Correlation Between Guizhou BroadcastingTV and Tianjin Pengling
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By analyzing existing cross correlation between Guizhou BroadcastingTV Info and Tianjin Pengling Rubber, you can compare the effects of market volatilities on Guizhou BroadcastingTV and Tianjin Pengling 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 Guizhou BroadcastingTV with a short position of Tianjin Pengling. Check out your portfolio center. Please also check ongoing floating volatility patterns of Guizhou BroadcastingTV and Tianjin Pengling.
Diversification Opportunities for Guizhou BroadcastingTV and Tianjin Pengling
0.94 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Guizhou and Tianjin is 0.94. Overlapping area represents the amount of risk that can be diversified away by holding Guizhou BroadcastingTV Info and Tianjin Pengling Rubber in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Tianjin Pengling Rubber and Guizhou BroadcastingTV 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 Guizhou BroadcastingTV Info are associated (or correlated) with Tianjin Pengling. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Tianjin Pengling Rubber has no effect on the direction of Guizhou BroadcastingTV i.e., Guizhou BroadcastingTV and Tianjin Pengling go up and down completely randomly.
Pair Corralation between Guizhou BroadcastingTV and Tianjin Pengling
Assuming the 90 days trading horizon Guizhou BroadcastingTV is expected to generate 1.06 times less return on investment than Tianjin Pengling. But when comparing it to its historical volatility, Guizhou BroadcastingTV Info is 1.13 times less risky than Tianjin Pengling. It trades about 0.02 of its potential returns per unit of risk. Tianjin Pengling Rubber is currently generating about 0.02 of returns per unit of risk over similar time horizon. If you would invest 545.00 in Tianjin Pengling Rubber on September 8, 2024 and sell it today you would lose (15.00) from holding Tianjin Pengling Rubber or give up 2.75% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Guizhou BroadcastingTV Info vs. Tianjin Pengling Rubber
Performance |
Timeline |
Guizhou BroadcastingTV |
Tianjin Pengling Rubber |
Guizhou BroadcastingTV and Tianjin Pengling Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Guizhou BroadcastingTV and Tianjin Pengling
The main advantage of trading using opposite Guizhou BroadcastingTV and Tianjin Pengling positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Guizhou BroadcastingTV position performs unexpectedly, Tianjin Pengling 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 Tianjin Pengling will offset losses from the drop in Tianjin Pengling's long position.Guizhou BroadcastingTV vs. Bank of China | Guizhou BroadcastingTV vs. Kweichow Moutai Co | Guizhou BroadcastingTV vs. PetroChina Co Ltd | Guizhou BroadcastingTV vs. Bank of Communications |
Tianjin Pengling vs. Holitech Technology Co | Tianjin Pengling vs. AVIC Fund Management | Tianjin Pengling vs. OBiO Technology Corp | Tianjin Pengling vs. Maxvision Technology Corp |
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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