Correlation Between Agha Steel and TPL Insurance
Can any of the company-specific risk be diversified away by investing in both Agha Steel and TPL Insurance 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 Agha Steel and TPL Insurance into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Agha Steel Industries and TPL Insurance, you can compare the effects of market volatilities on Agha Steel and TPL Insurance 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 Agha Steel with a short position of TPL Insurance. Check out your portfolio center. Please also check ongoing floating volatility patterns of Agha Steel and TPL Insurance.
Diversification Opportunities for Agha Steel and TPL Insurance
0.39 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Agha and TPL is 0.39. Overlapping area represents the amount of risk that can be diversified away by holding Agha Steel Industries and TPL Insurance in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on TPL Insurance and Agha Steel 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 Agha Steel Industries are associated (or correlated) with TPL Insurance. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of TPL Insurance has no effect on the direction of Agha Steel i.e., Agha Steel and TPL Insurance go up and down completely randomly.
Pair Corralation between Agha Steel and TPL Insurance
Assuming the 90 days trading horizon Agha Steel Industries is expected to under-perform the TPL Insurance. But the stock apears to be less risky and, when comparing its historical volatility, Agha Steel Industries is 1.06 times less risky than TPL Insurance. The stock trades about -0.05 of its potential returns per unit of risk. The TPL Insurance is currently generating about -0.04 of returns per unit of risk over similar time horizon. If you would invest 1,540 in TPL Insurance on August 27, 2024 and sell it today you would lose (527.00) from holding TPL Insurance or give up 34.22% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 96.67% |
| Values | Daily Returns |
Agha Steel Industries vs. TPL Insurance
Performance |
| Timeline |
| Agha Steel Industries |
| TPL Insurance |
Agha Steel and TPL Insurance Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Agha Steel and TPL Insurance
The main advantage of trading using opposite Agha Steel and TPL Insurance positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Agha Steel position performs unexpectedly, TPL Insurance 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 TPL Insurance will offset losses from the drop in TPL Insurance's long position.| Agha Steel vs. Habib Insurance | Agha Steel vs. Ghandhara Automobile | Agha Steel vs. Century Insurance | Agha Steel vs. Reliance Weaving Mills |
| TPL Insurance vs. Habib Bank | TPL Insurance vs. National Bank of | TPL Insurance vs. United Bank | TPL Insurance vs. MCB Bank |
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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