Correlation Between Cpt Drives and Thai Energy
Can any of the company-specific risk be diversified away by investing in both Cpt Drives and Thai Energy 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 Cpt Drives and Thai Energy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Cpt Drives Power and Thai Energy Storage, you can compare the effects of market volatilities on Cpt Drives and Thai Energy 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 Cpt Drives with a short position of Thai Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Cpt Drives and Thai Energy.
Diversification Opportunities for Cpt Drives and Thai Energy
0.15 | Correlation Coefficient |
Average diversification
The 3 months correlation between Cpt and Thai is 0.15. Overlapping area represents the amount of risk that can be diversified away by holding Cpt Drives Power and Thai Energy Storage in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Thai Energy Storage and Cpt Drives 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 Cpt Drives Power are associated (or correlated) with Thai Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Thai Energy Storage has no effect on the direction of Cpt Drives i.e., Cpt Drives and Thai Energy go up and down completely randomly.
Pair Corralation between Cpt Drives and Thai Energy
Assuming the 90 days trading horizon Cpt Drives Power is expected to generate 1.0 times more return on investment than Thai Energy. However, Cpt Drives Power is 1.0 times less risky than Thai Energy. It trades about 0.05 of its potential returns per unit of risk. Thai Energy Storage is currently generating about 0.05 per unit of risk. If you would invest 69.00 in Cpt Drives Power on August 31, 2024 and sell it today you would earn a total of 31.00 from holding Cpt Drives Power or generate 44.93% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Cpt Drives Power vs. Thai Energy Storage
Performance |
| Timeline |
| Cpt Drives Power |
| Thai Energy Storage |
Cpt Drives and Thai Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Cpt Drives and Thai Energy
The main advantage of trading using opposite Cpt Drives and Thai Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Cpt Drives position performs unexpectedly, Thai Energy 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 Thai Energy will offset losses from the drop in Thai Energy's long position.| Cpt Drives vs. Gulf Energy Development | Cpt Drives vs. Energy Absolute Public | Cpt Drives vs. WHA Public | Cpt Drives vs. Bangkok Expressway and |
| Thai Energy vs. AJ Advance Technology | Thai Energy vs. Cpt Drives Power | Thai Energy vs. Asia Metal Public | Thai Energy vs. The Erawan Group |
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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