Correlation Between Snap and Halliburton
Can any of the company-specific risk be diversified away by investing in both Snap and Halliburton 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 Snap and Halliburton into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Snap Inc and Halliburton, you can compare the effects of market volatilities on Snap and Halliburton 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 Snap with a short position of Halliburton. Check out your portfolio center. Please also check ongoing floating volatility patterns of Snap and Halliburton.
Diversification Opportunities for Snap and Halliburton
0.38 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Snap and Halliburton is 0.38. Overlapping area represents the amount of risk that can be diversified away by holding Snap Inc and Halliburton in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Halliburton and Snap 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 Snap Inc are associated (or correlated) with Halliburton. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Halliburton has no effect on the direction of Snap i.e., Snap and Halliburton go up and down completely randomly.
Pair Corralation between Snap and Halliburton
Given the investment horizon of 90 days Snap is expected to generate 20.92 times less return on investment than Halliburton. In addition to that, Snap is 1.06 times more volatile than Halliburton. It trades about 0.01 of its total potential returns per unit of risk. Halliburton is currently generating about 0.28 per unit of volatility. If you would invest 2,597 in Halliburton on September 4, 2024 and sell it today you would earn a total of 455.00 from holding Halliburton or generate 17.52% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 95.45% |
Values | Daily Returns |
Snap Inc vs. Halliburton
Performance |
Timeline |
Snap Inc |
Halliburton |
Snap and Halliburton Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Snap and Halliburton
The main advantage of trading using opposite Snap and Halliburton positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Snap position performs unexpectedly, Halliburton 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 Halliburton will offset losses from the drop in Halliburton's long position.The idea behind Snap Inc and Halliburton 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.Halliburton vs. MCEWEN MINING INC | Halliburton vs. Jacquet Metal Service | Halliburton vs. UPDATE SOFTWARE | Halliburton vs. GREENX METALS 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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