Correlation Between Vienna Insurance and PX Prague

Can any of the company-specific risk be diversified away by investing in both Vienna Insurance and PX Prague 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 Vienna Insurance and PX Prague into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Vienna Insurance Group and PX Prague Stock, you can compare the effects of market volatilities on Vienna Insurance and PX Prague 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 Vienna Insurance with a short position of PX Prague. Check out your portfolio center. Please also check ongoing floating volatility patterns of Vienna Insurance and PX Prague.

Diversification Opportunities for Vienna Insurance and PX Prague

-0.57

Correlation Coefficient

Vienna Insurance  PX Prague

Excellent diversification

The 3 months correlation between Vienna and PX Prague is -0.57. Overlapping area represents the amount of risk that can be diversified away by holding Vienna Insurance Group and PX Prague Stock in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PX Prague Stock and Vienna Insurance 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 Vienna Insurance Group are associated (or correlated) with PX Prague. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PX Prague Stock has no effect on the direction of Vienna Insurance i.e., Vienna Insurance and PX Prague go up and down completely randomly.

Pair Corralation between Vienna Insurance and PX Prague

Assuming the 90 days trading horizon Vienna Insurance Group is expected to under-perform the PX Prague. In addition to that, Vienna Insurance is 1.92 times more volatile than PX Prague Stock. It trades about -0.07 of its total potential returns per unit of risk. PX Prague Stock is currently generating about 0.2 per unit of volatility. If you would invest  164,334  in PX Prague Stock on August 30, 2024 and sell it today you would earn a total of  3,613  from holding PX Prague Stock or generate 2.2% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Against
Strength	Very Weak
Accuracy	100.0%
Values	Daily Returns

Vienna Insurance Group  vs.  PX Prague Stock

Pair Trading with Vienna Insurance and PX Prague

The main advantage of trading using opposite Vienna Insurance and PX Prague positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Vienna Insurance position performs unexpectedly, PX Prague 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 PX Prague will offset losses from the drop in PX Prague's long position.

The idea behind Vienna Insurance Group and PX Prague Stock pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.

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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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.