Correlation Between Budapest and Bucharest BET-NG

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Can any of the company-specific risk be diversified away by investing in both Budapest and Bucharest BET-NG 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 Budapest and Bucharest BET-NG into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Budapest SE and Bucharest BET-NG, you can compare the effects of market volatilities on Budapest and Bucharest BET-NG 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 Budapest with a short position of Bucharest BET-NG. Check out your portfolio center. Please also check ongoing floating volatility patterns of Budapest and Bucharest BET-NG.

Diversification Opportunities for Budapest and Bucharest BET-NG

-0.32
  Correlation Coefficient

Very good diversification

The 3 months correlation between Budapest and Bucharest is -0.32. Overlapping area represents the amount of risk that can be diversified away by holding Budapest SE and Bucharest BET-NG in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bucharest BET-NG and Budapest 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 Budapest SE are associated (or correlated) with Bucharest BET-NG. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bucharest BET-NG has no effect on the direction of Budapest i.e., Budapest and Bucharest BET-NG go up and down completely randomly.
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Pair Corralation between Budapest and Bucharest BET-NG

Assuming the 90 days trading horizon Budapest SE is expected to generate 1.16 times more return on investment than Bucharest BET-NG. However, Budapest is 1.16 times more volatile than Bucharest BET-NG. It trades about 0.19 of its potential returns per unit of risk. Bucharest BET-NG is currently generating about -0.13 per unit of risk. If you would invest  7,286,531  in Budapest SE on August 30, 2024 and sell it today you would earn a total of  616,869  from holding Budapest SE or generate 8.47% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Against 
StrengthInsignificant
Accuracy96.88%
ValuesDaily Returns

Budapest SE  vs.  Bucharest BET-NG

 Performance 
       Timeline  

Budapest and Bucharest BET-NG Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Budapest and Bucharest BET-NG

The main advantage of trading using opposite Budapest and Bucharest BET-NG positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Budapest position performs unexpectedly, Bucharest BET-NG 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 Bucharest BET-NG will offset losses from the drop in Bucharest BET-NG's long position.
The idea behind Budapest SE and Bucharest BET-NG 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 Portfolio Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.

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