Correlation Between GFPT Public and Gunkul Engineering
Can any of the company-specific risk be diversified away by investing in both GFPT Public and Gunkul Engineering 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 GFPT Public and Gunkul Engineering into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between GFPT Public and Gunkul Engineering Public, you can compare the effects of market volatilities on GFPT Public and Gunkul Engineering 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 GFPT Public with a short position of Gunkul Engineering. Check out your portfolio center. Please also check ongoing floating volatility patterns of GFPT Public and Gunkul Engineering.
Diversification Opportunities for GFPT Public and Gunkul Engineering
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between GFPT and Gunkul is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding GFPT Public and Gunkul Engineering Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Gunkul Engineering Public and GFPT Public 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 GFPT Public are associated (or correlated) with Gunkul Engineering. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Gunkul Engineering Public has no effect on the direction of GFPT Public i.e., GFPT Public and Gunkul Engineering go up and down completely randomly.
Pair Corralation between GFPT Public and Gunkul Engineering
Assuming the 90 days trading horizon GFPT Public is expected to generate 0.81 times more return on investment than Gunkul Engineering. However, GFPT Public is 1.23 times less risky than Gunkul Engineering. It trades about -0.02 of its potential returns per unit of risk. Gunkul Engineering Public is currently generating about -0.22 per unit of risk. If you would invest 1,070 in GFPT Public on September 14, 2024 and sell it today you would lose (10.00) from holding GFPT Public or give up 0.93% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
GFPT Public vs. Gunkul Engineering Public
Performance |
| Timeline |
| GFPT Public |
| Gunkul Engineering Public |
GFPT Public and Gunkul Engineering Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GFPT Public and Gunkul Engineering
The main advantage of trading using opposite GFPT Public and Gunkul Engineering positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GFPT Public position performs unexpectedly, Gunkul Engineering 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 Gunkul Engineering will offset losses from the drop in Gunkul Engineering's long position.| GFPT Public vs. Charoen Pokphand Foods | GFPT Public vs. Hana Microelectronics Public | GFPT Public vs. AP Public | GFPT Public vs. Thai Vegetable Oil |
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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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