Correlation Between Matahari Department and XL Axiata
Can any of the company-specific risk be diversified away by investing in both Matahari Department and XL Axiata 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 Matahari Department and XL Axiata into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Matahari Department Store and XL Axiata Tbk, you can compare the effects of market volatilities on Matahari Department and XL Axiata 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 Matahari Department with a short position of XL Axiata. Check out your portfolio center. Please also check ongoing floating volatility patterns of Matahari Department and XL Axiata.
Diversification Opportunities for Matahari Department and XL Axiata
0.73 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Matahari and EXCL is 0.73. Overlapping area represents the amount of risk that can be diversified away by holding Matahari Department Store and XL Axiata Tbk in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on XL Axiata Tbk and Matahari Department 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 Matahari Department Store are associated (or correlated) with XL Axiata. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of XL Axiata Tbk has no effect on the direction of Matahari Department i.e., Matahari Department and XL Axiata go up and down completely randomly.
Pair Corralation between Matahari Department and XL Axiata
Assuming the 90 days trading horizon Matahari Department Store is expected to under-perform the XL Axiata. In addition to that, Matahari Department is 1.61 times more volatile than XL Axiata Tbk. It trades about -0.45 of its total potential returns per unit of risk. XL Axiata Tbk is currently generating about -0.17 per unit of volatility. If you would invest 226,000 in XL Axiata Tbk on August 24, 2024 and sell it today you would lose (8,000) from holding XL Axiata Tbk or give up 3.54% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Matahari Department Store vs. XL Axiata Tbk
Performance |
| Timeline |
| Matahari Department Store |
| XL Axiata Tbk |
Matahari Department and XL Axiata Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Matahari Department and XL Axiata
The main advantage of trading using opposite Matahari Department and XL Axiata positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Matahari Department position performs unexpectedly, XL Axiata 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 XL Axiata will offset losses from the drop in XL Axiata's long position.| Matahari Department vs. Surya Citra Media | Matahari Department vs. Akr Corporindo Tbk | Matahari Department vs. Media Nusantara Citra | Matahari Department vs. Pembangunan Perumahan PT |
| XL Axiata vs. Mnc Land Tbk | XL Axiata vs. MNC Vision Networks | XL Axiata vs. MD Pictures Tbk | XL Axiata vs. Link Net Tbk |
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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