Correlation Between Snap and FS Energy
Can any of the company-specific risk be diversified away by investing in both Snap and FS Energy 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 FS Energy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Snap Inc and FS Energy and, you can compare the effects of market volatilities on Snap and FS Energy 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 FS Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Snap and FS Energy.
Diversification Opportunities for Snap and FS Energy
Very weak diversification
The 3 months correlation between Snap and FSEN is 0.41. Overlapping area represents the amount of risk that can be diversified away by holding Snap Inc and FS Energy and in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FS Energy 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 FS Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FS Energy has no effect on the direction of Snap i.e., Snap and FS Energy go up and down completely randomly.
Pair Corralation between Snap and FS Energy
Given the investment horizon of 90 days Snap Inc is expected to under-perform the FS Energy. But the stock apears to be less risky and, when comparing its historical volatility, Snap Inc is 3.43 times less risky than FS Energy. The stock trades about -0.11 of its potential returns per unit of risk. The FS Energy and is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 250.00 in FS Energy and on August 31, 2024 and sell it today you would earn a total of 0.00 from holding FS Energy and or generate 0.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Snap Inc vs. FS Energy and
Performance |
| Timeline |
| Snap Inc |
| FS Energy |
Snap and FS Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Snap and FS Energy
The main advantage of trading using opposite Snap and FS Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Snap position performs unexpectedly, FS Energy 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 FS Energy will offset losses from the drop in FS Energy's long position.The idea behind Snap Inc and FS Energy and 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.| FS Energy vs. Business Development Corp | FS Energy vs. Inpex Corp ADR | FS Energy vs. Daikin IndustriesLtd |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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