Correlation Between Singer Thailand and SiS Distribution
Can any of the company-specific risk be diversified away by investing in both Singer Thailand and SiS Distribution 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 Singer Thailand and SiS Distribution into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Singer Thailand Public and SiS Distribution Public, you can compare the effects of market volatilities on Singer Thailand and SiS Distribution 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 Singer Thailand with a short position of SiS Distribution. Check out your portfolio center. Please also check ongoing floating volatility patterns of Singer Thailand and SiS Distribution.
Diversification Opportunities for Singer Thailand and SiS Distribution
0.82 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Singer and SiS is 0.82. Overlapping area represents the amount of risk that can be diversified away by holding Singer Thailand Public and SiS Distribution Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SiS Distribution Public and Singer Thailand 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 Singer Thailand Public are associated (or correlated) with SiS Distribution. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SiS Distribution Public has no effect on the direction of Singer Thailand i.e., Singer Thailand and SiS Distribution go up and down completely randomly.
Pair Corralation between Singer Thailand and SiS Distribution
Assuming the 90 days trading horizon Singer Thailand Public is expected to under-perform the SiS Distribution. In addition to that, Singer Thailand is 1.82 times more volatile than SiS Distribution Public. It trades about -0.08 of its total potential returns per unit of risk. SiS Distribution Public is currently generating about -0.1 per unit of volatility. If you would invest 2,722 in SiS Distribution Public on December 29, 2024 and sell it today you would lose (382.00) from holding SiS Distribution Public or give up 14.03% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Singer Thailand Public vs. SiS Distribution Public
Performance |
Timeline |
Singer Thailand Public |
SiS Distribution Public |
Singer Thailand and SiS Distribution Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Singer Thailand and SiS Distribution
The main advantage of trading using opposite Singer Thailand and SiS Distribution positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Singer Thailand position performs unexpectedly, SiS Distribution 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 SiS Distribution will offset losses from the drop in SiS Distribution's long position.Singer Thailand vs. Jay Mart Public | Singer Thailand vs. JMT Network Services | Singer Thailand vs. KCE Electronics Public | Singer Thailand vs. Srisawad Power 1979 |
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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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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