Correlation Between Louisa Professional and Shan Loong
Can any of the company-specific risk be diversified away by investing in both Louisa Professional and Shan Loong 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 Louisa Professional and Shan Loong into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Louisa Professional Coffee and Shan Loong Transportation Co, you can compare the effects of market volatilities on Louisa Professional and Shan Loong 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 Louisa Professional with a short position of Shan Loong. Check out your portfolio center. Please also check ongoing floating volatility patterns of Louisa Professional and Shan Loong.
Diversification Opportunities for Louisa Professional and Shan Loong
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Louisa and Shan is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Louisa Professional Coffee and Shan Loong Transportation Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shan Loong Transport and Louisa Professional 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 Louisa Professional Coffee are associated (or correlated) with Shan Loong. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shan Loong Transport has no effect on the direction of Louisa Professional i.e., Louisa Professional and Shan Loong go up and down completely randomly.
Pair Corralation between Louisa Professional and Shan Loong
Assuming the 90 days trading horizon Louisa Professional Coffee is expected to under-perform the Shan Loong. In addition to that, Louisa Professional is 1.18 times more volatile than Shan Loong Transportation Co. It trades about -0.28 of its total potential returns per unit of risk. Shan Loong Transportation Co is currently generating about -0.21 per unit of volatility. If you would invest 2,050 in Shan Loong Transportation Co on September 18, 2024 and sell it today you would lose (270.00) from holding Shan Loong Transportation Co or give up 13.17% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Louisa Professional Coffee vs. Shan Loong Transportation Co
Performance |
Timeline |
Louisa Professional |
Shan Loong Transport |
Louisa Professional and Shan Loong Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Louisa Professional and Shan Loong
The main advantage of trading using opposite Louisa Professional and Shan Loong positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Louisa Professional position performs unexpectedly, Shan Loong 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 Shan Loong will offset losses from the drop in Shan Loong's long position.Louisa Professional vs. Gourmet Master Co | Louisa Professional vs. Wowprime Corp | Louisa Professional vs. Kura Sushi Asia | Louisa Professional vs. TTFB 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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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