Correlation Between US Bancorp and US Bancorp
Can any of the company-specific risk be diversified away by investing in both US Bancorp and US Bancorp 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 US Bancorp and US Bancorp into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between US Bancorp and US Bancorp, you can compare the effects of market volatilities on US Bancorp and US Bancorp 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 US Bancorp with a short position of US Bancorp. Check out your portfolio center. Please also check ongoing floating volatility patterns of US Bancorp and US Bancorp.
Diversification Opportunities for US Bancorp and US Bancorp
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between USB-PP and USB-PQ is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding US Bancorp and US Bancorp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on US Bancorp and US Bancorp 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 US Bancorp are associated (or correlated) with US Bancorp. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of US Bancorp has no effect on the direction of US Bancorp i.e., US Bancorp and US Bancorp go up and down completely randomly.
Pair Corralation between US Bancorp and US Bancorp
Assuming the 90 days trading horizon US Bancorp is expected to generate 0.46 times more return on investment than US Bancorp. However, US Bancorp is 2.18 times less risky than US Bancorp. It trades about 0.01 of its potential returns per unit of risk. US Bancorp is currently generating about -0.06 per unit of risk. If you would invest 2,486 in US Bancorp on August 27, 2024 and sell it today you would earn a total of 2.00 from holding US Bancorp or generate 0.08% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
US Bancorp vs. US Bancorp
Performance |
| Timeline |
| US Bancorp |
| US Bancorp |
US Bancorp and US Bancorp Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with US Bancorp and US Bancorp
The main advantage of trading using opposite US Bancorp and US Bancorp positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if US Bancorp position performs unexpectedly, US Bancorp 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 Bancorp will offset losses from the drop in US Bancorp's long position.The idea behind US Bancorp and US Bancorp 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.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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