Correlation Between Shanghai Metersbonwe and Kingclean Electric
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By analyzing existing cross correlation between Shanghai Metersbonwe FashionAccessories and Kingclean Electric Co, you can compare the effects of market volatilities on Shanghai Metersbonwe and Kingclean Electric 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 Shanghai Metersbonwe with a short position of Kingclean Electric. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shanghai Metersbonwe and Kingclean Electric.
Diversification Opportunities for Shanghai Metersbonwe and Kingclean Electric
0.85 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Shanghai and Kingclean is 0.85. Overlapping area represents the amount of risk that can be diversified away by holding Shanghai Metersbonwe FashionAc and Kingclean Electric Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kingclean Electric and Shanghai Metersbonwe 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 Shanghai Metersbonwe FashionAccessories are associated (or correlated) with Kingclean Electric. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kingclean Electric has no effect on the direction of Shanghai Metersbonwe i.e., Shanghai Metersbonwe and Kingclean Electric go up and down completely randomly.
Pair Corralation between Shanghai Metersbonwe and Kingclean Electric
Assuming the 90 days trading horizon Shanghai Metersbonwe FashionAccessories is expected to generate 2.24 times more return on investment than Kingclean Electric. However, Shanghai Metersbonwe is 2.24 times more volatile than Kingclean Electric Co. It trades about 0.08 of its potential returns per unit of risk. Kingclean Electric Co is currently generating about -0.13 per unit of risk. If you would invest 164.00 in Shanghai Metersbonwe FashionAccessories on August 25, 2024 and sell it today you would earn a total of 9.00 from holding Shanghai Metersbonwe FashionAccessories or generate 5.49% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Shanghai Metersbonwe FashionAc vs. Kingclean Electric Co
Performance |
| Timeline |
| Shanghai Metersbonwe |
| Kingclean Electric |
Shanghai Metersbonwe and Kingclean Electric Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Shanghai Metersbonwe and Kingclean Electric
The main advantage of trading using opposite Shanghai Metersbonwe and Kingclean Electric positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shanghai Metersbonwe position performs unexpectedly, Kingclean Electric 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 Kingclean Electric will offset losses from the drop in Kingclean Electric's long position.| Shanghai Metersbonwe vs. Industrial and Commercial | Shanghai Metersbonwe vs. Agricultural Bank of | Shanghai Metersbonwe vs. China Construction Bank | Shanghai Metersbonwe vs. Bank of China |
| Kingclean Electric vs. Bomesc Offshore Engineering | Kingclean Electric vs. China World Trade | Kingclean Electric vs. Eastroc Beverage Group | Kingclean Electric vs. Ping An Insurance |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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