Correlation Between Univacco Technology and Kwong Fong
Can any of the company-specific risk be diversified away by investing in both Univacco Technology 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 Univacco Technology and Kwong Fong into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Univacco Technology and Kwong Fong Industries, you can compare the effects of market volatilities on Univacco Technology 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 Univacco Technology with a short position of Kwong Fong. Check out your portfolio center. Please also check ongoing floating volatility patterns of Univacco Technology and Kwong Fong.
Diversification Opportunities for Univacco Technology and Kwong Fong
0.38 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Univacco and Kwong is 0.38. Overlapping area represents the amount of risk that can be diversified away by holding Univacco Technology 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 Univacco Technology 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 Univacco Technology 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 Univacco Technology i.e., Univacco Technology and Kwong Fong go up and down completely randomly.
Pair Corralation between Univacco Technology and Kwong Fong
Assuming the 90 days trading horizon Univacco Technology is expected to under-perform the Kwong Fong. In addition to that, Univacco Technology is 2.31 times more volatile than Kwong Fong Industries. It trades about -0.05 of its total potential returns per unit of risk. Kwong Fong Industries is currently generating about -0.06 per unit of volatility. If you would invest 1,345 in Kwong Fong Industries on October 9, 2024 and sell it today you would lose (85.00) from holding Kwong Fong Industries or give up 6.32% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 98.41% |
Values | Daily Returns |
Univacco Technology vs. Kwong Fong Industries
Performance |
Timeline |
Univacco Technology |
Kwong Fong Industries |
Univacco Technology and Kwong Fong Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Univacco Technology and Kwong Fong
The main advantage of trading using opposite Univacco Technology and Kwong Fong positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Univacco Technology 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.Univacco Technology vs. Hotel Holiday Garden | Univacco Technology vs. Johnson Health Tech | Univacco Technology vs. Pacific Hospital Supply | Univacco Technology vs. First Hotel Co |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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