Correlation Between Silver Bullet and Freddie Mac
Can any of the company-specific risk be diversified away by investing in both Silver Bullet and Freddie Mac 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 Silver Bullet and Freddie Mac into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Silver Bullet Data and Freddie Mac, you can compare the effects of market volatilities on Silver Bullet and Freddie Mac 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 Silver Bullet with a short position of Freddie Mac. Check out your portfolio center. Please also check ongoing floating volatility patterns of Silver Bullet and Freddie Mac.
Diversification Opportunities for Silver Bullet and Freddie Mac
0.68 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Silver and Freddie is 0.68. Overlapping area represents the amount of risk that can be diversified away by holding Silver Bullet Data and Freddie Mac in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Freddie Mac and Silver Bullet 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 Silver Bullet Data are associated (or correlated) with Freddie Mac. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Freddie Mac has no effect on the direction of Silver Bullet i.e., Silver Bullet and Freddie Mac go up and down completely randomly.
Pair Corralation between Silver Bullet and Freddie Mac
Assuming the 90 days trading horizon Silver Bullet is expected to generate 14.66 times less return on investment than Freddie Mac. But when comparing it to its historical volatility, Silver Bullet Data is 4.6 times less risky than Freddie Mac. It trades about 0.12 of its potential returns per unit of risk. Freddie Mac is currently generating about 0.37 of returns per unit of risk over similar time horizon. If you would invest 230.00 in Freddie Mac on October 6, 2024 and sell it today you would earn a total of 178.00 from holding Freddie Mac or generate 77.39% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Silver Bullet Data vs. Freddie Mac
Performance |
Timeline |
Silver Bullet Data |
Freddie Mac |
Silver Bullet and Freddie Mac Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Silver Bullet and Freddie Mac
The main advantage of trading using opposite Silver Bullet and Freddie Mac positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Silver Bullet position performs unexpectedly, Freddie Mac 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 Freddie Mac will offset losses from the drop in Freddie Mac's long position.Silver Bullet vs. Pressure Technologies Plc | Silver Bullet vs. Eneraqua Technologies PLC | Silver Bullet vs. PureTech Health plc | Silver Bullet vs. Amedeo Air Four |
Freddie Mac vs. Scandic Hotels Group | Freddie Mac vs. FC Investment Trust | Freddie Mac vs. InterContinental Hotels Group | Freddie Mac vs. EJF Investments |
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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