Correlation Between MRF and Bosch
Specify exactly 2 symbols:
By analyzing existing cross correlation between MRF Limited and Bosch Limited, you can compare the effects of market volatilities on MRF and Bosch 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 MRF with a short position of Bosch. Check out your portfolio center. Please also check ongoing floating volatility patterns of MRF and Bosch.
Diversification Opportunities for MRF and Bosch
Significant diversification
The 3 months correlation between MRF and Bosch is 0.01. Overlapping area represents the amount of risk that can be diversified away by holding MRF Limited and Bosch Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bosch Limited and MRF 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 MRF Limited are associated (or correlated) with Bosch. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bosch Limited has no effect on the direction of MRF i.e., MRF and Bosch go up and down completely randomly.
Pair Corralation between MRF and Bosch
Assuming the 90 days trading horizon MRF Limited is expected to generate 0.7 times more return on investment than Bosch. However, MRF Limited is 1.43 times less risky than Bosch. It trades about 0.07 of its potential returns per unit of risk. Bosch Limited is currently generating about -0.12 per unit of risk. If you would invest 12,250,800 in MRF Limited on August 30, 2024 and sell it today you would earn a total of 202,600 from holding MRF Limited or generate 1.65% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 95.24% |
Values | Daily Returns |
MRF Limited vs. Bosch Limited
Performance |
Timeline |
MRF Limited |
Bosch Limited |
MRF and Bosch Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with MRF and Bosch
The main advantage of trading using opposite MRF and Bosch positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if MRF position performs unexpectedly, Bosch 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 Bosch will offset losses from the drop in Bosch's long position.MRF vs. V Mart Retail Limited | MRF vs. Ortel Communications Limited | MRF vs. Spencers Retail Limited | MRF vs. Silver Touch Technologies |
Bosch vs. Jubilant Foodworks Limited | Bosch vs. Life Insurance | Bosch vs. Bikaji Foods International | Bosch vs. Styrenix Performance Materials |
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 Manager module to state of the art Portfolio Manager to monitor and improve performance of your invested capital.
Other Complementary Tools
Headlines Timeline Stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity | |
Instant Ratings Determine any equity ratings based on digital recommendations. Macroaxis instant equity ratings are based on combination of fundamental analysis and risk-adjusted market performance | |
Equity Analysis Research over 250,000 global equities including funds, stocks and ETFs to find investment opportunities | |
Idea Breakdown Analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes | |
Watchlist Optimization Optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm |