Correlation Between Pratama Abadi and Bank Dinar
Can any of the company-specific risk be diversified away by investing in both Pratama Abadi and Bank Dinar 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 Pratama Abadi and Bank Dinar into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Pratama Abadi Nusa and Bank Dinar Indonesia, you can compare the effects of market volatilities on Pratama Abadi and Bank Dinar 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 Pratama Abadi with a short position of Bank Dinar. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pratama Abadi and Bank Dinar.
Diversification Opportunities for Pratama Abadi and Bank Dinar
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Pratama and Bank is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding Pratama Abadi Nusa and Bank Dinar Indonesia in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bank Dinar Indonesia and Pratama Abadi 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 Pratama Abadi Nusa are associated (or correlated) with Bank Dinar. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bank Dinar Indonesia has no effect on the direction of Pratama Abadi i.e., Pratama Abadi and Bank Dinar go up and down completely randomly.
Pair Corralation between Pratama Abadi and Bank Dinar
Assuming the 90 days trading horizon Pratama Abadi Nusa is expected to generate 0.99 times more return on investment than Bank Dinar. However, Pratama Abadi Nusa is 1.01 times less risky than Bank Dinar. It trades about 0.17 of its potential returns per unit of risk. Bank Dinar Indonesia is currently generating about -0.01 per unit of risk. If you would invest 99,000 in Pratama Abadi Nusa on September 27, 2024 and sell it today you would earn a total of 1,521,000 from holding Pratama Abadi Nusa or generate 1536.36% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Pratama Abadi Nusa vs. Bank Dinar Indonesia
Performance |
Timeline |
Pratama Abadi Nusa |
Bank Dinar Indonesia |
Pratama Abadi and Bank Dinar Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Pratama Abadi and Bank Dinar
The main advantage of trading using opposite Pratama Abadi and Bank Dinar positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pratama Abadi position performs unexpectedly, Bank Dinar 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 Dinar will offset losses from the drop in Bank Dinar's long position.Pratama Abadi vs. Garudafood Putra Putri | Pratama Abadi vs. Campina Ice Cream | Pratama Abadi vs. Uni Charm Indonesia | Pratama Abadi vs. Sariguna Primatirta PT |
Bank Dinar vs. Maskapai Reasuransi Indonesia | Bank Dinar vs. Panin Sekuritas Tbk | Bank Dinar vs. Wahana Ottomitra Multiartha | Bank Dinar vs. Lenox Pasifik Investama |
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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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