Correlation Between Unilever Pakistan and National Foods
Can any of the company-specific risk be diversified away by investing in both Unilever Pakistan and National Foods 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 Unilever Pakistan and National Foods into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Unilever Pakistan Foods and National Foods, you can compare the effects of market volatilities on Unilever Pakistan and National Foods 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 Unilever Pakistan with a short position of National Foods. Check out your portfolio center. Please also check ongoing floating volatility patterns of Unilever Pakistan and National Foods.
Diversification Opportunities for Unilever Pakistan and National Foods
-0.09 | Correlation Coefficient |
Good diversification
The 3 months correlation between Unilever and National is -0.09. Overlapping area represents the amount of risk that can be diversified away by holding Unilever Pakistan Foods and National Foods in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on National Foods and Unilever Pakistan 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 Unilever Pakistan Foods are associated (or correlated) with National Foods. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of National Foods has no effect on the direction of Unilever Pakistan i.e., Unilever Pakistan and National Foods go up and down completely randomly.
Pair Corralation between Unilever Pakistan and National Foods
Assuming the 90 days trading horizon Unilever Pakistan Foods is expected to generate 0.69 times more return on investment than National Foods. However, Unilever Pakistan Foods is 1.45 times less risky than National Foods. It trades about 0.15 of its potential returns per unit of risk. National Foods is currently generating about 0.08 per unit of risk. If you would invest 1,866,686 in Unilever Pakistan Foods on August 30, 2024 and sell it today you would earn a total of 70,127 from holding Unilever Pakistan Foods or generate 3.76% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Unilever Pakistan Foods vs. National Foods
Performance |
Timeline |
Unilever Pakistan Foods |
National Foods |
Unilever Pakistan and National Foods Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Unilever Pakistan and National Foods
The main advantage of trading using opposite Unilever Pakistan and National Foods positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Unilever Pakistan position performs unexpectedly, National Foods 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 National Foods will offset losses from the drop in National Foods' long position.Unilever Pakistan vs. Shaheen Insurance | Unilever Pakistan vs. Hi Tech Lubricants | Unilever Pakistan vs. Century Insurance | Unilever Pakistan vs. 786 Investment Limited |
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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 Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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