Correlation Between Bikaji Foods and Styrenix Performance
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By analyzing existing cross correlation between Bikaji Foods International and Styrenix Performance Materials, you can compare the effects of market volatilities on Bikaji Foods and Styrenix Performance 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 Bikaji Foods with a short position of Styrenix Performance. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bikaji Foods and Styrenix Performance.
Diversification Opportunities for Bikaji Foods and Styrenix Performance
0.48 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Bikaji and Styrenix is 0.48. Overlapping area represents the amount of risk that can be diversified away by holding Bikaji Foods International and Styrenix Performance Materials in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Styrenix Performance and Bikaji Foods 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 Bikaji Foods International are associated (or correlated) with Styrenix Performance. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Styrenix Performance has no effect on the direction of Bikaji Foods i.e., Bikaji Foods and Styrenix Performance go up and down completely randomly.
Pair Corralation between Bikaji Foods and Styrenix Performance
Assuming the 90 days trading horizon Bikaji Foods is expected to generate 2.08 times less return on investment than Styrenix Performance. But when comparing it to its historical volatility, Bikaji Foods International is 1.07 times less risky than Styrenix Performance. It trades about 0.06 of its potential returns per unit of risk. Styrenix Performance Materials is currently generating about 0.12 of returns per unit of risk over similar time horizon. If you would invest 68,255 in Styrenix Performance Materials on November 28, 2024 and sell it today you would earn a total of 175,825 from holding Styrenix Performance Materials or generate 257.6% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 99.79% |
| Values | Daily Returns |
Bikaji Foods International vs. Styrenix Performance Materials
Performance |
| Timeline |
| Bikaji Foods Interna |
| Styrenix Performance |
Bikaji Foods and Styrenix Performance Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bikaji Foods and Styrenix Performance
The main advantage of trading using opposite Bikaji Foods and Styrenix Performance positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bikaji Foods position performs unexpectedly, Styrenix Performance 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 Styrenix Performance will offset losses from the drop in Styrenix Performance's long position.| Bikaji Foods vs. Tamilnadu Telecommunication Limited | Bikaji Foods vs. DMCC SPECIALITY CHEMICALS | Bikaji Foods vs. One 97 Communications | Bikaji Foods vs. S P Apparels |
| Styrenix Performance vs. Indraprastha Medical | Styrenix Performance vs. Infomedia Press Limited | Styrenix Performance vs. Network18 Media Investments | Styrenix Performance vs. Zee Entertainment Enterprises |
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 Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.
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