Correlation Between Profile Systems and National Bank
Can any of the company-specific risk be diversified away by investing in both Profile Systems and National Bank 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 Profile Systems and National Bank into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Profile Systems Software and National Bank of, you can compare the effects of market volatilities on Profile Systems and National Bank 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 Profile Systems with a short position of National Bank. Check out your portfolio center. Please also check ongoing floating volatility patterns of Profile Systems and National Bank.
Diversification Opportunities for Profile Systems and National Bank
0.65 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Profile and National is 0.65. Overlapping area represents the amount of risk that can be diversified away by holding Profile Systems Software and National Bank of in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on National Bank and Profile Systems 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 Profile Systems Software are associated (or correlated) with National Bank. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of National Bank has no effect on the direction of Profile Systems i.e., Profile Systems and National Bank go up and down completely randomly.
Pair Corralation between Profile Systems and National Bank
Assuming the 90 days trading horizon Profile Systems Software is expected to under-perform the National Bank. But the stock apears to be less risky and, when comparing its historical volatility, Profile Systems Software is 1.1 times less risky than National Bank. The stock trades about -0.08 of its potential returns per unit of risk. The National Bank of is currently generating about 0.23 of returns per unit of risk over similar time horizon. If you would invest 800.00 in National Bank of on November 5, 2024 and sell it today you would earn a total of 38.00 from holding National Bank of or generate 4.75% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Profile Systems Software vs. National Bank of
Performance |
| Timeline |
| Profile Systems Software |
| National Bank |
Profile Systems and National Bank Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Profile Systems and National Bank
The main advantage of trading using opposite Profile Systems and National Bank positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Profile Systems position performs unexpectedly, National Bank 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 National Bank will offset losses from the drop in National Bank's long position.| Profile Systems vs. Terna Energy Societe | Profile Systems vs. Mytilineos SA | Profile Systems vs. Public Power | Profile Systems vs. Autohellas SA |
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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 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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