Correlation Between GAMESTOP and Banpu PCL
Can any of the company-specific risk be diversified away by investing in both GAMESTOP and Banpu PCL 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 GAMESTOP and Banpu PCL into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between GAMESTOP and Banpu PCL, you can compare the effects of market volatilities on GAMESTOP and Banpu PCL 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 GAMESTOP with a short position of Banpu PCL. Check out your portfolio center. Please also check ongoing floating volatility patterns of GAMESTOP and Banpu PCL.
Diversification Opportunities for GAMESTOP and Banpu PCL
Pay attention - limited upside
The 3 months correlation between GAMESTOP and Banpu is -0.72. Overlapping area represents the amount of risk that can be diversified away by holding GAMESTOP and Banpu PCL in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Banpu PCL and GAMESTOP 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 GAMESTOP are associated (or correlated) with Banpu PCL. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Banpu PCL has no effect on the direction of GAMESTOP i.e., GAMESTOP and Banpu PCL go up and down completely randomly.
Pair Corralation between GAMESTOP and Banpu PCL
Assuming the 90 days trading horizon GAMESTOP is expected to generate 0.73 times more return on investment than Banpu PCL. However, GAMESTOP is 1.36 times less risky than Banpu PCL. It trades about 0.19 of its potential returns per unit of risk. Banpu PCL is currently generating about -0.03 per unit of risk. If you would invest 1,985 in GAMESTOP on September 27, 2024 and sell it today you would earn a total of 966.00 from holding GAMESTOP or generate 48.66% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
GAMESTOP vs. Banpu PCL
Performance |
Timeline |
GAMESTOP |
Banpu PCL |
GAMESTOP and Banpu PCL Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GAMESTOP and Banpu PCL
The main advantage of trading using opposite GAMESTOP and Banpu PCL positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GAMESTOP position performs unexpectedly, Banpu PCL 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 Banpu PCL will offset losses from the drop in Banpu PCL's long position.The idea behind GAMESTOP and Banpu PCL 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.Banpu PCL vs. CHINA SHENHUA ENA | Banpu PCL vs. Yancoal Australia | Banpu PCL vs. CONSOL Energy | Banpu PCL vs. HMS Bergbau AG |
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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