Correlation Between Saigon Machinery and Khang Dien
Can any of the company-specific risk be diversified away by investing in both Saigon Machinery and Khang Dien 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 Saigon Machinery and Khang Dien into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Saigon Machinery Spare and Khang Dien House, you can compare the effects of market volatilities on Saigon Machinery and Khang Dien 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 Saigon Machinery with a short position of Khang Dien. Check out your portfolio center. Please also check ongoing floating volatility patterns of Saigon Machinery and Khang Dien.
Diversification Opportunities for Saigon Machinery and Khang Dien
-0.53 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Saigon and Khang is -0.53. Overlapping area represents the amount of risk that can be diversified away by holding Saigon Machinery Spare and Khang Dien House in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Khang Dien House and Saigon Machinery 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 Saigon Machinery Spare are associated (or correlated) with Khang Dien. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Khang Dien House has no effect on the direction of Saigon Machinery i.e., Saigon Machinery and Khang Dien go up and down completely randomly.
Pair Corralation between Saigon Machinery and Khang Dien
Assuming the 90 days trading horizon Saigon Machinery Spare is expected to generate 2.11 times more return on investment than Khang Dien. However, Saigon Machinery is 2.11 times more volatile than Khang Dien House. It trades about 0.09 of its potential returns per unit of risk. Khang Dien House is currently generating about 0.03 per unit of risk. If you would invest 986,791 in Saigon Machinery Spare on September 25, 2024 and sell it today you would earn a total of 153,209 from holding Saigon Machinery Spare or generate 15.53% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 43.65% |
Values | Daily Returns |
Saigon Machinery Spare vs. Khang Dien House
Performance |
Timeline |
Saigon Machinery Spare |
Khang Dien House |
Saigon Machinery and Khang Dien Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Saigon Machinery and Khang Dien
The main advantage of trading using opposite Saigon Machinery and Khang Dien positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Saigon Machinery position performs unexpectedly, Khang Dien 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 Khang Dien will offset losses from the drop in Khang Dien's long position.Saigon Machinery vs. FIT INVEST JSC | Saigon Machinery vs. Damsan JSC | Saigon Machinery vs. An Phat Plastic | Saigon Machinery vs. Alphanam ME |
Khang Dien vs. FIT INVEST JSC | Khang Dien vs. Damsan JSC | Khang Dien vs. An Phat Plastic | Khang Dien vs. Alphanam ME |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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