Correlation Between MapsPeople and FOM Technologies
Can any of the company-specific risk be diversified away by investing in both MapsPeople and FOM Technologies 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 MapsPeople and FOM Technologies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between MapsPeople AS and FOM Technologies AS, you can compare the effects of market volatilities on MapsPeople and FOM Technologies 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 MapsPeople with a short position of FOM Technologies. Check out your portfolio center. Please also check ongoing floating volatility patterns of MapsPeople and FOM Technologies.
Diversification Opportunities for MapsPeople and FOM Technologies
0.74 | Correlation Coefficient |
Poor diversification
The 3 months correlation between MapsPeople and FOM is 0.74. Overlapping area represents the amount of risk that can be diversified away by holding MapsPeople AS and FOM Technologies AS in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FOM Technologies and MapsPeople 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 MapsPeople AS are associated (or correlated) with FOM Technologies. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FOM Technologies has no effect on the direction of MapsPeople i.e., MapsPeople and FOM Technologies go up and down completely randomly.
Pair Corralation between MapsPeople and FOM Technologies
Assuming the 90 days trading horizon MapsPeople AS is expected to generate 2.65 times more return on investment than FOM Technologies. However, MapsPeople is 2.65 times more volatile than FOM Technologies AS. It trades about 0.02 of its potential returns per unit of risk. FOM Technologies AS is currently generating about -0.46 per unit of risk. If you would invest 161.00 in MapsPeople AS on August 29, 2024 and sell it today you would lose (12.00) from holding MapsPeople AS or give up 7.45% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
MapsPeople AS vs. FOM Technologies AS
Performance |
| Timeline |
| MapsPeople AS |
| FOM Technologies |
MapsPeople and FOM Technologies Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with MapsPeople and FOM Technologies
The main advantage of trading using opposite MapsPeople and FOM Technologies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if MapsPeople position performs unexpectedly, FOM Technologies 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 FOM Technologies will offset losses from the drop in FOM Technologies' long position.| MapsPeople vs. Konsolidator AS | MapsPeople vs. Sparinvest INDEX Globale | MapsPeople vs. Bavarian Nordic | MapsPeople vs. Investeringsselskabet Luxor AS |
| FOM Technologies vs. cBrain AS | FOM Technologies vs. Penneo AS | FOM Technologies vs. Shape Robotics AS | FOM Technologies vs. ALK Abell AS |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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