Correlation Between Budapest and BIST Electricity

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

By analyzing existing cross correlation between Budapest SE and BIST Electricity, you can compare the effects of market volatilities on Budapest and BIST Electricity 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 BIST Electricity. Check out your portfolio center. Please also check ongoing floating volatility patterns of Budapest and BIST Electricity.

Diversification Opportunities for Budapest and BIST Electricity

0.37

Correlation Coefficient

Budapest  BIST Electricity

Weak diversification

The 3 months correlation between Budapest and BIST is 0.37. Overlapping area represents the amount of risk that can be diversified away by holding Budapest SE and BIST Electricity in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BIST Electricity 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 BIST Electricity. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BIST Electricity has no effect on the direction of Budapest i.e., Budapest and BIST Electricity go up and down completely randomly.

Pair Corralation between Budapest and BIST Electricity

Assuming the 90 days trading horizon Budapest is expected to generate 5.72 times less return on investment than BIST Electricity. But when comparing it to its historical volatility, Budapest SE is 1.83 times less risky than BIST Electricity. It trades about 0.1 of its potential returns per unit of risk. BIST Electricity is currently generating about 0.3 of returns per unit of risk over similar time horizon. If you would invest  55,221  in BIST Electricity on January 30, 2024 and sell it today you would earn a total of  5,310  from holding BIST Electricity or generate 9.62% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Very Weak
Accuracy	85.0%
Values	Daily Returns

Budapest SE  vs.  BIST Electricity

Pair Trading with Budapest and BIST Electricity

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

The idea behind Budapest SE and BIST Electricity 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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.