Correlation Between Shuang Bang and Kwong Fong
Can any of the company-specific risk be diversified away by investing in both Shuang Bang and Kwong Fong 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 Shuang Bang and Kwong Fong into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shuang Bang Industrial and Kwong Fong Industries, you can compare the effects of market volatilities on Shuang Bang and Kwong Fong 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 Shuang Bang with a short position of Kwong Fong. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shuang Bang and Kwong Fong.
Diversification Opportunities for Shuang Bang and Kwong Fong
0.36 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Shuang and Kwong is 0.36. Overlapping area represents the amount of risk that can be diversified away by holding Shuang Bang Industrial and Kwong Fong Industries in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kwong Fong Industries and Shuang Bang 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 Shuang Bang Industrial are associated (or correlated) with Kwong Fong. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kwong Fong Industries has no effect on the direction of Shuang Bang i.e., Shuang Bang and Kwong Fong go up and down completely randomly.
Pair Corralation between Shuang Bang and Kwong Fong
Assuming the 90 days trading horizon Shuang Bang is expected to generate 1.38 times less return on investment than Kwong Fong. In addition to that, Shuang Bang is 1.72 times more volatile than Kwong Fong Industries. It trades about 0.02 of its total potential returns per unit of risk. Kwong Fong Industries is currently generating about 0.05 per unit of volatility. If you would invest 1,255 in Kwong Fong Industries on September 14, 2024 and sell it today you would earn a total of 60.00 from holding Kwong Fong Industries or generate 4.78% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Shuang Bang Industrial vs. Kwong Fong Industries
Performance |
Timeline |
Shuang Bang Industrial |
Kwong Fong Industries |
Shuang Bang and Kwong Fong Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Shuang Bang and Kwong Fong
The main advantage of trading using opposite Shuang Bang and Kwong Fong positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shuang Bang position performs unexpectedly, Kwong Fong 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 Kwong Fong will offset losses from the drop in Kwong Fong's long position.Shuang Bang vs. Delta Electronics | Shuang Bang vs. Ruentex Development Co | Shuang Bang vs. WiseChip Semiconductor | Shuang Bang vs. Novatek Microelectronics Corp |
Kwong Fong vs. Standard Foods Corp | Kwong Fong vs. Est Global Apparel | Kwong Fong vs. General Plastic Industrial | Kwong Fong vs. Shuang Bang Industrial |
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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