Correlation Between Electronics Fund and Energy Services
Can any of the company-specific risk be diversified away by investing in both Electronics Fund and Energy Services 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 Electronics Fund and Energy Services into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Electronics Fund Investor and Energy Services Fund, you can compare the effects of market volatilities on Electronics Fund and Energy Services 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 Electronics Fund with a short position of Energy Services. Check out your portfolio center. Please also check ongoing floating volatility patterns of Electronics Fund and Energy Services.
Diversification Opportunities for Electronics Fund and Energy Services
0.37 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Electronics and Energy is 0.37. Overlapping area represents the amount of risk that can be diversified away by holding Electronics Fund Investor and Energy Services Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Energy Services and Electronics Fund 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 Electronics Fund Investor are associated (or correlated) with Energy Services. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Energy Services has no effect on the direction of Electronics Fund i.e., Electronics Fund and Energy Services go up and down completely randomly.
Pair Corralation between Electronics Fund and Energy Services
Assuming the 90 days horizon Electronics Fund Investor is expected to generate 1.4 times more return on investment than Energy Services. However, Electronics Fund is 1.4 times more volatile than Energy Services Fund. It trades about 0.0 of its potential returns per unit of risk. Energy Services Fund is currently generating about -0.14 per unit of risk. If you would invest 42,244 in Electronics Fund Investor on November 29, 2024 and sell it today you would lose (740.00) from holding Electronics Fund Investor or give up 1.75% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Electronics Fund Investor vs. Energy Services Fund
Performance |
Timeline |
Electronics Fund Investor |
Energy Services |
Electronics Fund and Energy Services Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Electronics Fund and Energy Services
The main advantage of trading using opposite Electronics Fund and Energy Services positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Electronics Fund position performs unexpectedly, Energy Services 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 Energy Services will offset losses from the drop in Energy Services' long position.Electronics Fund vs. Technology Fund Investor | Electronics Fund vs. Financial Services Fund | Electronics Fund vs. Telecommunications Fund Investor | Electronics Fund vs. Health Care Fund |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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