Correlation Between Jakarta Int and Steel Pipe
Can any of the company-specific risk be diversified away by investing in both Jakarta Int and Steel Pipe 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 Jakarta Int and Steel Pipe into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Jakarta Int Hotels and Steel Pipe Industry, you can compare the effects of market volatilities on Jakarta Int and Steel Pipe 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 Jakarta Int with a short position of Steel Pipe. Check out your portfolio center. Please also check ongoing floating volatility patterns of Jakarta Int and Steel Pipe.
Diversification Opportunities for Jakarta Int and Steel Pipe
0.55 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Jakarta and Steel is 0.55. Overlapping area represents the amount of risk that can be diversified away by holding Jakarta Int Hotels and Steel Pipe Industry in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Steel Pipe Industry and Jakarta Int 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 Jakarta Int Hotels are associated (or correlated) with Steel Pipe. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Steel Pipe Industry has no effect on the direction of Jakarta Int i.e., Jakarta Int and Steel Pipe go up and down completely randomly.
Pair Corralation between Jakarta Int and Steel Pipe
Assuming the 90 days trading horizon Jakarta Int is expected to generate 1.06 times less return on investment than Steel Pipe. In addition to that, Jakarta Int is 2.7 times more volatile than Steel Pipe Industry. It trades about 0.02 of its total potential returns per unit of risk. Steel Pipe Industry is currently generating about 0.04 per unit of volatility. If you would invest 27,600 in Steel Pipe Industry on January 16, 2025 and sell it today you would earn a total of 400.00 from holding Steel Pipe Industry or generate 1.45% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Jakarta Int Hotels vs. Steel Pipe Industry
Performance |
| Timeline |
| Jakarta Int Hotels |
| Steel Pipe Industry |
Jakarta Int and Steel Pipe Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Jakarta Int and Steel Pipe
The main advantage of trading using opposite Jakarta Int and Steel Pipe positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Jakarta Int position performs unexpectedly, Steel Pipe 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 Steel Pipe will offset losses from the drop in Steel Pipe's long position.| Jakarta Int vs. Hero Supermarket Tbk | Jakarta Int vs. Enseval Putra Megatrading | Jakarta Int vs. Bayu Buana Tbk | Jakarta Int vs. Integra Indocabinet Tbk |
| Steel Pipe vs. Indal Aluminium Industry | Steel Pipe vs. Integra Indocabinet Tbk | Steel Pipe vs. Multistrada Arah Sarana | Steel Pipe vs. J Resources Asia |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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