Correlation Between Bekasi Fajar and Bank Artos
Can any of the company-specific risk be diversified away by investing in both Bekasi Fajar and Bank Artos 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 Bekasi Fajar and Bank Artos into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bekasi Fajar Industrial and Bank Artos Indonesia, you can compare the effects of market volatilities on Bekasi Fajar and Bank Artos 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 Bekasi Fajar with a short position of Bank Artos. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bekasi Fajar and Bank Artos.
Diversification Opportunities for Bekasi Fajar and Bank Artos
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Bekasi and Bank is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding Bekasi Fajar Industrial and Bank Artos Indonesia in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bank Artos Indonesia and Bekasi Fajar 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 Bekasi Fajar Industrial are associated (or correlated) with Bank Artos. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bank Artos Indonesia has no effect on the direction of Bekasi Fajar i.e., Bekasi Fajar and Bank Artos go up and down completely randomly.
Pair Corralation between Bekasi Fajar and Bank Artos
Assuming the 90 days trading horizon Bekasi Fajar Industrial is expected to generate 0.75 times more return on investment than Bank Artos. However, Bekasi Fajar Industrial is 1.33 times less risky than Bank Artos. It trades about -0.14 of its potential returns per unit of risk. Bank Artos Indonesia is currently generating about -0.17 per unit of risk. If you would invest 10,300 in Bekasi Fajar Industrial on December 1, 2024 and sell it today you would lose (1,900) from holding Bekasi Fajar Industrial or give up 18.45% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Bekasi Fajar Industrial vs. Bank Artos Indonesia
Performance |
Timeline |
Bekasi Fajar Industrial |
Bank Artos Indonesia |
Bekasi Fajar and Bank Artos Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bekasi Fajar and Bank Artos
The main advantage of trading using opposite Bekasi Fajar and Bank Artos positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bekasi Fajar position performs unexpectedly, Bank Artos 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 Bank Artos will offset losses from the drop in Bank Artos' long position.Bekasi Fajar vs. Agung Podomoro Land | Bekasi Fajar vs. Surya Semesta Internusa | Bekasi Fajar vs. Alam Sutera Realty | Bekasi Fajar vs. Bumi Serpong Damai |
Bank Artos vs. Elang Mahkota Teknologi | Bank Artos vs. Bank Yudha Bhakti | Bank Artos vs. Bk Harda Internasional | Bank Artos vs. PT Bukalapak |
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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