Correlation Between Bank Negara and Wijaya Karya
Can any of the company-specific risk be diversified away by investing in both Bank Negara and Wijaya Karya 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 Bank Negara and Wijaya Karya into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank Negara Indonesia and Wijaya Karya Beton, you can compare the effects of market volatilities on Bank Negara and Wijaya Karya 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 Bank Negara with a short position of Wijaya Karya. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Negara and Wijaya Karya.
Diversification Opportunities for Bank Negara and Wijaya Karya
0.88 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Bank and Wijaya is 0.88. Overlapping area represents the amount of risk that can be diversified away by holding Bank Negara Indonesia and Wijaya Karya Beton in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Wijaya Karya Beton and Bank Negara 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 Bank Negara Indonesia are associated (or correlated) with Wijaya Karya. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Wijaya Karya Beton has no effect on the direction of Bank Negara i.e., Bank Negara and Wijaya Karya go up and down completely randomly.
Pair Corralation between Bank Negara and Wijaya Karya
Assuming the 90 days trading horizon Bank Negara Indonesia is expected to generate 1.21 times more return on investment than Wijaya Karya. However, Bank Negara is 1.21 times more volatile than Wijaya Karya Beton. It trades about 0.03 of its potential returns per unit of risk. Wijaya Karya Beton is currently generating about -0.02 per unit of risk. If you would invest 426,587 in Bank Negara Indonesia on September 14, 2024 and sell it today you would earn a total of 61,413 from holding Bank Negara Indonesia or generate 14.4% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 99.79% |
Values | Daily Returns |
Bank Negara Indonesia vs. Wijaya Karya Beton
Performance |
Timeline |
Bank Negara Indonesia |
Wijaya Karya Beton |
Bank Negara and Wijaya Karya Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bank Negara and Wijaya Karya
The main advantage of trading using opposite Bank Negara and Wijaya Karya positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Negara position performs unexpectedly, Wijaya Karya 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 Wijaya Karya will offset losses from the drop in Wijaya Karya's long position.Bank Negara vs. Bank Mandiri Persero | Bank Negara vs. Bank Rakyat Indonesia | Bank Negara vs. Bank Central Asia | Bank Negara vs. Astra International Tbk |
Wijaya Karya vs. Adhi Karya Persero | Wijaya Karya vs. Waskita Karya Persero | Wijaya Karya vs. Pembangunan Perumahan PT | Wijaya Karya vs. Jasa Marga 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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