Correlation Between Greene County and Triumph Financial
Can any of the company-specific risk be diversified away by investing in both Greene County and Triumph Financial 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 Greene County and Triumph Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Greene County Bancorp and Triumph Financial, you can compare the effects of market volatilities on Greene County and Triumph Financial 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 Greene County with a short position of Triumph Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Greene County and Triumph Financial.
Diversification Opportunities for Greene County and Triumph Financial
0.4 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Greene and Triumph is 0.4. Overlapping area represents the amount of risk that can be diversified away by holding Greene County Bancorp and Triumph Financial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Triumph Financial and Greene County 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 Greene County Bancorp are associated (or correlated) with Triumph Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Triumph Financial has no effect on the direction of Greene County i.e., Greene County and Triumph Financial go up and down completely randomly.
Pair Corralation between Greene County and Triumph Financial
Given the investment horizon of 90 days Greene County is expected to generate 3.0 times less return on investment than Triumph Financial. In addition to that, Greene County is 1.41 times more volatile than Triumph Financial. It trades about 0.03 of its total potential returns per unit of risk. Triumph Financial is currently generating about 0.14 per unit of volatility. If you would invest 8,282 in Triumph Financial on September 15, 2024 and sell it today you would earn a total of 1,774 from holding Triumph Financial or generate 21.42% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Greene County Bancorp vs. Triumph Financial
Performance |
Timeline |
Greene County Bancorp |
Triumph Financial |
Greene County and Triumph Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Greene County and Triumph Financial
The main advantage of trading using opposite Greene County and Triumph Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Greene County position performs unexpectedly, Triumph Financial 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 Triumph Financial will offset losses from the drop in Triumph Financial's long position.Greene County vs. Community West Bancshares | Greene County vs. First Northwest Bancorp | Greene County vs. Home Federal Bancorp | Greene County vs. First Financial Northwest |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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