Correlation Between SASA Polyester and BIST Electricity

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

By analyzing existing cross correlation between SASA Polyester Sanayi and BIST Electricity, you can compare the effects of market volatilities on SASA Polyester 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 SASA Polyester with a short position of BIST Electricity. Check out your portfolio center. Please also check ongoing floating volatility patterns of SASA Polyester and BIST Electricity.

Diversification Opportunities for SASA Polyester and BIST Electricity

-0.54

Correlation Coefficient

SASA Polyester  BIST Electricity

Excellent diversification

The 3 months correlation between SASA and BIST is -0.54. Overlapping area represents the amount of risk that can be diversified away by holding SASA Polyester Sanayi and BIST Electricity in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BIST Electricity and SASA Polyester 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 SASA Polyester Sanayi 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 SASA Polyester i.e., SASA Polyester and BIST Electricity go up and down completely randomly.

Pair Corralation between SASA Polyester and BIST Electricity

Assuming the 90 days trading horizon SASA Polyester Sanayi is expected to under-perform the BIST Electricity. In addition to that, SASA Polyester is 1.74 times more volatile than BIST Electricity. It trades about -0.17 of its total potential returns per unit of risk. BIST Electricity is currently generating about 0.16 per unit of volatility. If you would invest  49,365  in BIST Electricity on April 20, 2025 and sell it today you would earn a total of  6,455  from holding BIST Electricity or generate 13.08% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Against
Strength	Very Weak
Accuracy	98.33%
Values	Daily Returns

SASA Polyester Sanayi  vs.  BIST Electricity

Pair Trading with SASA Polyester and BIST Electricity

The main advantage of trading using opposite SASA Polyester and BIST Electricity positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SASA Polyester 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 SASA Polyester Sanayi 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.