Correlation Between Lalin Property and Srisawad Power
Can any of the company-specific risk be diversified away by investing in both Lalin Property and Srisawad Power 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 Lalin Property and Srisawad Power into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lalin Property Public and Srisawad Power 1979, you can compare the effects of market volatilities on Lalin Property and Srisawad Power 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 Lalin Property with a short position of Srisawad Power. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lalin Property and Srisawad Power.
Diversification Opportunities for Lalin Property and Srisawad Power
0.74 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Lalin and Srisawad is 0.74. Overlapping area represents the amount of risk that can be diversified away by holding Lalin Property Public and Srisawad Power 1979 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Srisawad Power 1979 and Lalin Property 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 Lalin Property Public are associated (or correlated) with Srisawad Power. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Srisawad Power 1979 has no effect on the direction of Lalin Property i.e., Lalin Property and Srisawad Power go up and down completely randomly.
Pair Corralation between Lalin Property and Srisawad Power
Assuming the 90 days trading horizon Lalin Property Public is expected to generate 0.44 times more return on investment than Srisawad Power. However, Lalin Property Public is 2.27 times less risky than Srisawad Power. It trades about -0.05 of its potential returns per unit of risk. Srisawad Power 1979 is currently generating about -0.03 per unit of risk. If you would invest 640.00 in Lalin Property Public on September 12, 2024 and sell it today you would lose (20.00) from holding Lalin Property Public or give up 3.12% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Lalin Property Public vs. Srisawad Power 1979
Performance |
Timeline |
Lalin Property Public |
Srisawad Power 1979 |
Lalin Property and Srisawad Power Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Lalin Property and Srisawad Power
The main advantage of trading using opposite Lalin Property and Srisawad Power positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lalin Property position performs unexpectedly, Srisawad Power 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 Srisawad Power will offset losses from the drop in Srisawad Power's long position.Lalin Property vs. AP Public | Lalin Property vs. Land and Houses | Lalin Property vs. LPN Development Public | Lalin Property vs. Supalai 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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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