Correlation Analysis Between OMXRGI and NQTH

This module allows you to analyze existing cross correlation between OMXRGI and NQTH. You can compare the effects of market volatilities on OMXRGI and NQTH 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 OMXRGI with a short position of NQTH. See also your portfolio center. Please also check ongoing floating volatility patterns of OMXRGI and NQTH.
Horizon     30 Days    Login   to change
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Comparative Performance

 Predicted Return Density 


 Performance (%) 

Pair Volatility

Assuming 30 trading days horizon, OMXRGI is expected to generate 64.1 times more return on investment than NQTH. However, OMXRGI is 64.1 times more volatile than NQTH. It trades about 0.07 of its potential returns per unit of risk. NQTH is currently generating about 0.01 per unit of risk. If you would invest  0.00  in OMXRGI on May 25, 2019 and sell it today you would earn a total of  102,514  from holding OMXRGI or generate 9.223372036854776E16% return on investment over 30 days.

Pair Corralation between OMXRGI and NQTH

Time Period2 Months [change]
StrengthVery Weak
ValuesDaily Returns

Diversification Opportunities for OMXRGI and NQTH

OMXRGI diversification synergy

Weak diversification

Overlapping area represents the amount of risk that can be diversified away by holding OMXRGI and NQTH in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on NQTH and OMXRGI 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 OMXRGI are associated (or correlated) with NQTH. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NQTH has no effect on the direction of OMXRGI i.e. OMXRGI and NQTH go up and down completely randomly.
See also your portfolio center. Please also try Pattern Recognition module to use different pattern recognition models to time the market across multiple global exchanges.