Correlation Between Singha Estate and Wave Entertainment
Can any of the company-specific risk be diversified away by investing in both Singha Estate and Wave Entertainment 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 Singha Estate and Wave Entertainment into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Singha Estate Public and Wave Entertainment Public, you can compare the effects of market volatilities on Singha Estate and Wave Entertainment 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 Singha Estate with a short position of Wave Entertainment. Check out your portfolio center. Please also check ongoing floating volatility patterns of Singha Estate and Wave Entertainment.
Diversification Opportunities for Singha Estate and Wave Entertainment
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Singha and Wave is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Singha Estate Public and Wave Entertainment Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Wave Entertainment Public and Singha Estate 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 Singha Estate Public are associated (or correlated) with Wave Entertainment. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Wave Entertainment Public has no effect on the direction of Singha Estate i.e., Singha Estate and Wave Entertainment go up and down completely randomly.
Pair Corralation between Singha Estate and Wave Entertainment
Given the investment horizon of 90 days Singha Estate Public is expected to generate 0.3 times more return on investment than Wave Entertainment. However, Singha Estate Public is 3.37 times less risky than Wave Entertainment. It trades about -0.19 of its potential returns per unit of risk. Wave Entertainment Public is currently generating about -0.1 per unit of risk. If you would invest 89.00 in Singha Estate Public on December 28, 2024 and sell it today you would lose (22.00) from holding Singha Estate Public or give up 24.72% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Singha Estate Public vs. Wave Entertainment Public
Performance |
Timeline |
Singha Estate Public |
Wave Entertainment Public |
Singha Estate and Wave Entertainment Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Singha Estate and Wave Entertainment
The main advantage of trading using opposite Singha Estate and Wave Entertainment positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Singha Estate position performs unexpectedly, Wave Entertainment 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 Wave Entertainment will offset losses from the drop in Wave Entertainment's long position.Singha Estate vs. Siri Prime Office | Singha Estate vs. WHA Public | Singha Estate vs. Supalai Public | Singha Estate vs. Quality Houses 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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