Correlation Between Karachi 100 and Bank of Punjab

Can any of the company-specific risk be diversified away by investing in both Karachi 100 and Bank of Punjab 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 Karachi 100 and Bank of Punjab into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Karachi 100 and Bank of Punjab, you can compare the effects of market volatilities on Karachi 100 and Bank of Punjab 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 Karachi 100 with a short position of Bank of Punjab. Check out your portfolio center. Please also check ongoing floating volatility patterns of Karachi 100 and Bank of Punjab.

Diversification Opportunities for Karachi 100 and Bank of Punjab

0.43

Correlation Coefficient

Karachi 100  Bank of Punjab

Very weak diversification

The 3 months correlation between Karachi and Bank is 0.43. Overlapping area represents the amount of risk that can be diversified away by holding Karachi 100 and Bank of Punjab in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bank of Punjab and Karachi 100 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 Karachi 100 are associated (or correlated) with Bank of Punjab. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bank of Punjab has no effect on the direction of Karachi 100 i.e., Karachi 100 and Bank of Punjab go up and down completely randomly.

Pair Corralation between Karachi 100 and Bank of Punjab

Assuming the 90 days trading horizon Karachi 100 is expected to generate 8.64 times less return on investment than Bank of Punjab. But when comparing it to its historical volatility, Karachi 100 is 3.52 times less risky than Bank of Punjab. It trades about 0.04 of its potential returns per unit of risk. Bank of Punjab is currently generating about 0.11 of returns per unit of risk over similar time horizon. If you would invest  903.00  in Bank of Punjab on December 30, 2024 and sell it today you would earn a total of  179.00  from holding Bank of Punjab or generate 19.82% return on investment over 90 days.

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

Karachi 100  vs.  Bank of Punjab

Pair Trading with Karachi 100 and Bank of Punjab

The main advantage of trading using opposite Karachi 100 and Bank of Punjab positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Karachi 100 position performs unexpectedly, Bank of Punjab 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 Bank of Punjab will offset losses from the drop in Bank of Punjab's long position.

The idea behind Karachi 100 and Bank of Punjab 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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.