation Between Thai Coating and Teka Construction
Can any of the company-specific risk be diversified away by investing in both Thai Coating and Teka Construction 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 Thai Coating and Teka Construction into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Thai Coating Industrial and Teka Construction PCL, you can compare the effects of market volatilities on Thai Coating and Teka Construction 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 Thai Coating with a short position of Teka Construction. Check out your portfolio center. Please also check ongoing floating volatility patterns of Thai Coating and Teka Construction.
Diversification Opportunities for Thai Coating and Teka Construction
-0.1 | Correlation Coefficient |
Good diversification
The 3 months correlation between Thai and Teka is -0.1. Overlapping area represents the amount of risk that can be diversified away by holding Thai Coating Industrial and Teka Construction PCL in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Teka Construction PCL and Thai Coating 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 Thai Coating Industrial are associated (or correlated) with Teka Construction. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Teka Construction PCL has no effect on the direction of Thai Coating i.e., Thai Coating and Teka Construction go up and down completely randomly.
Pair Corralation between Thai Coating and Teka Construction
Assuming the 90 days trading horizon Thai Coating Industrial is expected to generate 2.88 times more return on investment than Teka Construction. However, Thai Coating is 2.88 times more volatile than Teka Construction PCL. It trades about 0.03 of its potential returns per unit of risk. Teka Construction PCL is currently generating about -0.05 per unit of risk. If you would invest 2,500 in Thai Coating Industrial on September 14, 2024 and sell it today you would earn a total of 50.00 from holding Thai Coating Industrial or generate 2.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 98.33% |
Values | Daily Returns |
Thai Coating Industrial vs. Teka Construction PCL
Performance |
Timeline |
Thai Coating Industrial |
Teka Construction PCL |
Thai Coating and Teka Construction Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Thai Coating and Teka Construction
The main advantage of trading using opposite Thai Coating and Teka Construction positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Thai Coating position performs unexpectedly, Teka Construction 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 Teka Construction will offset losses from the drop in Teka Construction's long position.Thai Coating vs. Thantawan Industry Public | Thai Coating vs. The Erawan Group | Thai Coating vs. Jay Mart Public | Thai Coating vs. Airports of Thailand |
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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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