Correlation Between Fondo Mutuo and Karachi 100

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

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

Diversification Opportunities for Fondo Mutuo and Karachi 100

0.56

Correlation Coefficient

Fondo Mutuo  Karachi 100

Very weak diversification

The 3 months correlation between Fondo and Karachi is 0.56. Overlapping area represents the amount of risk that can be diversified away by holding Fondo Mutuo ETF and Karachi 100 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Karachi 100 and Fondo Mutuo 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 Fondo Mutuo ETF are associated (or correlated) with Karachi 100. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Karachi 100 has no effect on the direction of Fondo Mutuo i.e., Fondo Mutuo and Karachi 100 go up and down completely randomly.

Pair Corralation between Fondo Mutuo and Karachi 100

Assuming the 90 days trading horizon Fondo Mutuo is expected to generate 14.98 times less return on investment than Karachi 100. But when comparing it to its historical volatility, Fondo Mutuo ETF is 1.54 times less risky than Karachi 100. It trades about 0.04 of its potential returns per unit of risk. Karachi 100 is currently generating about 0.36 of returns per unit of risk over similar time horizon. If you would invest  7,848,822  in Karachi 100 on August 30, 2024 and sell it today you would earn a total of  2,078,103  from holding Karachi 100 or generate 26.48% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Weak
Accuracy	93.65%
Values	Daily Returns

Fondo Mutuo ETF  vs.  Karachi 100

Pair Trading with Fondo Mutuo and Karachi 100

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

The idea behind Fondo Mutuo ETF and Karachi 100 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.