Correlation Between Asuransi Harta and Sepatu Bata
Can any of the company-specific risk be diversified away by investing in both Asuransi Harta and Sepatu Bata 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 Sepatu Bata 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 Sepatu Bata Tbk, you can compare the effects of market volatilities on Asuransi Harta and Sepatu Bata 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 Sepatu Bata. Check out your portfolio center. Please also check ongoing floating volatility patterns of Asuransi Harta and Sepatu Bata.
Diversification Opportunities for Asuransi Harta and Sepatu Bata
0.65 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Asuransi and Sepatu is 0.65. Overlapping area represents the amount of risk that can be diversified away by holding Asuransi Harta Aman and Sepatu Bata Tbk in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sepatu Bata Tbk 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 Sepatu Bata. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sepatu Bata Tbk has no effect on the direction of Asuransi Harta i.e., Asuransi Harta and Sepatu Bata go up and down completely randomly.
Pair Corralation between Asuransi Harta and Sepatu Bata
Assuming the 90 days trading horizon Asuransi Harta Aman is expected to generate 1.07 times more return on investment than Sepatu Bata. However, Asuransi Harta is 1.07 times more volatile than Sepatu Bata Tbk. It trades about -0.14 of its potential returns per unit of risk. Sepatu Bata Tbk is currently generating about -0.24 per unit of risk. If you would invest 9,700 in Asuransi Harta Aman on September 5, 2024 and sell it today you would lose (600.00) from holding Asuransi Harta Aman or give up 6.19% 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. Sepatu Bata Tbk
Performance |
| Timeline |
| Asuransi Harta Aman |
| Sepatu Bata Tbk |
Asuransi Harta and Sepatu Bata Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Asuransi Harta and Sepatu Bata
The main advantage of trading using opposite Asuransi Harta and Sepatu Bata positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Asuransi Harta position performs unexpectedly, Sepatu Bata 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 Sepatu Bata will offset losses from the drop in Sepatu Bata's long position.| Asuransi Harta vs. Asuransi Bintang Tbk | Asuransi Harta vs. Asuransi Bina Dana | Asuransi Harta vs. Asuransi Dayin Mitra | Asuransi Harta vs. Asuransi Jasa Tania |
| Sepatu Bata vs. Jakarta Int Hotels | Sepatu Bata vs. Asuransi Harta Aman | Sepatu Bata vs. Indosterling Technomedia Tbk | Sepatu Bata vs. Indosat 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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