Correlation Between SBI Insurance and RLX TECH
Can any of the company-specific risk be diversified away by investing in both SBI Insurance and RLX TECH 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 SBI Insurance and RLX TECH into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SBI Insurance Group and RLX TECH SPADR1, you can compare the effects of market volatilities on SBI Insurance and RLX TECH 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 SBI Insurance with a short position of RLX TECH. Check out your portfolio center. Please also check ongoing floating volatility patterns of SBI Insurance and RLX TECH.
Diversification Opportunities for SBI Insurance and RLX TECH
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between SBI and RLX is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding SBI Insurance Group and RLX TECH SPADR1 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on RLX TECH SPADR1 and SBI Insurance 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 SBI Insurance Group are associated (or correlated) with RLX TECH. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of RLX TECH SPADR1 has no effect on the direction of SBI Insurance i.e., SBI Insurance and RLX TECH go up and down completely randomly.
Pair Corralation between SBI Insurance and RLX TECH
Assuming the 90 days trading horizon SBI Insurance is expected to generate 1.65 times less return on investment than RLX TECH. But when comparing it to its historical volatility, SBI Insurance Group is 2.31 times less risky than RLX TECH. It trades about 0.09 of its potential returns per unit of risk. RLX TECH SPADR1 is currently generating about 0.06 of returns per unit of risk over similar time horizon. If you would invest 186.00 in RLX TECH SPADR1 on December 19, 2024 and sell it today you would earn a total of 20.00 from holding RLX TECH SPADR1 or generate 10.75% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
SBI Insurance Group vs. RLX TECH SPADR1
Performance |
Timeline |
SBI Insurance Group |
RLX TECH SPADR1 |
SBI Insurance and RLX TECH Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SBI Insurance and RLX TECH
The main advantage of trading using opposite SBI Insurance and RLX TECH positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SBI Insurance position performs unexpectedly, RLX TECH 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 RLX TECH will offset losses from the drop in RLX TECH's long position.SBI Insurance vs. Kingdee International Software | SBI Insurance vs. ATOSS SOFTWARE | SBI Insurance vs. VARIOUS EATERIES LS | SBI Insurance vs. Molson Coors Beverage |
RLX TECH vs. TCL MULTIMEDIA TECH | RLX TECH vs. Southern Cross Media | RLX TECH vs. ETFS Coffee ETC | RLX TECH vs. Universal Entertainment |
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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