Correlation Between NR Old and Enerflex
Can any of the company-specific risk be diversified away by investing in both NR Old and Enerflex 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 NR Old and Enerflex into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NR Old and Enerflex, you can compare the effects of market volatilities on NR Old and Enerflex 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 NR Old with a short position of Enerflex. Check out your portfolio center. Please also check ongoing floating volatility patterns of NR Old and Enerflex.
Diversification Opportunities for NR Old and Enerflex
Very weak diversification
The 3 months correlation between NR Old and Enerflex is 0.47. Overlapping area represents the amount of risk that can be diversified away by holding NR Old and Enerflex in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Enerflex and NR Old 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 NR Old are associated (or correlated) with Enerflex. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Enerflex has no effect on the direction of NR Old i.e., NR Old and Enerflex go up and down completely randomly.
Pair Corralation between NR Old and Enerflex
Allowing for the 90-day total investment horizon NR Old is expected to generate 3.91 times less return on investment than Enerflex. In addition to that, NR Old is 1.34 times more volatile than Enerflex. It trades about 0.07 of its total potential returns per unit of risk. Enerflex is currently generating about 0.38 per unit of volatility. If you would invest 648.00 in Enerflex on October 25, 2024 and sell it today you would earn a total of 376.00 from holding Enerflex or generate 58.02% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 66.67% |
| Values | Daily Returns |
NR Old vs. Enerflex
Performance |
| Timeline |
| NR Old |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Modest
| Enerflex |
NR Old and Enerflex Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with NR Old and Enerflex
The main advantage of trading using opposite NR Old and Enerflex positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NR Old position performs unexpectedly, Enerflex 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 Enerflex will offset losses from the drop in Enerflex's long position.The idea behind NR Old and Enerflex 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.| Enerflex vs. Natural Gas Services | Enerflex vs. Archrock | Enerflex vs. Geospace Technologies | Enerflex vs. Forum Energy Technologies |
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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