Correlation Between Siam Commercial and North East
Can any of the company-specific risk be diversified away by investing in both Siam Commercial and North East 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 Siam Commercial and North East into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between The Siam Commercial and North East Rubbers, you can compare the effects of market volatilities on Siam Commercial and North East 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 Siam Commercial with a short position of North East. Check out your portfolio center. Please also check ongoing floating volatility patterns of Siam Commercial and North East.
Diversification Opportunities for Siam Commercial and North East
-0.21 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Siam and North is -0.21. Overlapping area represents the amount of risk that can be diversified away by holding The Siam Commercial and North East Rubbers in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on North East Rubbers and Siam Commercial 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 The Siam Commercial are associated (or correlated) with North East. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of North East Rubbers has no effect on the direction of Siam Commercial i.e., Siam Commercial and North East go up and down completely randomly.
Pair Corralation between Siam Commercial and North East
Assuming the 90 days trading horizon The Siam Commercial is expected to generate 75.19 times more return on investment than North East. However, Siam Commercial is 75.19 times more volatile than North East Rubbers. It trades about 0.12 of its potential returns per unit of risk. North East Rubbers is currently generating about -0.05 per unit of risk. If you would invest 10,945 in The Siam Commercial on September 26, 2024 and sell it today you would earn a total of 855.00 from holding The Siam Commercial or generate 7.81% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
The Siam Commercial vs. North East Rubbers
Performance |
Timeline |
Siam Commercial |
North East Rubbers |
Siam Commercial and North East Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Siam Commercial and North East
The main advantage of trading using opposite Siam Commercial and North East positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Siam Commercial position performs unexpectedly, North East 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 North East will offset losses from the drop in North East's long position.Siam Commercial vs. PTT Public | Siam Commercial vs. CP ALL Public | Siam Commercial vs. SCB X Public | Siam Commercial vs. Airports of Thailand |
North East vs. PTT Public | North East vs. The Siam Commercial | North East vs. Airports of Thailand | North East vs. CP ALL Public |
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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