Correlation Between THRACE PLASTICS and Astral Foods
Can any of the company-specific risk be diversified away by investing in both THRACE PLASTICS and Astral Foods 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 THRACE PLASTICS and Astral Foods into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between THRACE PLASTICS and Astral Foods Limited, you can compare the effects of market volatilities on THRACE PLASTICS and Astral Foods 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 THRACE PLASTICS with a short position of Astral Foods. Check out your portfolio center. Please also check ongoing floating volatility patterns of THRACE PLASTICS and Astral Foods.
Diversification Opportunities for THRACE PLASTICS and Astral Foods
0.44 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between THRACE and Astral is 0.44. Overlapping area represents the amount of risk that can be diversified away by holding THRACE PLASTICS and Astral Foods Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Astral Foods Limited and THRACE PLASTICS 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 THRACE PLASTICS are associated (or correlated) with Astral Foods. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Astral Foods Limited has no effect on the direction of THRACE PLASTICS i.e., THRACE PLASTICS and Astral Foods go up and down completely randomly.
Pair Corralation between THRACE PLASTICS and Astral Foods
Assuming the 90 days trading horizon THRACE PLASTICS is expected to under-perform the Astral Foods. But the stock apears to be less risky and, when comparing its historical volatility, THRACE PLASTICS is 2.05 times less risky than Astral Foods. The stock trades about -0.01 of its potential returns per unit of risk. The Astral Foods Limited is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 730.00 in Astral Foods Limited on October 7, 2024 and sell it today you would earn a total of 180.00 from holding Astral Foods Limited or generate 24.66% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
THRACE PLASTICS vs. Astral Foods Limited
Performance |
Timeline |
THRACE PLASTICS |
Astral Foods Limited |
THRACE PLASTICS and Astral Foods Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with THRACE PLASTICS and Astral Foods
The main advantage of trading using opposite THRACE PLASTICS and Astral Foods positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if THRACE PLASTICS position performs unexpectedly, Astral Foods 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 Astral Foods will offset losses from the drop in Astral Foods' long position.THRACE PLASTICS vs. CHINA SOUTHN AIR H | THRACE PLASTICS vs. Digilife Technologies Limited | THRACE PLASTICS vs. Alaska Air Group | THRACE PLASTICS vs. GAZTRTECHNIUADR15EO01 |
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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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