Correlation Between Intel and First Trust
Can any of the company-specific risk be diversified away by investing in both Intel and First Trust 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 Intel and First Trust into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Intel and First Trust China, you can compare the effects of market volatilities on Intel and First Trust 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 Intel with a short position of First Trust. Check out your portfolio center. Please also check ongoing floating volatility patterns of Intel and First Trust.
Diversification Opportunities for Intel and First Trust
0.6 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Intel and First is 0.6. Overlapping area represents the amount of risk that can be diversified away by holding Intel and First Trust China in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on First Trust China and Intel 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 Intel are associated (or correlated) with First Trust. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of First Trust China has no effect on the direction of Intel i.e., Intel and First Trust go up and down completely randomly.
Pair Corralation between Intel and First Trust
Given the investment horizon of 90 days Intel is expected to generate 0.87 times more return on investment than First Trust. However, Intel is 1.15 times less risky than First Trust. It trades about 0.03 of its potential returns per unit of risk. First Trust China is currently generating about -0.03 per unit of risk. If you would invest 2,346 in Intel on August 31, 2024 and sell it today you would earn a total of 59.00 from holding Intel or generate 2.51% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 97.78% |
| Values | Daily Returns |
Intel vs. First Trust China
Performance |
| Timeline |
| Intel |
| First Trust China |
Intel and First Trust Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Intel and First Trust
The main advantage of trading using opposite Intel and First Trust positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Intel position performs unexpectedly, First Trust 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 First Trust will offset losses from the drop in First Trust's long position.The idea behind Intel and First Trust China 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.| First Trust vs. First Trust Japan | First Trust vs. First Trust Asia | First Trust vs. First Trust Brazil | First Trust vs. First Trust Latin |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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