Correlation Between Asuransi Harta and Steel Pipe
Can any of the company-specific risk be diversified away by investing in both Asuransi Harta and Steel Pipe 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 Asuransi Harta and Steel Pipe into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Asuransi Harta Aman and Steel Pipe Industry, you can compare the effects of market volatilities on Asuransi Harta and Steel Pipe 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 Asuransi Harta with a short position of Steel Pipe. Check out your portfolio center. Please also check ongoing floating volatility patterns of Asuransi Harta and Steel Pipe.
Diversification Opportunities for Asuransi Harta and Steel Pipe
0.75 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Asuransi and Steel is 0.75. Overlapping area represents the amount of risk that can be diversified away by holding Asuransi Harta Aman and Steel Pipe Industry in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Steel Pipe Industry and Asuransi Harta 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 Asuransi Harta Aman are associated (or correlated) with Steel Pipe. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Steel Pipe Industry has no effect on the direction of Asuransi Harta i.e., Asuransi Harta and Steel Pipe go up and down completely randomly.
Pair Corralation between Asuransi Harta and Steel Pipe
Assuming the 90 days trading horizon Asuransi Harta Aman is expected to under-perform the Steel Pipe. In addition to that, Asuransi Harta is 1.76 times more volatile than Steel Pipe Industry. It trades about -0.24 of its total potential returns per unit of risk. Steel Pipe Industry is currently generating about -0.13 per unit of volatility. If you would invest 31,000 in Steel Pipe Industry on September 6, 2024 and sell it today you would lose (2,600) from holding Steel Pipe Industry or give up 8.39% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Asuransi Harta Aman vs. Steel Pipe Industry
Performance |
Timeline |
Asuransi Harta Aman |
Steel Pipe Industry |
Asuransi Harta and Steel Pipe Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Asuransi Harta and Steel Pipe
The main advantage of trading using opposite Asuransi Harta and Steel Pipe positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Asuransi Harta position performs unexpectedly, Steel Pipe 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 Steel Pipe will offset losses from the drop in Steel Pipe's long position.Asuransi Harta vs. Paninvest Tbk | Asuransi Harta vs. Mitra Pinasthika Mustika | Asuransi Harta vs. Jakarta Int Hotels | Asuransi Harta vs. Indosterling Technomedia Tbk |
Steel Pipe vs. Mitra Pinasthika Mustika | Steel Pipe vs. Jakarta Int Hotels | Steel Pipe vs. Asuransi Harta Aman | Steel Pipe vs. Indosterling Technomedia Tbk |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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