Correlation Between KOT Addu and Sindh Modaraba
Can any of the company-specific risk be diversified away by investing in both KOT Addu and Sindh Modaraba 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 KOT Addu and Sindh Modaraba into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between KOT Addu Power and Sindh Modaraba Management, you can compare the effects of market volatilities on KOT Addu and Sindh Modaraba 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 KOT Addu with a short position of Sindh Modaraba. Check out your portfolio center. Please also check ongoing floating volatility patterns of KOT Addu and Sindh Modaraba.
Diversification Opportunities for KOT Addu and Sindh Modaraba
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between KOT and Sindh is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding KOT Addu Power and Sindh Modaraba Management in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sindh Modaraba Management and KOT Addu 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 KOT Addu Power are associated (or correlated) with Sindh Modaraba. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sindh Modaraba Management has no effect on the direction of KOT Addu i.e., KOT Addu and Sindh Modaraba go up and down completely randomly.
Pair Corralation between KOT Addu and Sindh Modaraba
Assuming the 90 days trading horizon KOT Addu Power is expected to generate 0.94 times more return on investment than Sindh Modaraba. However, KOT Addu Power is 1.06 times less risky than Sindh Modaraba. It trades about 0.21 of its potential returns per unit of risk. Sindh Modaraba Management is currently generating about 0.08 per unit of risk. If you would invest 2,759 in KOT Addu Power on September 16, 2024 and sell it today you would earn a total of 951.00 from holding KOT Addu Power or generate 34.47% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 96.92% |
Values | Daily Returns |
KOT Addu Power vs. Sindh Modaraba Management
Performance |
Timeline |
KOT Addu Power |
Sindh Modaraba Management |
KOT Addu and Sindh Modaraba Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with KOT Addu and Sindh Modaraba
The main advantage of trading using opposite KOT Addu and Sindh Modaraba positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if KOT Addu position performs unexpectedly, Sindh Modaraba 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 Sindh Modaraba will offset losses from the drop in Sindh Modaraba's long position.KOT Addu vs. Faysal Bank | KOT Addu vs. Adamjee Insurance | KOT Addu vs. Security Investment Bank | KOT Addu vs. Bank of Punjab |
Sindh Modaraba vs. Masood Textile Mills | Sindh Modaraba vs. Fauji Foods | Sindh Modaraba vs. KSB Pumps | Sindh Modaraba vs. Mari Petroleum |
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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