Correlation Between MK Restaurant and Saha Pathanapibul
Can any of the company-specific risk be diversified away by investing in both MK Restaurant and Saha Pathanapibul 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 MK Restaurant and Saha Pathanapibul into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between MK Restaurant Group and Saha Pathanapibul Public, you can compare the effects of market volatilities on MK Restaurant and Saha Pathanapibul 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 MK Restaurant with a short position of Saha Pathanapibul. Check out your portfolio center. Please also check ongoing floating volatility patterns of MK Restaurant and Saha Pathanapibul.
Diversification Opportunities for MK Restaurant and Saha Pathanapibul
-0.24 | Correlation Coefficient |
Very good diversification
The 3 months correlation between MK Restaurant and Saha is -0.24. Overlapping area represents the amount of risk that can be diversified away by holding MK Restaurant Group and Saha Pathanapibul Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Saha Pathanapibul Public and MK Restaurant 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 MK Restaurant Group are associated (or correlated) with Saha Pathanapibul. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Saha Pathanapibul Public has no effect on the direction of MK Restaurant i.e., MK Restaurant and Saha Pathanapibul go up and down completely randomly.
Pair Corralation between MK Restaurant and Saha Pathanapibul
Given the investment horizon of 90 days MK Restaurant Group is expected to under-perform the Saha Pathanapibul. In addition to that, MK Restaurant is 3.75 times more volatile than Saha Pathanapibul Public. It trades about -0.13 of its total potential returns per unit of risk. Saha Pathanapibul Public is currently generating about 0.04 per unit of volatility. If you would invest 5,800 in Saha Pathanapibul Public on December 31, 2024 and sell it today you would earn a total of 100.00 from holding Saha Pathanapibul Public or generate 1.72% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
MK Restaurant Group vs. Saha Pathanapibul Public
Performance |
Timeline |
MK Restaurant Group |
Saha Pathanapibul Public |
MK Restaurant and Saha Pathanapibul Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with MK Restaurant and Saha Pathanapibul
The main advantage of trading using opposite MK Restaurant and Saha Pathanapibul positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if MK Restaurant position performs unexpectedly, Saha Pathanapibul 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 Saha Pathanapibul will offset losses from the drop in Saha Pathanapibul's long position.MK Restaurant vs. Minor International Public | MK Restaurant vs. Home Product Center | MK Restaurant vs. CP ALL Public | MK Restaurant vs. Central Pattana Public |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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