Correlation Between GRINM Semiconductor and Beijing Kingsoft
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By analyzing existing cross correlation between GRINM Semiconductor Materials and Beijing Kingsoft Office, you can compare the effects of market volatilities on GRINM Semiconductor and Beijing Kingsoft 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 GRINM Semiconductor with a short position of Beijing Kingsoft. Check out your portfolio center. Please also check ongoing floating volatility patterns of GRINM Semiconductor and Beijing Kingsoft.
Diversification Opportunities for GRINM Semiconductor and Beijing Kingsoft
0.42 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between GRINM and Beijing is 0.42. Overlapping area represents the amount of risk that can be diversified away by holding GRINM Semiconductor Materials and Beijing Kingsoft Office in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Beijing Kingsoft Office and GRINM Semiconductor 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 GRINM Semiconductor Materials are associated (or correlated) with Beijing Kingsoft. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Beijing Kingsoft Office has no effect on the direction of GRINM Semiconductor i.e., GRINM Semiconductor and Beijing Kingsoft go up and down completely randomly.
Pair Corralation between GRINM Semiconductor and Beijing Kingsoft
Assuming the 90 days trading horizon GRINM Semiconductor Materials is expected to under-perform the Beijing Kingsoft. But the stock apears to be less risky and, when comparing its historical volatility, GRINM Semiconductor Materials is 1.4 times less risky than Beijing Kingsoft. The stock trades about -0.09 of its potential returns per unit of risk. The Beijing Kingsoft Office is currently generating about 0.04 of returns per unit of risk over similar time horizon. If you would invest 26,450 in Beijing Kingsoft Office on October 25, 2024 and sell it today you would earn a total of 1,269 from holding Beijing Kingsoft Office or generate 4.8% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
GRINM Semiconductor Materials vs. Beijing Kingsoft Office
Performance |
Timeline |
GRINM Semiconductor |
Beijing Kingsoft Office |
GRINM Semiconductor and Beijing Kingsoft Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GRINM Semiconductor and Beijing Kingsoft
The main advantage of trading using opposite GRINM Semiconductor and Beijing Kingsoft positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GRINM Semiconductor position performs unexpectedly, Beijing Kingsoft 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 Beijing Kingsoft will offset losses from the drop in Beijing Kingsoft's long position.GRINM Semiconductor vs. Kweichow Moutai Co | GRINM Semiconductor vs. NAURA Technology Group | GRINM Semiconductor vs. APT Medical | GRINM Semiconductor vs. BYD Co Ltd |
Beijing Kingsoft vs. Kweichow Moutai Co | Beijing Kingsoft vs. NAURA Technology Group | Beijing Kingsoft vs. APT Medical | Beijing Kingsoft vs. BYD Co Ltd |
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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