Correlation Between GRINM Semiconductor and Jiangxi Naipu
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By analyzing existing cross correlation between GRINM Semiconductor Materials and Jiangxi Naipu Mining, you can compare the effects of market volatilities on GRINM Semiconductor and Jiangxi Naipu 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 Jiangxi Naipu. Check out your portfolio center. Please also check ongoing floating volatility patterns of GRINM Semiconductor and Jiangxi Naipu.
Diversification Opportunities for GRINM Semiconductor and Jiangxi Naipu
-0.36 | Correlation Coefficient |
Very good diversification
The 3 months correlation between GRINM and Jiangxi is -0.36. Overlapping area represents the amount of risk that can be diversified away by holding GRINM Semiconductor Materials and Jiangxi Naipu Mining in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Jiangxi Naipu Mining 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 Jiangxi Naipu. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Jiangxi Naipu Mining has no effect on the direction of GRINM Semiconductor i.e., GRINM Semiconductor and Jiangxi Naipu go up and down completely randomly.
Pair Corralation between GRINM Semiconductor and Jiangxi Naipu
Assuming the 90 days trading horizon GRINM Semiconductor Materials is expected to generate 1.26 times more return on investment than Jiangxi Naipu. However, GRINM Semiconductor is 1.26 times more volatile than Jiangxi Naipu Mining. It trades about 0.02 of its potential returns per unit of risk. Jiangxi Naipu Mining is currently generating about -0.14 per unit of risk. If you would invest 1,176 in GRINM Semiconductor Materials on December 30, 2024 and sell it today you would earn a total of 19.00 from holding GRINM Semiconductor Materials or generate 1.62% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
GRINM Semiconductor Materials vs. Jiangxi Naipu Mining
Performance |
Timeline |
GRINM Semiconductor |
Jiangxi Naipu Mining |
GRINM Semiconductor and Jiangxi Naipu Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GRINM Semiconductor and Jiangxi Naipu
The main advantage of trading using opposite GRINM Semiconductor and Jiangxi Naipu positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GRINM Semiconductor position performs unexpectedly, Jiangxi Naipu 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 Jiangxi Naipu will offset losses from the drop in Jiangxi Naipu's long position.The idea behind GRINM Semiconductor Materials and Jiangxi Naipu Mining pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.
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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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