Correlation Between Masood Textile and First Fidelity
Can any of the company-specific risk be diversified away by investing in both Masood Textile and First Fidelity 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 Masood Textile and First Fidelity into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Masood Textile Mills and First Fidelity Leasing, you can compare the effects of market volatilities on Masood Textile and First Fidelity 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 Masood Textile with a short position of First Fidelity. Check out your portfolio center. Please also check ongoing floating volatility patterns of Masood Textile and First Fidelity.
Diversification Opportunities for Masood Textile and First Fidelity
0.48 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Masood and First is 0.48. Overlapping area represents the amount of risk that can be diversified away by holding Masood Textile Mills and First Fidelity Leasing in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on First Fidelity Leasing and Masood Textile 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 Masood Textile Mills are associated (or correlated) with First Fidelity. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of First Fidelity Leasing has no effect on the direction of Masood Textile i.e., Masood Textile and First Fidelity go up and down completely randomly.
Pair Corralation between Masood Textile and First Fidelity
Assuming the 90 days trading horizon Masood Textile Mills is expected to generate 0.6 times more return on investment than First Fidelity. However, Masood Textile Mills is 1.68 times less risky than First Fidelity. It trades about 0.06 of its potential returns per unit of risk. First Fidelity Leasing is currently generating about 0.02 per unit of risk. If you would invest 4,969 in Masood Textile Mills on October 10, 2024 and sell it today you would earn a total of 485.00 from holding Masood Textile Mills or generate 9.76% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 92.86% |
Values | Daily Returns |
Masood Textile Mills vs. First Fidelity Leasing
Performance |
Timeline |
Masood Textile Mills |
First Fidelity Leasing |
Masood Textile and First Fidelity Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Masood Textile and First Fidelity
The main advantage of trading using opposite Masood Textile and First Fidelity positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Masood Textile position performs unexpectedly, First Fidelity 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 First Fidelity will offset losses from the drop in First Fidelity's long position.Masood Textile vs. Pak Datacom | Masood Textile vs. Universal Insurance | Masood Textile vs. Matco Foods | Masood Textile vs. Pakistan Reinsurance |
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Check out your portfolio center.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.
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