Correlation Between CitiGroup and US Commodity
Can any of the company-specific risk be diversified away by investing in both CitiGroup and US Commodity 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 CitiGroup and US Commodity into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CitiGroup and US Commodity Funds, you can compare the effects of market volatilities on CitiGroup and US Commodity 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 CitiGroup with a short position of US Commodity. Check out your portfolio center. Please also check ongoing floating volatility patterns of CitiGroup and US Commodity.
Diversification Opportunities for CitiGroup and US Commodity
0.0 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between CitiGroup and USOU is 0.0. Overlapping area represents the amount of risk that can be diversified away by holding CitiGroup and US Commodity Funds in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on US Commodity Funds and CitiGroup 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 CitiGroup are associated (or correlated) with US Commodity. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of US Commodity Funds has no effect on the direction of CitiGroup i.e., CitiGroup and US Commodity go up and down completely randomly.
Pair Corralation between CitiGroup and US Commodity
If you would invest (100.00) in US Commodity Funds on January 19, 2024 and sell it today you would earn a total of 100.00 from holding US Commodity Funds or generate -100.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Flat |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
CitiGroup vs. US Commodity Funds
Performance |
| Timeline |
| CitiGroup |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
| US Commodity Funds |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
CitiGroup and US Commodity Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with CitiGroup and US Commodity
The main advantage of trading using opposite CitiGroup and US Commodity positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CitiGroup position performs unexpectedly, US Commodity 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 US Commodity will offset losses from the drop in US Commodity's long position.The idea behind CitiGroup and US Commodity Funds 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.| US Commodity vs. Zillow Group Class | US Commodity vs. Northern Lights | US Commodity vs. VanEck Vectors Moodys | US Commodity vs. BZDYF |
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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