Correlation Between United States and Listed Funds
Can any of the company-specific risk be diversified away by investing in both United States and Listed Funds 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 United States and Listed Funds into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between United States Oil and Listed Funds Trust, you can compare the effects of market volatilities on United States and Listed Funds 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 United States with a short position of Listed Funds. Check out your portfolio center. Please also check ongoing floating volatility patterns of United States and Listed Funds.
Diversification Opportunities for United States and Listed Funds
0.16 | Correlation Coefficient |
Average diversification
The 3 months correlation between United and Listed is 0.16. Overlapping area represents the amount of risk that can be diversified away by holding United States Oil and Listed Funds Trust in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Listed Funds Trust and United States 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 United States Oil are associated (or correlated) with Listed Funds. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Listed Funds Trust has no effect on the direction of United States i.e., United States and Listed Funds go up and down completely randomly.
Pair Corralation between United States and Listed Funds
Considering the 90-day investment horizon United States Oil is expected to generate 1.85 times more return on investment than Listed Funds. However, United States is 1.85 times more volatile than Listed Funds Trust. It trades about 0.08 of its potential returns per unit of risk. Listed Funds Trust is currently generating about -0.23 per unit of risk. If you would invest 7,473 in United States Oil on October 6, 2024 and sell it today you would earn a total of 319.00 from holding United States Oil or generate 4.27% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
United States Oil vs. Listed Funds Trust
Performance |
Timeline |
United States Oil |
Listed Funds Trust |
United States and Listed Funds Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with United States and Listed Funds
The main advantage of trading using opposite United States and Listed Funds positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if United States position performs unexpectedly, Listed Funds 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 Listed Funds will offset losses from the drop in Listed Funds' long position.United States vs. United States Natural | United States vs. SPDR Gold Shares | United States vs. ProShares Ultra Bloomberg | United States vs. Energy Select Sector |
Listed Funds vs. Teucrium Agricultural | Listed Funds vs. Teucrium Sugar | Listed Funds vs. Teucrium Soybean | Listed Funds vs. Teucrium Wheat |
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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