Correlation Between Transport and Tien Phong
Can any of the company-specific risk be diversified away by investing in both Transport and Tien Phong 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 Transport and Tien Phong into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Transport and Industry and Tien Phong Plastic, you can compare the effects of market volatilities on Transport and Tien Phong 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 Transport with a short position of Tien Phong. Check out your portfolio center. Please also check ongoing floating volatility patterns of Transport and Tien Phong.
Diversification Opportunities for Transport and Tien Phong
0.13 | Correlation Coefficient |
Average diversification
The 3 months correlation between Transport and Tien is 0.13. Overlapping area represents the amount of risk that can be diversified away by holding Transport and Industry and Tien Phong Plastic in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Tien Phong Plastic and Transport 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 Transport and Industry are associated (or correlated) with Tien Phong. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Tien Phong Plastic has no effect on the direction of Transport i.e., Transport and Tien Phong go up and down completely randomly.
Pair Corralation between Transport and Tien Phong
Assuming the 90 days trading horizon Transport and Industry is expected to under-perform the Tien Phong. In addition to that, Transport is 1.1 times more volatile than Tien Phong Plastic. It trades about -0.1 of its total potential returns per unit of risk. Tien Phong Plastic is currently generating about -0.01 per unit of volatility. If you would invest 6,691,923 in Tien Phong Plastic on December 5, 2024 and sell it today you would lose (341,923) from holding Tien Phong Plastic or give up 5.11% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Transport and Industry vs. Tien Phong Plastic
Performance |
Timeline |
Transport and Industry |
Tien Phong Plastic |
Transport and Tien Phong Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Transport and Tien Phong
The main advantage of trading using opposite Transport and Tien Phong positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Transport position performs unexpectedly, Tien Phong 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 Tien Phong will offset losses from the drop in Tien Phong's long position.Transport vs. An Phat Plastic | Transport vs. DIC Holdings Construction | Transport vs. Binh Duong Construction | Transport vs. Southern Rubber Industry |
Tien Phong vs. Hochiminh City Metal | Tien Phong vs. Truong Thanh Furniture | Tien Phong vs. BIDV Insurance Corp | Tien Phong vs. Vietnam Airlines JSC |
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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