Correlation Between FIRST SAVINGS and PennyMac Mortgage
Can any of the company-specific risk be diversified away by investing in both FIRST SAVINGS and PennyMac Mortgage 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 FIRST SAVINGS and PennyMac Mortgage into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between FIRST SAVINGS FINL and PennyMac Mortgage Investment, you can compare the effects of market volatilities on FIRST SAVINGS and PennyMac Mortgage 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 FIRST SAVINGS with a short position of PennyMac Mortgage. Check out your portfolio center. Please also check ongoing floating volatility patterns of FIRST SAVINGS and PennyMac Mortgage.
Diversification Opportunities for FIRST SAVINGS and PennyMac Mortgage
-0.49 | Correlation Coefficient |
Very good diversification
The 3 months correlation between FIRST and PennyMac is -0.49. Overlapping area represents the amount of risk that can be diversified away by holding FIRST SAVINGS FINL and PennyMac Mortgage Investment in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PennyMac Mortgage and FIRST SAVINGS 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 FIRST SAVINGS FINL are associated (or correlated) with PennyMac Mortgage. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PennyMac Mortgage has no effect on the direction of FIRST SAVINGS i.e., FIRST SAVINGS and PennyMac Mortgage go up and down completely randomly.
Pair Corralation between FIRST SAVINGS and PennyMac Mortgage
Assuming the 90 days horizon FIRST SAVINGS FINL is expected to under-perform the PennyMac Mortgage. In addition to that, FIRST SAVINGS is 1.83 times more volatile than PennyMac Mortgage Investment. It trades about -0.01 of its total potential returns per unit of risk. PennyMac Mortgage Investment is currently generating about 0.14 per unit of volatility. If you would invest 1,189 in PennyMac Mortgage Investment on December 21, 2024 and sell it today you would earn a total of 141.00 from holding PennyMac Mortgage Investment or generate 11.86% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
FIRST SAVINGS FINL vs. PennyMac Mortgage Investment
Performance |
Timeline |
FIRST SAVINGS FINL |
PennyMac Mortgage |
FIRST SAVINGS and PennyMac Mortgage Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with FIRST SAVINGS and PennyMac Mortgage
The main advantage of trading using opposite FIRST SAVINGS and PennyMac Mortgage positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if FIRST SAVINGS position performs unexpectedly, PennyMac Mortgage 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 PennyMac Mortgage will offset losses from the drop in PennyMac Mortgage's long position.FIRST SAVINGS vs. INTERCONT HOTELS | FIRST SAVINGS vs. REGAL HOTEL INTL | FIRST SAVINGS vs. CyberArk Software | FIRST SAVINGS vs. MELIA HOTELS |
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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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