Correlation Between Kung Long and U Ming
Can any of the company-specific risk be diversified away by investing in both Kung Long and U Ming 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 Kung Long and U Ming into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Kung Long Batteries and U Ming Marine Transport, you can compare the effects of market volatilities on Kung Long and U Ming 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 Kung Long with a short position of U Ming. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kung Long and U Ming.
Diversification Opportunities for Kung Long and U Ming
Average diversification
The 3 months correlation between Kung and 2606 is 0.19. Overlapping area represents the amount of risk that can be diversified away by holding Kung Long Batteries and U Ming Marine Transport in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on U Ming Marine and Kung Long 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 Kung Long Batteries are associated (or correlated) with U Ming. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of U Ming Marine has no effect on the direction of Kung Long i.e., Kung Long and U Ming go up and down completely randomly.
Pair Corralation between Kung Long and U Ming
Assuming the 90 days trading horizon Kung Long is expected to generate 3.97 times less return on investment than U Ming. In addition to that, Kung Long is 1.19 times more volatile than U Ming Marine Transport. It trades about 0.03 of its total potential returns per unit of risk. U Ming Marine Transport is currently generating about 0.13 per unit of volatility. If you would invest 5,180 in U Ming Marine Transport on September 15, 2024 and sell it today you would earn a total of 540.00 from holding U Ming Marine Transport or generate 10.42% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Kung Long Batteries vs. U Ming Marine Transport
Performance |
Timeline |
Kung Long Batteries |
U Ming Marine |
Kung Long and U Ming Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Kung Long and U Ming
The main advantage of trading using opposite Kung Long and U Ming positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kung Long position performs unexpectedly, U Ming 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 U Ming will offset losses from the drop in U Ming's long position.The idea behind Kung Long Batteries and U Ming Marine Transport pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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