Correlation Between AAPICO Hitech and Lotus Retail
Can any of the company-specific risk be diversified away by investing in both AAPICO Hitech and Lotus Retail 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 AAPICO Hitech and Lotus Retail into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between AAPICO Hitech Public and Lotus Retail Growth, you can compare the effects of market volatilities on AAPICO Hitech and Lotus Retail 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 AAPICO Hitech with a short position of Lotus Retail. Check out your portfolio center. Please also check ongoing floating volatility patterns of AAPICO Hitech and Lotus Retail.
Diversification Opportunities for AAPICO Hitech and Lotus Retail
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between AAPICO and Lotus is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding AAPICO Hitech Public and Lotus Retail Growth in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Lotus Retail Growth and AAPICO Hitech 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 AAPICO Hitech Public are associated (or correlated) with Lotus Retail. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Lotus Retail Growth has no effect on the direction of AAPICO Hitech i.e., AAPICO Hitech and Lotus Retail go up and down completely randomly.
Pair Corralation between AAPICO Hitech and Lotus Retail
Assuming the 90 days horizon AAPICO Hitech Public is expected to under-perform the Lotus Retail. In addition to that, AAPICO Hitech is 1.9 times more volatile than Lotus Retail Growth. It trades about -0.03 of its total potential returns per unit of risk. Lotus Retail Growth is currently generating about 0.03 per unit of volatility. If you would invest 1,095 in Lotus Retail Growth on August 31, 2024 and sell it today you would earn a total of 175.00 from holding Lotus Retail Growth or generate 15.98% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 98.92% |
Values | Daily Returns |
AAPICO Hitech Public vs. Lotus Retail Growth
Performance |
Timeline |
AAPICO Hitech Public |
Lotus Retail Growth |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
AAPICO Hitech and Lotus Retail Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with AAPICO Hitech and Lotus Retail
The main advantage of trading using opposite AAPICO Hitech and Lotus Retail positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if AAPICO Hitech position performs unexpectedly, Lotus Retail 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 Lotus Retail will offset losses from the drop in Lotus Retail's long position.AAPICO Hitech vs. TRC Construction Public | AAPICO Hitech vs. Bangkok Expressway and | AAPICO Hitech vs. Lohakit Metal Public | AAPICO Hitech vs. Gunkul Engineering 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 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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