Correlation Between Bank Negara and Gudang Garam
Can any of the company-specific risk be diversified away by investing in both Bank Negara and Gudang Garam 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 Gudang Garam 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 Gudang Garam Tbk, you can compare the effects of market volatilities on Bank Negara and Gudang Garam 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 Gudang Garam. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Negara and Gudang Garam.
Diversification Opportunities for Bank Negara and Gudang Garam
0.33 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Bank and Gudang is 0.33. Overlapping area represents the amount of risk that can be diversified away by holding Bank Negara Indonesia and Gudang Garam Tbk in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Gudang Garam Tbk 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 Gudang Garam. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Gudang Garam Tbk has no effect on the direction of Bank Negara i.e., Bank Negara and Gudang Garam go up and down completely randomly.
Pair Corralation between Bank Negara and Gudang Garam
Assuming the 90 days trading horizon Bank Negara Indonesia is expected to generate 1.56 times more return on investment than Gudang Garam. However, Bank Negara is 1.56 times more volatile than Gudang Garam Tbk. It trades about 0.0 of its potential returns per unit of risk. Gudang Garam Tbk is currently generating about -0.2 per unit of risk. If you would invest 435,000 in Bank Negara Indonesia on December 30, 2024 and sell it today you would lose (11,000) from holding Bank Negara Indonesia or give up 2.53% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Bank Negara Indonesia vs. Gudang Garam Tbk
Performance |
Timeline |
Bank Negara Indonesia |
Gudang Garam Tbk |
Bank Negara and Gudang Garam Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bank Negara and Gudang Garam
The main advantage of trading using opposite Bank Negara and Gudang Garam positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Negara position performs unexpectedly, Gudang Garam 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 Gudang Garam will offset losses from the drop in Gudang Garam'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 |
Gudang Garam vs. Hanjaya Mandala Sampoerna | Gudang Garam vs. Unilever Indonesia Tbk | Gudang Garam vs. PT Indofood Sukses | Gudang Garam vs. United Tractors 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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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