Correlation Between Wah Nobel and Bank of Punjab
Can any of the company-specific risk be diversified away by investing in both Wah Nobel 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 Wah Nobel 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 Wah Nobel Chemicals and Bank of Punjab, you can compare the effects of market volatilities on Wah Nobel 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 Wah Nobel with a short position of Bank of Punjab. Check out your portfolio center. Please also check ongoing floating volatility patterns of Wah Nobel and Bank of Punjab.
Diversification Opportunities for Wah Nobel and Bank of Punjab
0.37 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Wah and Bank is 0.37. Overlapping area represents the amount of risk that can be diversified away by holding Wah Nobel Chemicals 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 Wah Nobel 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 Wah Nobel Chemicals 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 Wah Nobel i.e., Wah Nobel and Bank of Punjab go up and down completely randomly.
Pair Corralation between Wah Nobel and Bank of Punjab
Assuming the 90 days trading horizon Wah Nobel is expected to generate 1.09 times less return on investment than Bank of Punjab. But when comparing it to its historical volatility, Wah Nobel Chemicals is 1.01 times less risky than Bank of Punjab. It trades about 0.05 of its potential returns per unit of risk. Bank of Punjab is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 393.00 in Bank of Punjab on August 27, 2024 and sell it today you would earn a total of 282.00 from holding Bank of Punjab or generate 71.76% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 76.91% |
| Values | Daily Returns |
Wah Nobel Chemicals vs. Bank of Punjab
Performance |
| Timeline |
| Wah Nobel Chemicals |
| Bank of Punjab |
Wah Nobel and Bank of Punjab Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Wah Nobel and Bank of Punjab
The main advantage of trading using opposite Wah Nobel and Bank of Punjab positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Wah Nobel 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.| Wah Nobel vs. Adamjee Insurance | Wah Nobel vs. Ghani Chemical Industries | Wah Nobel vs. Jubilee Life Insurance | Wah Nobel vs. Oil and Gas |
| Bank of Punjab vs. Habib Bank | Bank of Punjab vs. National Bank of | Bank of Punjab vs. Meezan Bank | Bank of Punjab vs. Bank Al Habib |
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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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